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Journal articles on the topic 'Local motion planner'
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1
Karakaya, Suat, and Hasan Ocak. "A Novel Local Motion Planner: Navibug." Journal of Intelligent & Robotic Systems 100, no. 3-4 (August 17, 2020): 987–1003. http://dx.doi.org/10.1007/s10846-020-01239-4.
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Yoshida, Eiichi, Satoshi Murata, Akiya Kamimura, Kohji Tomita, Haruhisa Kurokawa, and Shigeru Kokaji. "Motion Generation for a Modular Robot." Journal of Robotics and Mechatronics 14, no. 2 (April 20, 2002): 177–85. http://dx.doi.org/10.20965/jrm.2002.p0177.
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We discuss motion generation of a homogeneous modular robot called a Modular Transformer (M-TRAN). Modules are designed to be self-reconfigurable so a collection of modules can transform itself into a robotic structure. The motion generation of the self-reconfigurable robot presents a computationally difficult problem due to the many combinatorial possibilities for the module configuration, even though the module itself is simple, with 2 degrees of freedom. We describe a motion generation for a class of multimodule structures based on a motion planner and a motion scheduler. The motion planner has 2 layers, with a global planner to plan overall movement of the cluster and a local planner to determine locally coordinated module motions, called motion schemes. After motion is generated as a sequence of single motion schemes, the motion scheduler processes the output plan to allow parallel motions to improve efficiency. The effectiveness of the motion generator is verified through a multiple-module simulation.
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Hoshino, Satoshi, and Kenichiro Uchida. "Interactive Motion Planning for Mobile Robot Navigation in Dynamic Environments." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 4 (July 20, 2017): 667–74. http://dx.doi.org/10.20965/jaciii.2017.p0667.
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In dynamic environments, taking static and moving obstacles into consideration in motion planning for mobile robot navigation is a technical issue. In this paper, we use a single mobile robot, for which humans are moving obstacles. Since moving humans sometimes get in the way of the robot, it must avoid collisions with them. Furthermore, if a part of the environment is crowded with humans, it is better for the robot to detour around the congested area. For this navigational challenge, we focus on the interaction between humans and the robot, so this paper proposes a motion planner for successfully getting through the human-robot interaction. The interactive motion planner is based on the hybrid use of global and local path planners. Furthermore, the local path planner is executed repetitively during the navigation. Through the human-robot interaction, the robot is enabled not only to avoid the collisions with humans but also to detour around congested areas. The emergence of this movement is the main contribution of this paper. We discuss the simulation results in terms of the effectiveness of the proposed motion planner for robot navigation in dynamic environments that include humans.
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Garrido, S., L. Moreno, D. Blanco, and M. L. Munoz. "Sensor-based global planning for mobile robot navigation." Robotica 25, no. 2 (March 2007): 189–99. http://dx.doi.org/10.1017/s0263574707003384.
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SUMMARYThe proposed algorithm integrates in a single planner the global motion planning and local obstacle avoidance capabilities. It efficiently guides the robot in a dynamic environment. This eliminates some of the traditional problems of planned architectures (model-plan-act scheme) while obtaining many of the qualities of behavior-based architectures. The computational efficiency of the method allows the planner to operate at high-rate sensor frequencies. This avoids the need for using both a collision-avoidance algorithm and a global motion planner for navigation in a cluttered environment. The method combines map-based and sensor-based planning operations to provide a smooth and reliable motion plan. Operating on a simple grid-based world model, the method uses a fast marching technique to determine a motion plan on a Voronoi extended transform extracted from the environment model. In addition to this real-time response ability, the method produces smooth and safe robot trajectories.
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Vass, Gábor, Béla Lantos, and Shahram Payandeh. "Object Reconfiguration with Dextrous Robot Agents." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 2 (March 20, 2006): 234–40. http://dx.doi.org/10.20965/jaciii.2006.p0234.
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This paper addresses an object manipulation planning algorithm for dextrous robot systems consisting a multifingered hand and a robotic manipulator. A method has been developed for object reconfiguration design. The result is a new algorithm using artificial intelligence based on simulated annealing and A* search. The upper level of the manipulation system, the global planner generates the motion of the object. The lower level, the local planner deals with the motion of the agents relative to the object and the design of the contact forces. The local planner is based on simulated annealing, thus the the local minima can be avoided in the energy function of the motion with high probability. Application of the algorithm has been discussed for three robot arms.
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Lin, Chien-Chou, Kun-Cheng Chen, and Wei-Ju Chuang. "Motion Planning Using a Memetic Evolution Algorithm for Swarm Robots." International Journal of Advanced Robotic Systems 9, no. 1 (January 1, 2012): 19. http://dx.doi.org/10.5772/45669.
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A hierarchical memetic algorithm (MA) is proposed for the path planning and formation control of swarm robots. The proposed algorithm consists of a global path planner (GPP) and a local motion planner (LMP). The GPP plans a trajectory within the Voronoi diagram (VD) of the free space. An MA with a non-random initial population plans a series of configurations along the path given by the former stage. The MA locally adjusts the robot positions to search for better fitness along the gradient direction of the distance between the swarm robots and the intermediate goals (IGs). Once the optimal configuration is obtained, the best chromosomes are reserved as the initial population for the next generation. Since the proposed MA has a non-random initial population and local searching, it is more efficient and the planned path is faster compared to a traditional genetic algorithm (GA). The simulation results show that the proposed algorithm works well in terms of path smoothness and computation efficiency.
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Hong, Sun-Gi, and Ju-Jang Lee. "A local motion planner for car-like robots in a cluttered environment." Artificial Life and Robotics 1, no. 1 (March 1997): 39–42. http://dx.doi.org/10.1007/bf02471111.
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McConachie, Dale, Andrew Dobson, Mengyao Ruan, and Dmitry Berenson. "Manipulating deformable objects by interleaving prediction, planning, and control." International Journal of Robotics Research 39, no. 8 (June 19, 2020): 957–82. http://dx.doi.org/10.1177/0278364920918299.
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We present a framework for deformable object manipulation that interleaves planning and control, enabling complex manipulation tasks without relying on high-fidelity modeling or simulation. The key question we address is when should we use planning and when should we use control to achieve the task? Planners are designed to find paths through complex configuration spaces, but for highly underactuated systems, such as deformable objects, achieving a specific configuration is very difficult even with high-fidelity models. Conversely, controllers can be designed to achieve specific configurations, but they can be trapped in undesirable local minima owing to obstacles. Our approach consists of three components: (1) a global motion planner to generate gross motion of the deformable object; (2) a local controller for refinement of the configuration of the deformable object; and (3) a novel deadlock prediction algorithm to determine when to use planning versus control. By separating planning from control we are able to use different representations of the deformable object, reducing overall complexity and enabling efficient computation of motion. We provide a detailed proof of probabilistic completeness for our planner, which is valid despite the fact that our system is underactuated and we do not have a steering function. We then demonstrate that our framework is able to successfully perform several manipulation tasks with rope and cloth in simulation, which cannot be performed using either our controller or planner alone. These experiments suggest that our planner can generate paths efficiently, taking under a second on average to find a feasible path in three out of four scenarios. We also show that our framework is effective on a 16-degree-of-freedom physical robot, where reachability and dual-arm constraints make the planning more difficult.
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Tian, Yuan, and Feng Gao. "Efficient motion generation for a six-legged robot walking on irregular terrain via integrated foothold selection and optimization-based whole-body planning." Robotica 36, no. 3 (November 6, 2017): 333–52. http://dx.doi.org/10.1017/s0263574717000418.
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SUMMARYIn this paper, an efficient motion planning method is proposed for a six-legged robot walking on irregular terrain. The method provides the robot with fast-generated free-gait motions to traverse the terrain with medium irregularities. We first of all introduce our six-legged robot with legs in parallel mechanism. After that, we decompose the motion planning problem into two main steps: first is the foothold selection based on a local footstep cost map, in which both terrain features and the robot mobility are considered; second is a whole-body configuration planner which casts the problem into a general convex optimization problem. Such decomposition reduces the complexity of the motion planning problem. Along with the two-step planner, discussions are also given in terms of the robot-environmental relationship, convexity of constraints and robot rotation integration. Both simulations and experiments are carried out on typical irregular terrains. The results demonstrate effectiveness of the planning method.
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Looi, Chen Zheng, and Danny Wee Kiat Ng. "A Study on the Effect of Parameters for ROS Motion Planer and Navigation System for Indoor Robot." International Journal of Electrical and Computer Engineering Research 1, no. 1 (June 15, 2021): 29–36. http://dx.doi.org/10.53375/ijecer.2021.21.
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In the past decades, the service robot industry had risen rapidly. The office assistant robot is one type of service robot used to assist officers in an office environment. For the robot to navigate autonomously in the office, navigation algorithms and motion planners were implemented on these robots. Robot Operating System (ROS) is one of the common platforms to develop these robots. The parameters applied to the motion planners will affect the performance of the Robot. In this study, the global planners, A* and Dijkstra algorithm and local planners, Dynamic Window Approach (DWA) and Time Elastic Band (TEB) algorithms were implemented and tested on a robot in simulation and a real environment. Results from the experiments were used to evaluate and compare the performance of the robot with different planners and parameters. Based on the results obtained, the global planners, A* and Dijkstra algorithm both can achieve the required performance for this application whereas TEB outperforms DWA as the local planner due to its feasibility in avoiding dynamic obstacles in the experiments conducted.
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Xu, Wang-bao, Jie Zhao, Xue-bo Chen, and Ying Zhang. "Artificial moment method using attractive points for the local path planning of a single robot in complicated dynamic environments." Robotica 31, no. 8 (June 7, 2013): 1263–74. http://dx.doi.org/10.1017/s0263574713000477.
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SUMMARYA novel path planner is presented for the local path planning of a single robot (represented with R) in a complicated dynamic environment. Here a series of attractive points are computed based on attractive segments for guiding R to move along a shorter path. Each attractive segment is obtained by using the full environmental knowledge and will be used for several sampling times in general. A motion controller, which is designed based on artificial moments and a robot model that has a principal motion direction line(PMDline), makes R move closely to attractive points while away from obstacles. Attractive and repulsive moments are designed, which only make R's PMDline face toward attractive points and opposite to obstacles in general, as in most cases, R will move along its PMDline with its full speed. Because of the guidance of attractive points and R's full-speed motion, the global convergence is guaranteed. Simulations indicate that the proposed path planner meets the requirements of real-time property while can optimize R's traveling path.
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Zeng, Junjie, Long Qin, Yue Hu, Quanjun Yin, and Cong Hu. "Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments." Applied Sciences 9, no. 7 (April 2, 2019): 1384. http://dx.doi.org/10.3390/app9071384.
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Since an individual approach can hardly navigate robots through complex environments, we present a novel two-level hierarchical framework called JPS-IA3C (Jump Point Search improved Asynchronous Advantage Actor-Critic) in this paper for robot navigation in dynamic environments through continuous controlling signals. Its global planner JPS+ (P) is a variant of JPS (Jump Point Search), which efficiently computes an abstract path of neighboring jump points. These nodes, which are seen as subgoals, completely rid Deep Reinforcement Learning (DRL)-based controllers of notorious local minima. To satisfy the kinetic constraints and be adaptive to changing environments, we propose an improved A3C (IA3C) algorithm to learn the control policies of the robots’ local motion. Moreover, the combination of modified curriculum learning and reward shaping helps IA3C build a novel reward function framework to avoid learning inefficiency because of sparse reward. We additionally strengthen the robots’ temporal reasoning of the environments by a memory-based network. These improvements make the IA3C controller converge faster and become more adaptive to incomplete, noisy information caused by partial observability. Simulated experiments show that compared with existing methods, this JPS-IA3C hierarchy successfully outputs continuous commands to accomplish large-range navigation tasks at shorter paths and less time through reasonable subgoal selection and rational motions.
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Younes, Younes Al, and Martin Barczyk. "Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon." Sensors 21, no. 16 (August 18, 2021): 5547. http://dx.doi.org/10.3390/s21165547.
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Navigating robotic systems autonomously through unknown, dynamic and GPS-denied environments is a challenging task. One requirement of this is a path planner which provides safe trajectories in real-world conditions such as nonlinear vehicle dynamics, real-time computation requirements, complex 3D environments, and moving obstacles. This paper presents a methodological motion planning approach which integrates a novel local path planning approach with a graph-based planner to enable an autonomous vehicle (here a drone) to navigate through GPS-denied subterranean environments. The local path planning approach is based on a recently proposed method by the authors called Nonlinear Model Predictive Horizon (NMPH). The NMPH formulation employs a copy of the plant dynamics model (here a nonlinear system model of the drone) plus a feedback linearization control law to generate feasible, optimal, smooth and collision-free paths while respecting the dynamics of the vehicle, supporting dynamic obstacles and operating in real time. This design is augmented with computationally efficient algorithms for global path planning and dynamic obstacle mapping and avoidance. The overall design is tested in several simulations and a preliminary real flight test in unexplored GPS-denied environments to demonstrate its capabilities and evaluate its performance.
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Shvalb, Nir, Boaz Ben Moshe, and Oded Medina. "A real-time motion planning algorithm for a hyper-redundant set of mechanisms." Robotica 31, no. 8 (June 11, 2013): 1327–35. http://dx.doi.org/10.1017/s0263574713000489.
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SUMMARYWe introduce a novel probabilistic algorithm (CPRM) for real-time motion planning in the configuration space${\EuScript C}$. Our algorithm differs from a probabilistic road map (PRM) algorithm in the motion between a pair of anchoring points (local planner) which takes place on the boundary of the obstacle subspace${\EuScript O}$. We define a varying potential fieldfon ∂${\EuScript O}$as a Morse function and follow$\vec{\nabla} f$. We then exemplify our algorithm on a redundant worm climbing robot withndegrees of freedom and compare our algorithm running results with those of the PRM.
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Quer, Stefano, and Luz Garcia. "Modules and Techniques for Motion Planning: An Industrial Perspective." Sensors 21, no. 2 (January 9, 2021): 420. http://dx.doi.org/10.3390/s21020420.
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Research on autonomous cars has become one of the main research paths in the automotive industry, with many critical issues that remain to be explored while considering the overall methodology and its practical applicability. In this paper, we present an industrial experience in which we build a complete autonomous driving system, from the sensor units to the car control equipment, and we describe its adoption and testing phase on the field. We report how we organize data fusion and map manipulation to represent the required reality. We focus on the communication and synchronization issues between the data-fusion device and the path-planner, between the CPU and the GPU units, and among different CUDA kernels implementing the core local planner module. In these frameworks, we propose simple representation strategies and approximation techniques which guarantee almost no penalty in terms of accuracy and large savings in terms of memory occupation and memory transfer times. We show how we adopt a recent implementation on parallel many-core devices, such as CUDA-based GPGPU, to reduce the computational burden of rapidly exploring random trees to explore the state space along with a given reference path. We report on our use of the controller and the vehicle simulator. We run experiments on several real scenarios, and we report the paths generated with the different settings, with their relative errors and computation times. We prove that our approach can generate reasonable paths on a multitude of standard maneuvers in real time.
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Quer, Stefano, and Luz Garcia. "Modules and Techniques for Motion Planning: An Industrial Perspective." Sensors 21, no. 2 (January 9, 2021): 420. http://dx.doi.org/10.3390/s21020420.
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Research on autonomous cars has become one of the main research paths in the automotive industry, with many critical issues that remain to be explored while considering the overall methodology and its practical applicability. In this paper, we present an industrial experience in which we build a complete autonomous driving system, from the sensor units to the car control equipment, and we describe its adoption and testing phase on the field. We report how we organize data fusion and map manipulation to represent the required reality. We focus on the communication and synchronization issues between the data-fusion device and the path-planner, between the CPU and the GPU units, and among different CUDA kernels implementing the core local planner module. In these frameworks, we propose simple representation strategies and approximation techniques which guarantee almost no penalty in terms of accuracy and large savings in terms of memory occupation and memory transfer times. We show how we adopt a recent implementation on parallel many-core devices, such as CUDA-based GPGPU, to reduce the computational burden of rapidly exploring random trees to explore the state space along with a given reference path. We report on our use of the controller and the vehicle simulator. We run experiments on several real scenarios, and we report the paths generated with the different settings, with their relative errors and computation times. We prove that our approach can generate reasonable paths on a multitude of standard maneuvers in real time.
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Shigemura, Atsushi, Yuki Ishikawa, Jun Miura, and Junji Satake. "An RT Component for Simulating People Movement in Public Space and its Application to Robot Motion Planner Development." Journal of Robotics and Mechatronics 24, no. 1 (February 20, 2012): 165–73. http://dx.doi.org/10.20965/jrm.2012.p0165.
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This paper describes a software module for simulating “people movement” in public space such as shopping centers and cafeterias. We decompose people movement into global and local, and make a model of each of them. Global movement corresponds to following a route from a current position to a destination. In local movement, a person moves toward the next subgoal while avoiding surrounding persons and obstacles. We also model behavior specific to a cafeteria, such as queuing and searching for unoccupied seats. We implement these simulation algorithms in a simulator RT component, that can be used easily for development of robot motion planners, which are also realized as RT components. Various simulation experiments show the effectiveness of the simulation algorithms and the simulator RT component.
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Park, Chonhyon, Jia Pan, and Dinesh Manocha. "High-DOF Robots in Dynamic Environments Using Incremental Trajectory Optimization." International Journal of Humanoid Robotics 11, no. 02 (June 2014): 1441001. http://dx.doi.org/10.1142/s0219843614410011.
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We present a novel optimization-based motion planning algorithm for high degree-of-freedom (DOF) robots in dynamic environments. Our approach decomposes the high-dimensional motion planning problem into a sequence of low-dimensional sub-problems. We compute collision-free and smooth paths using optimization-based planning and trajectory perturbation for each sub-problem. The overall algorithm does not require a priori knowledge about global motion or trajectories of dynamic obstacles. Rather, we compute a conservative local bound on the position or trajectory of each obstacle over a short time and use the bound to incrementally compute a collision-free trajectory for the robot. The high-DOF robot is treated as a tightly coupled system, and we incrementally use constrained coordination to plan its motion. We highlight the performance of our planner in simulated environments on robots with tens of DOFs.
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Aghi, Diego, Vittorio Mazzia, and Marcello Chiaberge. "Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy." Machines 8, no. 2 (May 25, 2020): 27. http://dx.doi.org/10.3390/machines8020027.
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With the advent of agriculture 3.0 and 4.0, in view of efficient and sustainable use of resources, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. Autonomous agricultural field machines have been gaining significant attention from farmers and industries to reduce costs, human workload, and required resources. Nevertheless, achieving sufficient autonomous navigation capabilities requires the simultaneous cooperation of different processes; localization, mapping, and path planning are just some of the steps that aim at providing to the machine the right set of skills to operate in semi-structured and unstructured environments. In this context, this study presents a low-cost, power-efficient local motion planner for autonomous navigation in vineyards based only on an RGB-D camera, low range hardware, and a dual layer control algorithm. The first algorithm makes use of the disparity map and its depth representation to generate a proportional control for the robotic platform. Concurrently, a second back-up algorithm, based on representations learning and resilient to illumination variations, can take control of the machine in case of a momentaneous failure of the first block generating high-level motion primitives. Moreover, due to the double nature of the system, after initial training of the deep learning model with an initial dataset, the strict synergy between the two algorithms opens the possibility of exploiting new automatically labeled data, coming from the field, to extend the existing model’s knowledge. The machine learning algorithm has been trained and tested, using transfer learning, with acquired images during different field surveys in the North region of Italy and then optimized for on-device inference with model pruning and quantization. Finally, the overall system has been validated with a customized robot platform in the appropriate environment.
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Sun, Jiubo, Guoliang Liu, Guohui Tian, and Jianhua Zhang. "Smart Obstacle Avoidance Using a Danger Index for a Dynamic Environment." Applied Sciences 9, no. 8 (April 17, 2019): 1589. http://dx.doi.org/10.3390/app9081589.
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The artificial potential field approach provides a simple and effective motion planner for robot navigation. However, the traditional artificial potential field approach in practice can have a local minimum problem, i.e., the attractive force from the target position is in the balance with the repulsive force from the obstacle, such that the robot cannot escape from this situation and reach the target. Moreover, the moving object detection and avoidance is still a challenging problem with the current artificial potential field method. In this paper, we present an improved version of the artificial potential field method, which uses a dynamic window approach to solve the local minimum problem and define a danger index in the speed field for moving object avoidance. The new danger index considers not only the relative distance between the robot and the obstacle, but also the relative velocity according to the motion of the moving objects. In this way, the robot can find an optimized path to avoid local minimum and moving obstacles, which is proved by our experimental results.
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Ahmed, Alaa, Turki Abdalla, and Ali Abed. "Path Planning of Mobile Robot Using Fuzzy-Potential Field Method." Iraqi Journal for Electrical and Electronic Engineering 11, no. 1 (June 1, 2015): 32–41. http://dx.doi.org/10.37917/ijeee.11.1.4.
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This paper deals with the navigation of a mobile robot in unknown environment using artificial potential field method. The aim of this paper is to develop a complete method that allows the mobile robot to reach its goal while avoiding unknown obstacles on its path. An approach proposed is introduced in this paper based on combing the artificial potential field method with fuzzy logic controller to solve drawbacks of artificial potential field method such as local minima problems, make an effective motion planner and improve the quality of the trajectory of mobile robot.
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Fu, Yue-wen, Meng Li, Jia-hong Liang, and Xiao-qian Hu. "A Grid-Based Motion Planning Approach for Coherent Groups." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/416913.
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This paper presents a novel motion planning approach for coherent groups with constant area, and it integrates C-L method into the probabilistic roadmap algorithm with sampling on the medial axis (MAPRM). In the preprocessing phase, the group is discretized into a grid-set which represents the configuration of the group. Then, a number of samples are generated on workspace by medial axis technique. These samples are extended into group’s configuration nodes of the roadmap using an extending strategy. Also, the group's deformation degree relative to the desired shape is introduced to improve the evaluation function. It gives users more flexibility to determine the respective weights of the group’s deformation degree and its distance to the goal in the query phase. After that, a novel local planner is constructed to connect any two neighbor configurations by using C-L method and the improved evaluation function. Experiments show that our approach is able to find paths for the coherent group efficiently and keep its area invariant when moving toward the goal.
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McMahon, Troy, Shawna Thomas, and Nancy M. Amato. "Sampling-based motion planning with reachable volumes for high-degree-of-freedom manipulators." International Journal of Robotics Research 37, no. 7 (June 2018): 779–817. http://dx.doi.org/10.1177/0278364918779555.
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Motion planning for constrained systems is a version of the motion planning problem in which the motion of a robot is limited by constraints. For example, one can require that a humanoid robot such as a PR2 remain upright by constraining its torso to be above its base or require that an object such as a bucket of water remain upright by constraining the vertices of the object to be parallel to the robot’s base. Grasping can be modeled by requiring that the end effectors of the robot be located at specified handle positions. Constraints might require that the robot remain in contact with a surface, or that certain joints of the robot remain in contact with each other (e.g., closed chains). Such problems are particularly difficult because the constraints form a manifold in C-space, and planning must be restricted to this manifold. High-degree-of-freedom motion planning and motion planning for constrained systems has applications in parallel robotics, grasping and manipulation, computational biology and molecular simulations, and animation. We introduce a new concept, reachable volumes, that are a geometric representation of the regions the joints and end effectors of a robot can reach, and use it to define a new planning space called RV-space where all points automatically satisfy a problem’s constraints. Visualizations of reachable volumes can enable operators to see the regions of workspace that different parts of the robot can reach. Samples and paths generated in RV-space naturally conform to constraints, making planning for constrained systems no more difficult than planning for unconstrained systems. Consequently, constrained motion planning problems that were previously difficult or unsolvable become manageable and in many cases trivial. We introduce tools and techniques to extend the state-of-the-art sampling-based motion planning algorithms to RV-space. We define a reachable volumes sampler, a reachable volumes local planner, and a reachable volumes distance metric. We showcase the effectiveness of RV-space by applying these tools to motion planning problems for robots with constraints on the end effectors and/or internal joints of the robot. We show that RV-based planners are more efficient than existing methods, particularly for higher-dimensional problems, solving problems with 1000 or more degrees of freedom for multi-loop and tree-like linkages.
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Arslan, Omur, and Daniel E. Koditschek. "Sensor-based reactive navigation in unknown convex sphere worlds." International Journal of Robotics Research 38, no. 2-3 (September 5, 2018): 196–223. http://dx.doi.org/10.1177/0278364918796267.
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We construct a sensor-based feedback law that provably solves the real-time collision-free robot navigation problem in a compact convex Euclidean subset cluttered with unknown but sufficiently separated and strongly convex obstacles. Our algorithm introduces a novel use of separating hyperplanes for identifying the robot’s local obstacle-free convex neighborhood, affording a reactive (online-computed) continuous and piecewise smooth closed-loop vector field whose smooth flow brings almost all configurations in the robot’s free space to a designated goal location, with the guarantee of no collisions along the way. Specialized attention to planar navigable environments yields a necessary and sufficient condition on convex obstacles for almost-global navigation towards any goal location in the environment. We further extend these provable properties of the planar setting to practically motivated limited range, isotropic and anisotropic sensing models, and the non-holonomically constrained kinematics of the standard differential-drive vehicle. We conclude with numerical and experimental evidence demonstrating the effectiveness of the proposed sensory feedback motion planner.
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Guo, Fei, Shoukun Wang, Junzheng Wang, and Huan Yu. "Kinematics-searched framework for quadruped traversal in a parallel robot." Industrial Robot: the international journal of robotics research and application 47, no. 2 (December 2, 2019): 267–79. http://dx.doi.org/10.1108/ir-05-2019-0098.
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Purpose In this research, the authors established a hierarchical motion planner for quadruped locomotion, which enables a parallel wheel-quadruped robot, the “BIT-NAZA” robot, to traverse rough three-dimensional (3-D) terrain. Design/methodology/approach Presented is a novel wheel-quadruped mobile robot with parallel driving mechanisms and based on the Stewart six degrees of freedom (6-DOF) platform. The task for traversing rough terrain is decomposed into two prospects: one is the configuration selection in terms of a local foothold cost map, in which the kinematic feasibility of parallel mechanism and terrain features are satisfied in heuristic search planning, and the other one is a whole-body controller to complete smooth and continuous motion transitions. Findings A fan-shaped foot search region focuses on footholds with a strong possibility of becoming foot placement, simplifying computation complexity. A receding horizon avoids kinematic deadlock during the search process and improves robot adaptation. Research limitations/implications Both simulation and experimental results validated the proposed scenario available and appropriate for quadruped locomotion to traverse challenging 3-D terrains. Originality/value This paper analyzes kinematic workspace for a parallel robot with 6-DOF Stewart mechanism on both body and foot. A fan-shaped foot search region enhances computation efficiency. Receding horizon broadens the preview search to decrease the possibility of deadlock minima resulting from terrain variation.
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Delgado, Raimarius, and Byoung Choi. "Network-Oriented Real-Time Embedded System Considering Synchronous Joint Space Motion for an Omnidirectional Mobile Robot." Electronics 8, no. 3 (March 13, 2019): 317. http://dx.doi.org/10.3390/electronics8030317.
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This paper proposes a real-time embedded system for joint space control of omnidirectional mobile robots. Actuators driving an omnidirectional mobile robot are connected in a line topology which requires synchronization to move simultaneously in translation and rotation. We employ EtherCAT, a real-time Ethernet network, to control servo controllers for the mobile robot. The first part of this study focuses on the design of a low-cost embedded system utilizing an open-source EtherCAT master. Although satisfying real-time constraints is critical, a desired trajectory on the center of the mobile robot should be decomposed into the joint space to drive the servo controllers. For the center of the robot, a convolution-based path planner and a corresponding joint space control algorithm are presented considering its physical limits. To avoid obstacles that introduce geometric constraints on the curved path, a trajectory generation algorithm considering high curvature turning points is adapted for an omnidirectional mobile robot. Tracking a high curvature path increases mathematical complexity, which requires precise synchronization between the actuators of the mobile robot. An improvement of the distributed clock—the synchronization mechanism of EtherCAT for slaves—is presented and applied to the joint controllers of the mobile robot. The local time of the EtherCAT master is dynamically adjusted according to the drift of the reference slave, which minimizes the synchronization error between each joint. Experiments are conducted on our own developed four-wheeled omnidirectional mobile robot. The experiment results confirm that the proposed system is very effective in real-time control applications for precise motion control of the robot even for tracking high curvature paths.
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Alonso-Mora, Javier, Stuart Baker, and Daniela Rus. "Multi-robot formation control and object transport in dynamic environments via constrained optimization." International Journal of Robotics Research 36, no. 9 (August 2017): 1000–1021. http://dx.doi.org/10.1177/0278364917719333.
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We present a constrained optimization method for multi-robot formation control in dynamic environments, where the robots adjust the parameters of the formation, such as size and three-dimensional orientation, to avoid collisions with static and moving obstacles, and to make progress towards their goal. We describe two variants of the algorithm, one for local motion planning and one for global path planning. The local planner first computes a large obstacle-free convex region in a neighborhood of the robots, embedded in position-time space. Then, the parameters of the formation are optimized therein by solving a constrained optimization, via sequential convex programming. The robots navigate towards the optimized formation with individual controllers that account for their dynamics. The idea is extended to global path planning by sampling convex regions in free position space and connecting them if a transition in formation is possible - computed via the constrained optimization. The path of lowest cost to the goal is then found via graph search. The method applies to ground and aerial vehicles navigating in two- and three-dimensional environments among static and dynamic obstacles, allows for reconfiguration, and is efficient and scalable with the number of robots. In particular, we consider two applications, a team of aerial vehicles navigating in formation, and a small team of mobile manipulators that collaboratively carry an object. The approach is verified in experiments with a team of three mobile manipulators and in simulations with a team of up to sixteen Micro Air Vehicles (quadrotors).
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Mustafa, K. A. A., N. Botteghi, B. Sirmacek, M. Poel, and S. Stramigioli. "TOWARDS CONTINUOUS CONTROL FOR MOBILE ROBOT NAVIGATION: A REINFORCEMENT LEARNING AND SLAM BASED APPROACH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 857–63. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-857-2019.
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<p><strong>Abstract.</strong> We introduce a new autonomous path planning algorithm for mobile robots for reaching target locations in an unknown environment where the robot relies on its on-board sensors. In particular, we describe the design and evaluation of a deep reinforcement learning motion planner with continuous linear and angular velocities to navigate to a desired target location based on deep deterministic policy gradient (DDPG). Additionally, the algorithm is enhanced by making use of the available knowledge of the environment provided by a grid-based SLAM with Rao-Blackwellized particle filter algorithm in order to shape the reward function in an attempt to improve the convergence rate, escape local optima and reduce the number of collisions with the obstacles. A comparison is made between a reward function shaped based on the map provided by the SLAM algorithm and a reward function when no knowledge of the map is available. Results show that the required learning time has been decreased in terms of number of episodes required to converge, which is 560 episodes compared to 1450 episodes in the standard RL algorithm, after adopting the proposed approach and the number of obstacle collision is reduced as well with a success ratio of 83% compared to 56% in the standard RL algorithm. The results are validated in a simulated experiment on a skid-steering mobile robot.</p>
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Bardin, B. S. "On a Method of Introducing Local Coordinates in the Problem of the Orbital Stability of Planar Periodic Motions of a Rigid Body." Nelineinaya Dinamika 16, no. 4 (2020): 581–94. http://dx.doi.org/10.20537/nd200404.
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A method is presented of constructing a nonlinear canonical change of variables which makes it possible to introduce local coordinates in a neighborhood of periodic motions of an autonomous Hamiltonian system with two degrees of freedom. The problem of the orbital stability of pendulum-like oscillations of a heavy rigid body with a fixed point in the Bobylev – Steklov case is discussed as an application. The nonlinear analysis of orbital stability is carried out including terms through degree six in the expansion of the Hamiltonian function in a neighborhood of the unperturbed periodic motion. This makes it possible to draw rigorous conclusions on orbital stability for the parameter values corresponding to degeneracy of terms of degree four in the normal form of the Hamiltonian function of equations of perturbed motion.
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Montani, Margherita, Leandro Ronchi, Renzo Capitani, and Claudio Annicchiarico. "A Hierarchical Autonomous Driver for a Racing Car: Real-Time Planning and Tracking of the Trajectory." Energies 14, no. 19 (September 22, 2021): 6008. http://dx.doi.org/10.3390/en14196008.
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The aim of this study was to develop trajectory planning that would allow an autonomous racing car to be driven as close as possible to what a driver would do, defining the most appropriate inputs for the current scenario. The search for the optimal trajectory in terms of lap time reduction involves the modeling of all the non-linearities of the vehicle dynamics with the disadvantage of being a time-consuming problem and not being able to be implemented in real-time. However, to improve the vehicle performances, the trajectory needs to be optimized online with the knowledge of the actual vehicle dynamics and path conditions. Therefore, this study involved the development of an architecture that allows an autonomous racing car to have an optimal online trajectory planning and path tracking ensuring professional driver performances. The real-time trajectory optimization can also ensure a possible future implementation in the urban area where obstacles and dynamic scenarios could be faced. It was chosen to implement a local trajectory planning based on the Model Predictive Control(MPC) logic and solved as Linear Programming (LP) by Sequential Convex Programming (SCP). The idea was to achieve a computational cost, 0.1 s, using a point mass vehicle model constrained by experimental definition and approximation of the car’s GG-V, and developing an optimum model-based path tracking to define the driver model that allows A car to follow the trajectory defined by the planner ensuring a signal input every 0.001 s. To validate the algorithm, two types of tests were carried out: a Matlab-Simulink, Vi-Grade co-simulation test, comparing the proposed algorithm with the performance of an offline motion planning, and a real-time simulator test, comparing the proposed algorithm with the performance of a professional driver. The results obtained showed that the computational cost of the optimization algorithm developed is below the limit of 0.1 s, and the architecture showed a reduction of the lap time of about 1 s compared to the offline optimizer and reproducibility of the performance obtained by the driver.
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Pereira, Guilherme A. S., and Elias J. R. Freitas. "Navigation of Semi-autonomous Service Robots Using Local Information and Anytime Motion Planners." Robotica 38, no. 11 (January 14, 2020): 2080–98. http://dx.doi.org/10.1017/s0263574719001838.
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SUMMARYThis paper deals with the problem of navigating semi-autonomous mobile robots without global localization systems in unknown environments. We propose a planning-based obstacle avoidance strategy that relies on local maps and a series of short-time coordinate frames. With this approach, simple odometry and range information are sufficient to make the robot to safely follow the user commands. Different from reactive obstacle avoidance strategies, the proposed approach chooses a good and smooth local path for the robot. The methodology is evaluated using a mobile service robot moving in an unknown corridor environment populated with obstacles and people.
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Tao, Songqiao, and Yumeng Yang. "Collision-free motion planning of a virtual arm based on the FABRIK algorithm." Robotica 35, no. 6 (April 18, 2016): 1431–50. http://dx.doi.org/10.1017/s0263574716000205.
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SUMMARYCollision-free motion planning of a virtual arm is an intractable task in high-interference environments. In this paper, an approach for collision-free motion planning of a virtual arm based on the forward and backward reaching inverse kinematics (FABRIK) algorithm is proposed. First, a random rotation strategy and local optimum-seeking technology are introduced to improve the FABRIK algorithm in order to avoid obstacles. The improvement FABRIK algorithm is used to design the final grasping posture of a virtual arm according the target position. Then, a bidirectional rapidly exploring random tree (Bi-RRT) algorithm is adopted to explore the process postures from a given initial posture to the final grasping posture. Different from the existing method, the proposed Bi-RRT algorithm in this paper plans the motions of a virtual arm in a seven-dimensional angle space, and the final grasping posture is automatically designed using the obstacle-avoidance FABRIK algorithm instead of the manual design. Finally, the post-processing technique is introduced to remove redundant nodes from the planned motions. This procedure has resolved the problem that the Bi-RRT algorithm is a random algorithm. The experimental results show the proposed method for collision-free motion planning of a virtual arm is feasible.
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Lasovsky, Yoav, and Leo Joskowicz. "Motion planning in crowded planar environments." Robotica 17, no. 4 (July 1999): 365–71. http://dx.doi.org/10.1017/s0263574799001629.
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We present a new algorithm for fine motion planning in geometrically complex situations. Geometrically complex situations have complex robot and environment geometry, crowded environments, narrow passages and tight fits. They require complex robot motions with coupled degrees of freedom. The algorithm constructs a path by incrementally building a graph of linearized convex configuration space cells and solving a series of linear optimization problems with varying objective functions. Its advantages are that it better exploits the local geometry of narrow passages in configuration space, and that its complexity does not significantly increase as the clearance of narrow passages decreases. We demonstrate the algorithm on examples which other planners could not solve.
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Kingston, Zachary, Mark Moll, and Lydia E. Kavraki. "Exploring implicit spaces for constrained sampling-based planning." International Journal of Robotics Research 38, no. 10-11 (August 26, 2019): 1151–78. http://dx.doi.org/10.1177/0278364919868530.
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We present a review and reformulation of manifold constrained sampling-based motion planning within a unifying framework, IMACS (implicit manifold configuration space). IMACS enables a broad class of motion planners to plan in the presence of manifold constraints, decoupling the choice of motion planning algorithm and method for constraint adherence into orthogonal choices. We show that implicit configuration spaces defined by constraints can be presented to sampling-based planners by addressing two key fundamental primitives, sampling and local planning, and that IMACS preserves theoretical properties of probabilistic completeness and asymptotic optimality through these primitives. Within IMACS, we implement projection- and continuation-based methods for constraint adherence, and demonstrate the framework on a range of planners with both methods in simulated and realistic scenarios. Our results show that the choice of method for constraint adherence depends on many factors and that novel combinations of planners and methods of constraint adherence can be more effective than previous approaches. Our implementation of IMACS is open source within the Open Motion Planning Library and is easily extended for novel planners and constraint spaces.
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Gao, Xiangyu, Shuyou Zhang, Lemiao Qiu, Xiaojian Liu, Zili Wang, and Yang Wang. "Double B-Spline Curve-Fitting and Synchronization-Integrated Feedrate Scheduling Method for Five-Axis Linear-Segment Toolpath." Applied Sciences 10, no. 9 (May 1, 2020): 3158. http://dx.doi.org/10.3390/app10093158.
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The discontinuities of a five-axis linear-segment toolpath result in fluctuation in the feedrate, acceleration and jerk commands that lead to machine tool vibration and poor surface finish. For path smoothing, with the global curve-fitting method it is difficult to control fitting error and the local corner-smoothing method has large curvature extreme. For path synchronization, the parameter synchronization method cannot ensure smooth rotary motion. Aiming at these problems, this paper proposes a double B-spline curve-fitting and synchronization-integrated feedrate scheduling method. Two C2-continuous and error-bounded B-spline curves are produced to fit tool-tip position and tool orientation, respectively. The fitting error is controlled by locally refining the curve segments that exceed the fitting tolerance. The tool-tip position trajectory is firstly planned to address axial kinematic constraints in the feedrate scheduling process. Then the feedrate is deformed for the tool orientation to guarantee smooth rotary motion as well as to share the same motion time with the tool-tip position segment by segment. The feasibility and effectiveness of the proposed method have been validated by simulations and experiments on the S-shape test piece. Simulations show that the proposed curve-fitting method can generate smooth toolpath and constrain fitting error. The proposed feedrate scheduling method can guarantee smooth rotary motion and keep axial motions under kinematic limits, compared with the method that does not consider axial kinematic constraints and the parameter synchronization method. Experimental results verify that the proposed curve-fitting method can generate smooth tool path under fitting tolerance, and the proposed feedrate scheduling method can produce smooth and restricted axial motions.
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Lee, Kyuman, Daegyun Choi, and Donghoon Kim. "Incorporation of Potential Fields and Motion Primitives for the Collision Avoidance of Unmanned Aircraft." Applied Sciences 11, no. 7 (March 31, 2021): 3103. http://dx.doi.org/10.3390/app11073103.
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Collision avoidance (CA) using the artificial potential field (APF) usually faces several known issues such as local minima and dynamically infeasible problems, so unmanned aerial vehicles’ (UAVs) paths planned based on the APF are safe only in a certain environment. This research proposes a CA approach that combines the APF and motion primitives (MPs) to tackle the known problems associated with the APF. Since MPs solve for a locally optimal trajectory with respect to allocated time, the trajectory obtained by the MPs is verified as dynamically feasible. When a collision checker based on the k-d tree search algorithm detects collision risk on extracted sample points from the planned trajectory, generating re-planned path candidates to avoid obstacles is performed. After rejecting unsafe route candidates, one applies the APF to select the best route among the remaining safe-path candidates. To validate the proposed approach, we simulated two meaningful scenario cases—the presence of static obstacles situation with local minima and dynamic environments with multiple UAVs present. The simulation results show that the proposed approach provides smooth, efficient, and dynamically feasible pathing compared to the APF.
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Pajak, Grzegorz, and Iwona Pajak. "Point-to-Point Collision-Free Trajectory Planning for Mobile Manipulators." Journal of Intelligent & Robotic Systems 85, no. 3-4 (June 16, 2016): 523–38. http://dx.doi.org/10.1007/s10846-016-0390-8.
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AbstractThe collision-free trajectory planning method subject to control constraints for mobile manipulators is presented. The robot task is to move from the current configuration to a given final position in the workspace. The motions are planned in order to maximise an instantaneous manipulability measure to avoid manipulator singularities. Inequality constraints on state variables i.e. collision avoidance conditions and mechanical constraints are taken into consideration. The collision avoidance is accomplished by local perturbation of the mobile manipulator motion in the obstacles neighbourhood. The fulfilment of mechanical constraints is ensured by using a penalty function approach. The proposed method guarantees satisfying control limitations resulting from capabilities of robot actuators by applying the trajectory scaling approach. Nonholonomic constraints in a Pfaffian form are explicitly incorporated into the control algorithm. A computer example involving a mobile manipulator consisting of nonholonomic platform (2,0) class and 3DOF RPR type holonomic manipulator operating in a three-dimensional task space is also presented.
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Ansuategui, A., A. Arruti, L. Susperregi, Y. Yurramendi, E. Jauregi, E. Lazkano, and B. Sierra. "Robot Trajectories Comparison: A Statistical Approach." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/298462.
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The task of planning a collision-free trajectory from a start to a goal position is fundamental for an autonomous mobile robot. Although path planning has been extensively investigated since the beginning of robotics, there is no agreement on how to measure the performance of a motion algorithm. This paper presents a new approach to perform robot trajectories comparison that could be applied to any kind of trajectories and in both simulated and real environments. Given an initial set of features, it automatically selects the most significant ones and performs a statistical comparison using them. Additionally, a graphical data visualization named polygraph which helps to better understand the obtained results is provided. The proposed method has been applied, as an example, to compare two different motion planners,FM2and WaveFront, using different environments, robots, and local planners.
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Kim, Dong-Hyung, Kyoosik Shin, Chang-Soo Han, and Ji Yeong Lee. "Sensor-based navigation of a car-like robot based on Bug family algorithms." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227, no. 6 (September 12, 2012): 1224–41. http://dx.doi.org/10.1177/0954406212458202.
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This article presents a sensor-based navigation algorithm for 3-degree-of-freedom car-like robots with a nonholonomic constraint. Similar to the classical Bug family algorithms, the proposed algorithm enables the car-like robot to navigate in a completely unknown environment by only using range sensor information. The car-like robot uses the local range sensor view to determine the local path so that it moves toward the goal. To guarantee that the car-like robot can approach the goal, the two modes of motion are repeated, termed as motion-to-goal and wall-following. The motion-to-goal behavior lets the robot directly move toward the goal and the wall-following behavior makes the car-like robot circumnavigate the obstacle boundary until it meets the leaving condition. For each behavior, the nonholonomic motion for the car-like robot is planned in terms of the turning radius at each step. The proposed algorithm is implemented with a real robot and the experimental results show the performance of the proposed algorithm.
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Ipanaque Zapata, Cesar A., and Jesús González. "Multitasking collision-free optimal motion planning algorithms in Euclidean spaces." Discrete Mathematics, Algorithms and Applications 12, no. 03 (June 2020): 2050040. http://dx.doi.org/10.1142/s1793830920500408.
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We present optimal motion planning algorithms which can be used in designing practical systems controlling objects moving in Euclidean space without collisions. Our algorithms are optimal in a very concrete sense, namely, they have the minimal possible number of local planners. Our algorithms are motivated by those presented by Mas-Ku and Torres-giese (as streamlined by Farber), and are developed within the more general context of the multitasking (a.k.a. higher) motion planning problem. In addition, an eventual implementation of our algorithms is expected to work more efficiently than previous ones when applied to systems with a large number of moving objects.
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Sahu, Pradip Kumar. "Optimal Trajectory Planning of Industrial Robots using Geodesic." IAES International Journal of Robotics and Automation (IJRA) 5, no. 3 (September 1, 2016): 190. http://dx.doi.org/10.11591/ijra.v5i3.pp190-198.
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<p>This paper intends to propose an optimal trajectory planning technique using geodesic to achieve smooth and accurate trajectory for industrial robots. Geodesic is a distance minimizing curve between any two points on a Riemannian manifold. A Riemannian metric has been assigned to the workspace by combining its position and orientation space together in order to attain geodesic conditions for desired motion of the end-effector. Previously, trajectory has been planned by considering position and orientation separately. However, practically we cannot plan separately because the manipulator joints are interlinked. Here, trajectory is planned by combining position and orientation together. Cartesian trajectories are shown by joint trajectories in which joint variables are treated as local coordinates of position space and orientation space. Then, the obtained geodesic equations for the workspace are evaluated for initial conditions of trajectory and results are plotted. The effectiveness of the geodesic method validated through numerical computations considering a Kawasaki RS06L robot model. The simulation results confirm the accuracy, smoothness and the optimality of the end-effector motion. </p>
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Zhai, Guang, Jing-rui Zhang, and Zhang Yao. "Circular Orbit Target Capture Using Space Tether-Net System." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/601482.
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The space tether-net system for on-orbit capture is proposed in this paper. In order to research the dynamic behaviors during system deployment, both free and nonfree deployment dynamics in circular orbit are developed; the system motion with respect to Local Vertical and Local Horizontal frame is also researched with analysis and simulation. The results show that in the case of free deployment, the capture net follows curve trajectories due to the relative orbit dynamic perturbation, and the initial deployment velocities are planned by state transformation equations for static and floating target captures; in the case of non-free deployment, the system undergoes an altitude libration along the Local Vertical, and the analytical solutions that describe the attitude libration are obtained by using variable separation and integration. Finally, the dynamics of postdeployment system is also proved marginally stable if the critical initial conditions are satisfied.
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Rana, A. S., and A. M. S. Zalzala. "Collision-free motion planning of multiarm robots using evolutionary algorithms." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 211, no. 5 (August 1, 1997): 373–84. http://dx.doi.org/10.1243/0959651971539902.
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This paper presents an evolutionary algorithm for the collision-free path planning of multiarm robots. A global path planning technique is used where the paths are represented by a string of via-points that the robots have to pass through, connected together by cubic spline polynomials. Since the entire paths of the robots are considered for optimization, the problem of deadlock between the arms and the static obstacles does not occur. Repeated path modification is done through evolutionary techniques to find an optimized path with respect to length and collision among the robots and the obstacles and the robots themselves. The proposed algorithm departs from simple genetic algorithms in that floating point vector strings represent the chromosomes and customized operators are used to improve upon the performance of the search. Moreover, a local search is carried out on each individual in addition to the global population based search. The result is a highly efficient path-planning algorithm that can deal with complex problems easily. Simulation results are presented for collision-free paths planned for two planar arms and then for two 3 degree-of-freedom (DOF) PUMA®-like arms moving in three-dimensional operational space.
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Liu, Yi, Ming Cong, Hang Dong, and Dong Liu. "Human skill integrated motion planning of assembly manipulation for 6R industrial robot." Industrial Robot: the international journal of robotics research and application 46, no. 1 (January 21, 2019): 171–80. http://dx.doi.org/10.1108/ir-09-2018-0189.
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Purpose The purpose of this paper is to propose a new method based on three-dimensional (3D) vision technologies and human skill integrated deep learning to solve assembly positioning task such as peg-in-hole. Design/methodology/approach Hybrid camera configuration was used to provide the global and local views. Eye-in-hand mode guided the peg to be in contact with the hole plate using 3D vision in global view. When the peg was in contact with the workpiece surface, eye-to-hand mode provided the local view to accomplish peg-hole positioning based on trained CNN. Findings The results of assembly positioning experiments proved that the proposed method successfully distinguished the target hole from the other same size holes according to the CNN. The robot planned the motion according to the depth images and human skill guide line. The final positioning precision was good enough for the robot to carry out force controlled assembly. Practical implications The developed framework can have an important impact on robotic assembly positioning process, which combine with the existing force-guidance assembly technology as to build a whole set of autonomous assembly technology. Originality/value This paper proposed a new approach to the robotic assembly positioning based on 3D visual technologies and human skill integrated deep learning. Dual cameras swapping mode was used to provide visual feedback for the entire assembly motion planning process. The proposed workpiece positioning method provided an effective disturbance rejection, autonomous motion planning and increased overall performance with depth images feedback. The proposed peg-hole positioning method with human skill integrated provided the capability of target perceptual aliasing avoiding and successive motion decision for the robotic assembly manipulation.
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Sahu, Pradip Kumar, and Bibhuti Bhusan Biswal. "Geodesic Approach for an Efficient Trajectory Planning of Mobile Robot Manipulators." International Journal of Mathematical, Engineering and Management Sciences 4, no. 5 (October 1, 2019): 1196–207. http://dx.doi.org/10.33889/ijmems.2019.4.5-094.
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In this paper, the geodesic approach has been employed for an effective, optimal, accurate and smooth trajectory planning of a mobile robot manipulator mechanism. Generally, geodesic can be described as the shortest curvature between two loci on a Riemannian manifold. In order to attain the planned end-effector motion, Riemannian metrics has been consigned to the forward kinematics of mobile robot wheel as well as the mobile robot manipulator workspace. The rotational angles of wheel and joint kinematic parameters are chosen as local coordinates of spaces to represent Cartesian trajectories for mobile wheel rotation trajectories and joint trajectories respectively. The geodesic equalities for a given set of boundary conditions are evaluated for the chosen Riemannian metrics and the computational results of the geodesic equations have been shown. So as to verify and validate the efficiency of the chosen geodesic scheme, the method has been implemented for the motion planning and optimization of a mobile robot with a simple 3R manipulator installed upon its platform.
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Zhang, Hongwen, and Zhanxia Zhu. "Sampling-Based Motion Planning for Free-Floating Space Robot without Inverse Kinematics." Applied Sciences 10, no. 24 (December 21, 2020): 9137. http://dx.doi.org/10.3390/app10249137.
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Motion planning is one of the most important technologies for free-floating space robots (FFSRs) to increase operation safety and autonomy in orbit. As a nonholonomic system, a first-order differential relationship exists between the joint angle and the base attitude of the space robot, which makes it pretty challenging to implement the relevant motion planning. Meanwhile, the existing planning framework must solve inverse kinematics for goal configuration and has the limitation that the goal configuration and the initial configuration may not be in the same connected domain. Thus, faced with these questions, this paper investigates a novel motion planning algorithm based on rapidly-exploring random trees (RRTs) for an FFSR from an initial configuration to a goal end-effector (EE) pose. In a motion planning algorithm designed to deal with differential constraints and restrict base attitude disturbance, two control-based local planners are proposed, respectively, for random configuration guiding growth and goal EE pose-guiding growth of the tree. The former can ensure the effective exploration of the configuration space, and the latter can reduce the possibility of occurrence of singularity while ensuring the fast convergence of the algorithm and no violation of the attitude constraints. Compared with the existing works, it does not require the inverse kinematics to be solved while the planning task is completed and the attitude constraint is preserved. The simulation results verify the effectiveness of the algorithm.
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Trung, G. K., and N. D. Vinh. "An overview of seismic ground response methods over the world and their applications in Vietnam." Geofizicheskiy Zhurnal 43, no. 2 (June 3, 2021): 131–51. http://dx.doi.org/10.24028/gzh.v43i2.230193.
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The estimation of the impact of earthquakes on buildings and mega structures in large urban areas is of extremely importance. That is why it always gets attentions from construction planners and policy makers who are concerned about construction rules. When earthquake occurs, the vibration is transferred to sites. Although the vibration intensity is at first not too strong, the motion probably becomes stronger and lasts longer under special conditions of the local site. Two famous examples for these effects occurred in Mexico City in 1985 and in Taiwan in 1999. There are a number of approaches to this problem, such as evaluations based on seismic field observations, the microtremor method, the method using the weak motion data, the method using the strong motion data, the one-dimensional wave propagation method or the three dimensional wave propagation method with simulation etc. In this paper, we will give an overview and discuss about the advantages and the disadvantages of the methods that have been commonly applied in the world. We also present the application of these methods in studies carried out in Vietnam in general and in particular, in Hanoi city. We found that the studies for Hanoi city were mainly carried out in the western areas of Hanoi and a few positions in the urban districts. In addition, the authors only gave comments about and assessments of the shear wave velocity, and classified the ground type without a detailed map of local site effects for the entire area of Hanoi. In order to obtain a full site effects evaluation for Hanoi city, future studies should focus on the application of 1D analysis for the central area of Hanoi city and combining 1D analysis with 2D or 3D to give a better picture about the impact of local site effects. This hybrid approach is necessary in order to compare and verify the data obtained by the empirical and the analytical methods. On the other hand, many problems need to be addressed, for instance, the construction of a detailed 3D geological model for Hanoi, the calculation of the dominant periods and the amplification of the local soil conditions for the urban areas.
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Karpenko, Mark, Nariman Sepehri, and John Anderson. "Decentralized Coordinated Motion Control of Two Hydraulic Actuators Handling a Common Object." Journal of Dynamic Systems, Measurement, and Control 129, no. 5 (January 25, 2007): 729–41. http://dx.doi.org/10.1115/1.2764516.
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In this paper, reinforcement learning is applied to coordinate, in a decentralized fashion, the motions of a pair of hydraulic actuators whose task is to firmly hold and move an object along a specified trajectory under conventional position control. The learning goal is to reduce the interaction forces acting on the object that arise due to inevitable positioning errors resulting from the imperfect closed-loop actuator dynamics. Each actuator is therefore outfitted with a reinforcement learning neural network that modifies a centrally planned formation constrained position trajectory in response to the locally measured interaction force. It is shown that the actuators, which form a multiagent learning system, can learn decentralized control strategies that reduce the object interaction forces and thus greatly improve their coordination on the manipulation task. However, the problem of credit assignment, a common difficulty in multiagent learning systems, prevents the actuators from learning control strategies where each actuator contributes equally to reducing the interaction force. This problem is resolved in this paper via the periodic communication of limited local state information between the reinforcement learning actuators. Using both simulations and experiments, this paper examines some of the issues pertaining to learning in dynamic multiagent environments and establishes reinforcement learning as a potential technique for coordinating several nonlinear hydraulic manipulators performing a common task.
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Martyshkin, Alexey I. "Motion Planning Algorithm for a Mobile Robot with a Smart Machine Vision System." Nexo Revista Científica 33, no. 02 (December 31, 2020): 651–71. http://dx.doi.org/10.5377/nexo.v33i02.10800.
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This study is devoted to the challenges of motion planning for mobile robots with smart machine vision systems. Motion planning for mobile robots in the environment with obstacles is a problem to deal with when creating robots suitable for operation in real-world conditions. The solutions found today are predominantly private, and are highly specialized, which prevents judging of how successful they are in solving the problem of effective motion planning. Solutions with a narrow application field already exist and are being already developed for a long time, however, no major breakthrough has been observed yet. Only a systematic improvement in the characteristics of such systems can be noted. The purpose of this study: develop and investigate a motion planning algorithm for a mobile robot with a smart machine vision system. The research subject for this article is a motion planning algorithm for a mobile robot with a smart machine vision system. This study provides a review of domestic and foreign mobile robots that solve the motion planning problem in a known environment with unknown obstacles. The following navigation methods are considered for mobile robots: local, global, individual. In the course of work and research, a mobile robot prototype has been built, capable of recognizing obstacles of regular geometric shapes, as well as plan and correct the movement path. Environment objects are identified and classified as obstacles by means of digital image processing methods and algorithms. Distance to the obstacle and relative angle are calculated by photogrammetry methods, image quality is improved by linear contrast enhancement and optimal linear filtering using the Wiener-Hopf equation. Virtual tools, related to mobile robot motion algorithm testing, have been reviewed, which led us to selecting Webots software package for prototype testing. Testing results allowed us to make the following conclusions. The mobile robot has successfully identified the obstacle, planned a path in accordance with the obstacle avoidance algorithm, and continued moving to the destination. Conclusions have been drawn regarding the concluded research.
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Jin, Hongzhe, Hui Zhang, Zhangxing Liu, Decai Yang, Dongyang Bie, He Zhang, Ge Li, Yanhe Zhu, and Jie Zhao. "A Synthetic Algorithm for Tracking a Moving Object in a Multiple-Dynamic Obstacles Environment Based on Kinematically Planar Redundant Manipulators." Mathematical Problems in Engineering 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/7310105.
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This paper presents a synthetic algorithm for tracking a moving object in a multiple-dynamic obstacles environment based on kinematically planar manipulators. By observing the motions of the object and obstacles, Spline filter associated with polynomial fitting is utilized to predict their moving paths for a period of time in the future. Several feasible paths for the manipulator in Cartesian space can be planned according to the predicted moving paths and the defined feasibility criterion. The shortest one among these feasible paths is selected as the optimized path. Then the real-time path along the optimized path is planned for the manipulator to track the moving object in real-time. To improve the convergence rate of tracking, a virtual controller based on PD controller is designed to adaptively adjust the real-time path. In the process of tracking, the null space of inverse kinematic and the local rotation coordinate method (LRCM) are utilized for the arms and the end-effector to avoid obstacles, respectively. Finally, the moving object in a multiple-dynamic obstacles environment is thus tracked via real-time updating the joint angles of manipulator according to the iterative method. Simulation results show that the proposed algorithm is feasible to track a moving object in a multiple-dynamic obstacles environment.