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Статті в журналах з теми "Dynamic Modeling, Collision Avoidance, Hybrid Systems"
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Fan, Tingxiang, Pinxin Long, Wenxi Liu, and Jia Pan. "Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios." International Journal of Robotics Research 39, no. 7 (May 31, 2020): 856–92. http://dx.doi.org/10.1177/0278364920916531.
Повний текст джерелаАнотація:
Developing a safe and efficient collision-avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generates its paths with limited observation of other robots’ states and intentions. Prior distributed multi-robot collision-avoidance systems often require frequent inter-robot communication or agent-level features to plan a local collision-free action, which is not robust and computationally prohibitive. In addition, the performance of these methods is not comparable with their centralized counterparts in practice. In this article, we present a decentralized sensor-level collision-avoidance policy for multi-robot systems, which shows promising results in practical applications. In particular, our policy directly maps raw sensor measurements to an agent’s steering commands in terms of the movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to learn an optimal policy. The policy is trained over a large number of robots in rich, complex environments simultaneously using a policy-gradient-based reinforcement-learning algorithm. The learning algorithm is also integrated into a hybrid control framework to further improve the policy’s robustness and effectiveness. We validate the learned sensor-level collision-3avoidance policy in a variety of simulated and real-world scenarios with thorough performance evaluations for large-scale multi-robot systems. The generalization of the learned policy is verified in a set of unseen scenarios including the navigation of a group of heterogeneous robots and a large-scale scenario with 100 robots. Although the policy is trained using simulation data only, we have successfully deployed it on physical robots with shapes and dynamics characteristics that are different from the simulated agents, in order to demonstrate the controller’s robustness against the simulation-to-real modeling error. Finally, we show that the collision-avoidance policy learned from multi-robot navigation tasks provides an excellent solution for safe and effective autonomous navigation for a single robot working in a dense real human crowd. Our learned policy enables a robot to make effective progress in a crowd without getting stuck. More importantly, the policy has been successfully deployed on different types of physical robot platforms without tedious parameter tuning. Videos are available at https://sites.google.com/view/hybridmrca .
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Moon, Jungho, Byung-Yoon Lee, and Min-Jea Tahk. "A Hybrid Dynamic Window Approach for Collision Avoidance of VTOL UAVs." International Journal of Aeronautical and Space Sciences 19, no. 4 (August 1, 2018): 889–903. http://dx.doi.org/10.1007/s42405-018-0061-z.
Повний текст джерела
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Yang, Zhen, Junli Li, Liwei Yang, Qian Wang, Ping Li, and Guofeng Xia. "Path planning and collision avoidance methods for distributed multi-robot systems in complex dynamic environments." Mathematical Biosciences and Engineering 20, no. 1 (2022): 145–78. http://dx.doi.org/10.3934/mbe.2023008.
Повний текст джерелаАнотація:
<abstract> <p>Multi-robot systems are experiencing increasing popularity in joint rescue, intelligent transportation, and other fields. However, path planning and navigation obstacle avoidance among multiple robots, as well as dynamic environments, raise significant challenges. We propose a distributed multi-mobile robot navigation and obstacle avoidance method in unknown environments. First, we propose a bidirectional alternating jump point search A* algorithm (BAJPSA*) to obtain the robot's global path in the prior environment and further improve the heuristic function to enhance efficiency. We construct a robot kinematic model based on the dynamic window approach (DWA), present an adaptive navigation strategy, and introduce a new path tracking evaluation function that improves path tracking accuracy and optimality. To strengthen the security of obstacle avoidance, we modify the decision rules and obstacle avoidance rules of the single robot and further improve the decision avoidance capability of multi-robot systems. Moreover, the mainstream prioritization method is used to coordinate the local dynamic path planning of our multi-robot systems to resolve collision conflicts, reducing the difficulty of obstacle avoidance and simplifying the algorithm. Experimental results show that this distributed multi-mobile robot motion planning method can provide better navigation and obstacle avoidance strategies in complex dynamic environments, which provides a technical reference in practical situations.</p> </abstract>
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Park, Jaeyong, Arda Kurt, and Ümit Özgüner. "Hybrid Systems Modeling and Reachability-Based Controller Design Methods for Vehicular Automation." Unmanned Systems 02, no. 02 (April 2014): 101–19. http://dx.doi.org/10.1142/s2301385014500071.
Повний текст джерелаАнотація:
In this study, applicability of verification and correct-by-design hybrid systems modeling and reachability-based controllers for vehicular automation are investigated. Two perspectives in hybrid systems modeling will be introduced, and then reachability analysis techniques will be developed to compute exact reachable sets from a specified unsafe set. Using level set methods, a Hamilton–Jacobi–Isaacs equation is derived whose solutions describe the boundaries of the finite time backward reachable set, which will be manipulated to design a safe controller that guarantees the safety of a given system. An automated longitudinal controller with a fully integrated collision avoidance functionality will be designed as a hybrid system and validated through simulations with a number of different scenarios in order to illustrate the potential of verification methods in automated vehicles.
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Omisore, Olatunji Mumini, Shipeng Han, Yousef Al-Handarish, Wenjing Du, Wenke Duan, Toluwanimi Oluwadara Akinyemi, and Lei Wang. "Motion and Trajectory Constraints Control Modeling for Flexible Surgical Robotic Systems." Micromachines 11, no. 4 (April 7, 2020): 386. http://dx.doi.org/10.3390/mi11040386.
Повний текст джерелаАнотація:
Success of the da Vinci surgical robot in the last decade has motivated the development of flexible access robots to assist clinical experts during single-port interventions of core intrabody organs. Prototypes of flexible robots have been proposed to enhance surgical tasks, such as suturing, tumor resection, and radiosurgery in human abdominal areas; nonetheless, precise constraint control models are still needed for flexible pathway navigation. In this paper, the design of a flexible snake-like robot is presented, along with the constraints model that was proposed for kinematics and dynamics control, motion trajectory planning, and obstacle avoidance during motion. Simulation of the robot and implementation of the proposed control models were done in Matlab. Several points on different circular paths were used for evaluation, and the results obtained show the model had a mean kinematic error of 0.37 ± 0.36 mm with very fast kinematics and dynamics resolution times. Furthermore, the robot’s movement was geometrically and parametrically continuous for three different trajectory cases on a circular pathway. In addition, procedures for dynamic constraint and obstacle collision detection were also proposed and validated. In the latter, a collision-avoidance scheme was kept optimal by keeping a safe distance between the robot’s links and obstacles in the workspace. Analyses of the results showed the control system was optimal in determining the necessary joint angles to reach a given target point, and motion profiles with a smooth trajectory was guaranteed, while collision with obstacles were detected a priori and avoided in close to real-time. Furthermore, the complexity and computational effort of the algorithmic models were negligibly small. Thus, the model can be used to enhance the real-time control of flexible robotic systems.
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Ajeil, Fatin Hassan, Ibraheem Kasim Ibraheem, Ahmad Taher Azar, and Amjad J. Humaidi. "Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment." International Journal of Advanced Robotic Systems 17, no. 3 (May 1, 2020): 172988142092949. http://dx.doi.org/10.1177/1729881420929498.
Повний текст джерелаАнотація:
The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. Two modifications are suggested to improve the searching process of the standard bat algorithm with the result of two novel algorithms. The first algorithm is a Modified Frequency Bat algorithm, and the second is a hybridization between the Particle Swarm Optimization with the Modified Frequency Bat algorithm, namely, the Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithm. Both Modified Frequency Bat and Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithms have been integrated with a proposed technique for obstacle detection and avoidance and are applied to different static and dynamic environments using free-space modeling. Moreover, a new procedure is proposed to convert the infeasible solutions suggested via path the proposed swarm-inspired optimization-based path planning algorithm into feasible ones. The simulations are run in MATLAB environment to test the validation of the suggested algorithms. They have shown that the proposed path planning algorithms result in superior performance by finding the shortest and smoothest collision-free path under various static and dynamic scenarios.
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Maw, Aye Aye, Maxim Tyan, Tuan Anh Nguyen, and Jae-Woo Lee. "iADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV." Applied Sciences 11, no. 9 (April 27, 2021): 3948. http://dx.doi.org/10.3390/app11093948.
Повний текст джерелаАнотація:
Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an autonomous UAV. In particular, we aim to achieve a highly autonomous UAV mission planning system that is adaptive to real-world environments consisting of both static and moving obstacles for collision avoidance capabilities. To achieve adaptive behavior for real-world problems, a simulator is required that can imitate real environments for learning. For this reason, the simulator must be sufficiently flexible to allow the UAV to learn about the environment and to adapt to real-world conditions. In our scheme, the UAV first learns about the environment via a simulator, and only then is it applied to the real-world. The proposed system is divided into two main parts: optimal flight path generation and collision avoidance. A hybrid path planning approach is developed by combining a graph-based path planning algorithm with a learning-based algorithm for local planning to allow the UAV to avoid a collision in real time. The global path planning problem is solved in the first stage using a novel anytime incremental search algorithm called improved Anytime Dynamic A* (iADA*). A reinforcement learning method is used to carry out local planning between waypoints, to avoid any obstacles within the environment. The developed hybrid path planning system was investigated and validated in an AirSim environment. A number of different simulations and experiments were performed using AirSim platform in order to demonstrate the effectiveness of the proposed system for an autonomous UAV. This study helps expand the existing research area in designing efficient and safe path planning algorithms for UAVs.
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Finaev, V. I., M. Yu Medvedev, V. Kh Pshikhopov, V. A. Pereverzev, and V. V. Soloviev. "Unmanned Powerboat Motion Terminal Control in an Environment with Moving Obstacles." Mekhatronika, Avtomatizatsiya, Upravlenie 22, no. 3 (March 2, 2021): 145–54. http://dx.doi.org/10.17587/mau.22.145-154.
Повний текст джерелаАнотація:
The major point for consideration throughout this paper is controlling the motion of an unmanned powerboat in an obstructed environment with stationary and moving objects. It offers a procedure for the terminal control law development based on the powerboat programmed motion trajectory in a polynomial form and proposes position-trajectory-based control algorithms. A hybrid method based on virtual fields and unstable driving modes, taking into account powerboat speeds and obstacles, is used to plan motion trajectories for obstacle avoidance. There were experiments carried out to test the developed methods and algorithms meanwhile estimating the energy consumption for control, the length of the trajectory and the safety indicator for obstacle avoidance. The novelty of the proposed approach lies in the method used to develop a local movement trajectory in the field with obstacles and in the hybridization of trajectory scheduling methods. This approach allows us to achieve a given safe distance when avoiding obstacles and virtually eliminate the chances of an emergency collision. The presented results can be used in systems of boats autonomous motion control and allow safe stationary and dynamic obstacles avoidance.
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Dai, Wei, Yongjun Pan, Chuan Min, Sheng-Peng Zhang, and Jian Zhao. "Real-Time Modeling of Vehicle’s Longitudinal-Vertical Dynamics in ADAS Applications." Actuators 11, no. 12 (December 16, 2022): 378. http://dx.doi.org/10.3390/act11120378.
Повний текст джерелаАнотація:
The selection of an appropriate method for modeling vehicle dynamics heavily depends on the application. Due to the absence of human intervention, the demand for an accurate and real-time model of vehicle dynamics for intelligent control increases for autonomous vehicles. This paper develops a multibody vehicle model for longitudinal-vertical dynamics applicable to advanced driver assistance (ADAS) applications. The dynamic properties of the chassis, suspension, and tires are considered and modeled, which results in accurate vehicle dynamics and states. Unlike the vehicle dynamics models built into commercial software packages, such as ADAMS and CarSim, the proposed nonlinear dynamics model poses the equations of motion using a subset of relative coordinates. Therefore, the real-time simulation is conducted to improve riding performance and transportation safety. First, a vehicle system is modeled using a semi-recursive multibody dynamics formulation, and the vehicle kinematics and dynamics are accurately calculated using the system tree-topology. Second, a fork-arm removal technique based on the rod-removal technique is proposed to reduce the number of bodies, relative coordinates, and equations constrained by loop-closure. This increase the computational efficiency even further. Third, the dynamic simulations of the vehicle are performed on bumpy and sloping roads. The accuracy and efficiency of the numerical results are compared to the reference data. The comparative results demonstrate that the proposed vehicle model is effective. This efficient model can be utilized for the intelligent control of vehicle ADAS applications, such as forward collision avoidance, adaptive cruise control, and platooning.
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Hafez, Ahmed T., and Mohamed A. Kamel. "Cooperative Task Assignment and Trajectory Planning of Unmanned Systems Via HFLC and PSO." Unmanned Systems 07, no. 02 (April 2019): 65–81. http://dx.doi.org/10.1142/s2301385019500018.
Повний текст джерелаАнотація:
This paper investigates the problems of cooperative task assignment and trajectory planning for teams of cooperative unmanned aerial vehicles (UAVs). A novel approach of hierarchical fuzzy logic controller (HFLC) and particle swarm optimization (PSO) is proposed. Initially, teams of UAVs are moving in a pre-defined formation covering a specified area. When one or more targets are detected, the teams send a package of information to the ground station (GS) including the target’s degree of threat, degree of importance, and the separating distance between each team and each detected target. Based on the gathered information, the ground station assigns the teams to the targets. HFLC is implemented in the GS to solve the assignment problem ensuring that each team is assigned to a unique target. Next, each team plans its own path by formulating the path planning problem as an optimization problem. The objective in this case is to minimize the time to reach their destination considering the UAVs dynamic constraints and collision avoidance between teams. A hybrid approach of control parametrization and time discretization (CPTD) and PSO is proposed to solve this optimization problem. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.
Дисертації з теми "Dynamic Modeling, Collision Avoidance, Hybrid Systems"
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Vesentini, Federico, Luca Di Persio, and Riccardo Muradore. "Collision avoidance and dynamic modeling for wheeled mobile robots and industrial manipulators." Doctoral thesis, 2022. http://hdl.handle.net/11562/1068427.
Повний текст джерелаАнотація:
Collision Avoidance and Dynamic Modeling are key topics for researchers dealing with mobile and industrial robotics. A wide variety of algorithms, approaches and methodologies have been exploited, designed or adapted to tackle the problems of finding safe trajectories for mobile robots and industrial manipulators, and of calculating reliable dynamics models able to capture expected and possible also unexpected behaviors of robots. The knowledge of these two aspects and their potential is important to ensure the efficient and correct functioning of Industry 4.0 plants such as automated warehouses, autonomous surveillance systems and assembly lines. Collision avoidance is a crucial aspect to improve automation and safety, and to solve the problem of planning collision-free trajectories in systems composed of multiple autonomous agents such as unmanned mobile robots and manipulators with several degrees of freedom. A rigorous and accurate model explaining the dynamics of robots, is necessary to tackle tasks such as simulation, torque estimation, reduction of mechanical vibrations and design of control law.
Частини книг з теми "Dynamic Modeling, Collision Avoidance, Hybrid Systems"
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Kim, J. H., S. Okuma, S. Hayakawa, N. Tsuchida, T. Suzuki, K. Hayashi, M. Shimizu, and S. Kido. "Modeling of Driver's Collision Avoidance Behavior Based on Expression as Hybrid Dynamical System." In Systems and Human Science, 323–35. Elsevier, 2005. http://dx.doi.org/10.1016/b978-044451813-2/50026-9.
Повний текст джерелаТези доповідей конференцій з теми "Dynamic Modeling, Collision Avoidance, Hybrid Systems"
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Gan, Xusheng, Yarong Wu, Pingni Liu, and Qian Wang. "Dynamic Collision Avoidance Zone Modeling Method Based on UAV Emergency Collision Avoidance Trajectory." In 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS). IEEE, 2020. http://dx.doi.org/10.1109/icaiis49377.2020.9194915.
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Yi, Xiongfeng, Zheng Chen, and Animesh Chakravarthy. "Cooperative Collision Avoidance Control of Robotic Fish Propelled by a Servo/IPMC Driven Hybrid Tail." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9228.
Повний текст джерелаАнотація:
Abstract This paper develops and demonstrates cooperative collision avoidance control on two robotic fish propelled by a servo motor and an ionic polymer-metal composite (IPMC)-driven fish tail. First, experiments conducted on a servo motor/IPMC-driven fish demonstrate an impulsive turning behavior in the fish’s trajectory under the application of a specific frequency, amplitude of the servo motor, and a constant voltage on the IPMC joint. These experiments validate the ‘back relaxation’ of the IPMC joint by observing the angular velocity and the centripetal acceleration of the fish. This impulsive turning speed due to the ‘back relaxation’ of IPMC joint is subsequently modeled by a transfer function and this transfer function is then integrated into the development of the collision avoidance laws for the fish. The collision avoidance control law utilizes the impulsive turning capability of the robotic fish. An experimental validation of the collision avoidance law is performed.
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Mahani, Maziar Fooladi, and Yue Wang. "Trust-Based Runtime Verification for Multi-Quad-Rotor Motion Planning With a Human-in-the-Loop." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9174.
Повний текст джерелаАнотація:
In this paper, we propose a trust-based runtime verification (RV) framework for deploying multiple quad-rotors with a human-in-the-loop (HIL). By bringing together approaches from runtime verification, trust-based decision-making, human-robot interaction (HRI), and hybrid systems, we develop a unified framework that is capable of integrating human cognitive skills with autonomous capabilities of multi-robot systems to improve system performance and maximize the intuitiveness of the human-robot-interaction. On top of the RV framework, we utilize a probabilistic trust inference model as the key component in forming the HRI, designed to maintain the system performance. A violation avoidance controller is designed to account for the unexpected/unmodeled environment behaviors e.g. collision with static/moving obstacles. We also use the automata theoretic approaches to generate motion plans for the quad-rotors working in a partially-known environment by automatic synthesis of controllers enforcing specifications given in temporal logic languages. Finally, we illustrated the effectiveness of this framework as well as its feasibility through a simulated case study.
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Zhu, Xiaoyuan, Jian Chen, Yan Ma, Jianqiang Deng, and Yuexuan Wang. "Predictive Motion Planning for Autonomous Vehicles With Geometric Constraints via Convex Optimization." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3169.
Повний текст джерелаАнотація:
Abstract In this paper, we propose an MPC-based motion planning algorithm, including a decision-making module, an obstacle-constraints generating module, and an MPC-based planning module. The designed decision module effectively distinguishes between structured and unstructured roads and processes them separately, so that the algorithm is more robust in different environments. Besides, the movement of obstacles is considered in the decision-making and obstacle constraints generating module. By processing obstacles with lateral and longitudinal speed separately, obstacle avoidance can be done in scenarios with moving obstacles, including moving obstacles crossing the road. Instead of treating the vehicle as a mass point, we explicitly consider the geometric constraints by modeling the vehicle as three intersecting circles when generating obstacle constraints. This ensures that the vehicle is collision-free in motion planning, especially when the vehicle turns. For non-convex obstacle constraints, we propose an algorithm that generates up to two alternative linear constraints to convexify the obstacle constraints for improving computational efficiency. In MPC, we consider the vehicle kino-dynamic constraints and two generated linear constraints. Therefore, the proposed method can achieve better real-time performance and can be applied to more complicated traffic scenarios with moving obstacles. Simulation results in three different scenarios show that motion planning can achieve satisfactory performance in both structured and unstructured roads with moving obstacles.
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Matveev, Konstantin I. "Modeling of Autonomous Hydrofoil Craft Avoiding Moving Obstacles." In SNAME Maritime Convention. SNAME, 2022. http://dx.doi.org/10.5957/smc-2022-017.
Повний текст джерелаАнотація:
Hydrodynamic arrangements of most autonomous surface marine vessels belong to conventional displacement-type monohulls or catamarans. Applications of advanced hydrodynamic concepts, such as considered here hydrofoils, can help unmanned marine craft operate efficiently at higher speeds and have better seakeeping. However, dynamics of such boats are rather complex. In this work, a 6-DOF dynamics model with engineering correlations for hydrodynamic forces is applied to simulate motions of an autonomous hydrofoil craft. Collision avoidance maneuvers based on introduction of a dynamic waypoint outside unsafe zone around a moving obstacle have been modeled. The description of planning decisions, implementation of controls, simulated boat trajectories, and time histories of kinematic and controlled variables are presented and discussed. The developed model can be used for design of unmanned hydrofoil craft and control systems of fast autonomous boats.
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Abbaspour, Adel, Hadi Zare Jafari, Mohammad Ali Askari Hemmat, and Khalil Alipour. "Redundancy Resolution for Singularity Avoidance of Wheeled Mobile Manipulators." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-38639.
Повний текст джерелаАнотація:
Mobile robots consist of a mobile platform with manipulator can provide interesting functionalities in a number of applications, since, combination of platform and manipulator causes robot operates in extended work space. The analysis of these systems includes kinematics redundancy that makes more complicated problem. However, it gives more feasibility to robotic systems because of the existence of multiple solutions in a specified workspace. This paper presents a novel combination of evolutionary algorithms and artificial potential field theory for motion planning of mobile manipulator which guaranteed collision and singularity avoidance. In the proposed algorithm, the developed concepts of potential field method are applied to obstacle avoidance and interaction of mobile base with manipulator is used as a new idea for singularity avoidance ability of intermediate links for mobile operations. For this purpose, kinematic and dynamic modeling is derived to define redundant solutions. Afterward, methods from potential field theory combine with evolutionary algorithms to provide an optimum solution among possibly of redundancy resolution scheme. A controller based on dynamic feedback linearization is augmented to track the selective motion trajectory. Simulation results verify obstacle avoidance, singularity avoidance for the manipulators and asymptotic convergence of the robots errors.