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Journal articles on the topic 'Kinodynamic motion planning'

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Published: 14 March 2023

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1

Masoud, Ahmad. "Kinodynamic Motion Planning." IEEE Robotics & Automation Magazine 17, no. 1 (March 2010): 85–99. http://dx.doi.org/10.1109/mra.2010.935794.

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2

Donald, Bruce, Patrick Xavier, John Canny, and John Reif. "Kinodynamic motion planning." Journal of the ACM 40, no. 5 (November 1993): 1048–66. http://dx.doi.org/10.1145/174147.174150.

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3

Choi, Jiwung. "Kinodynamic Motion Planning for Autonomous Vehicles." International Journal of Advanced Robotic Systems 11, no. 6 (January 2014): 90. http://dx.doi.org/10.5772/58683.

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4

Kulathunga, G., D. Devitt, R. Fedorenko, and A. Klimchik. "Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs)." Nelineinaya Dinamika 17, no. 4 (2021): 491–505. http://dx.doi.org/10.20537/nd210410.

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Any obstacle-free path planning algorithm, in general, gives a sequence of waypoints that connect start and goal positions by a sequence of straight lines, which does not ensure the smoothness and the dynamic feasibility to maneuver the MAV. Kinodynamic-based motion planning is one of the ways to impose dynamic feasibility in planning. However, kinodynamic motion planning is not an optimal solution due to high computational demands for real-time applications. Thus, we explore path planning followed by kinodynamic smoothing while ensuring the dynamic feasibility of MAV. The main difference in the proposed technique is not to use kinodynamic planning when finding a feasible path, but rather to apply kinodynamic smoothing along the obtained feasible path. We have chosen a geometric-based path planning algorithm “RRT*” as the path finding algorithm. In the proposed technique, we modified the original RRT* introducing an adaptive search space and a steering function that helps to increase the consistency of the planner. Moreover, we propose a multiple RRT* that generates a set of desired paths. The optimal path from the generated paths is selected based on a cost function. Afterwards, we apply kinodynamic smoothing that will result in a dynamically feasible as well as obstacle-free path. Thereafter, a b-spline-based trajectory is generated to maneuver the vehicle autonomously in unknown environments. Finally, we have tested the proposed technique in various simulated environments. According to the experiment results, we were able to speed up the path planning task by 1.3 times when using the proposed multiple RRT* over the original RRT*.
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5

Pham, Quang-Cuong, Stéphane Caron, Puttichai Lertkultanon, and Yoshihiko Nakamura. "Admissible velocity propagation: Beyond quasi-static path planning for high-dimensional robots." International Journal of Robotics Research 36, no. 1 (November 2, 2016): 44–67. http://dx.doi.org/10.1177/0278364916675419.

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Path-velocity decomposition is an intuitive yet powerful approach to addressing the complexity of kinodynamic motion planning. The difficult trajectory planning problem is solved in two separate, simpler steps: first, a path is found in the configuration space that satisfies the geometric constraints (path planning), and second, a time-parameterization of that path satisfying the kinodynamic constraints is found. A fundamental requirement is that the path found in the first step must be time-parameterizable. Most existing works fulfill this requirement by enforcing quasi-static constraints during the path planning step, resulting in an important loss in completeness. We propose a method that enables path-velocity decomposition to discover truly dynamic motions, i.e. motions that are not quasi-statically executable. At the heart of the proposed method is a new algorithm – Admissible Velocity Propagation – which, given a path and an interval of reachable velocities at the beginning of that path, computes exactly and efficiently the interval of all the velocities the system can reach after traversing the path, while respecting the system’s kinodynamic constraints. Combining this algorithm with usual sampling-based planners then gives rise to a family of new trajectory planners that can appropriately handle kinodynamic constraints while retaining the advantages associated with path-velocity decomposition. We demonstrate the efficiency of the proposed method on some difficult kinodynamic planning problems, where, in particular, quasi-static methods are guaranteed to fail.
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6

Ha, Jung-Su, Han-Lim Choi, and Jeong Hwan Jeon. "Iterative methods for efficient sampling-based optimal motion planning of nonlinear systems." International Journal of Applied Mathematics and Computer Science 28, no. 1 (March 1, 2018): 155–68. http://dx.doi.org/10.2478/amcs-2018-0012.

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AbstractThis paper extends the RRT* algorithm, a recently developed but widely used sampling based optimal motion planner, in order to effectively handle nonlinear kinodynamic constraints. Nonlinearity in kinodynamic differential constraints often leads to difficulties in choosing an appropriate distance metric and in computing optimized trajectory segments in tree construction. To tackle these two difficulties, this work adopts the affine quadratic regulator-based pseudo-metric as the distance measure and utilizes iterative two-point boundary value problem solvers to compute the optimized segments. The proposed extension then preserves the inherent asymptotic optimality of the RRT* framework, while efficiently handling a variety of kinodynamic constraints. Three numerical case studies validate the applicability of the proposed method.
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7

Ogay, Dmitriy, and Eun-Gyung Kim. "Kinodynamic Motion Planning with Artificial Wavefront Propagation." Journal of information and communication convergence engineering 11, no. 4 (December 31, 2013): 274–81. http://dx.doi.org/10.6109/jicce.2013.11.4.274.

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8

Hsu, David, Robert Kindel, Jean-Claude Latombe, and Stephen Rock. "Randomized Kinodynamic Motion Planning with Moving Obstacles." International Journal of Robotics Research 21, no. 3 (March 2002): 233–55. http://dx.doi.org/10.1177/027836402320556421.

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9

Sakcak, Basak, Luca Bascetta, Gianni Ferretti, and Maria Prandini. "Sampling-based optimal kinodynamic planning with motion primitives." Autonomous Robots 43, no. 7 (January 14, 2019): 1715–32. http://dx.doi.org/10.1007/s10514-019-09830-x.

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10

MOTONAKA, Kimiko, Keigo WATANABE, and Shoichi MAEYAMA. "Kinodynamic motion planning and control for a quadrotor." Transactions of the JSME (in Japanese) 81, no. 825 (2015): 14–00631. http://dx.doi.org/10.1299/transjsme.14-00631.

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11

Reif, John H., and Hongyan Wang. "Nonuniform Discretization for Kinodynamic Motion Planning and its Applications." SIAM Journal on Computing 30, no. 1 (January 2000): 161–90. http://dx.doi.org/10.1137/s0097539798331975.

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12

Littlefield, Zakary, David Surovik, Massimo Vespignani, Jonathan Bruce, Weifu Wang, and Kostas E. Bekris. "Kinodynamic planning for spherical tensegrity locomotion with effective gait primitives." International Journal of Robotics Research 38, no. 12-13 (May 23, 2019): 1442–62. http://dx.doi.org/10.1177/0278364919847763.

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Tensegrity-based robots can achieve locomotion through shape deformation and compliance. They are highly adaptable to their surroundings, and are lightweight, low cost, and physically robust. Their high dimensionality and strongly dynamic nature, however, can complicate motion planning. Efforts to date have primarily considered quasi-static reconfiguration and short-term dynamic motion of tensegrity robots, which do not fully exploit the underlying system dynamics in the long term. Longer-horizon planning has previously required costly search over the full space of valid control inputs. This work synthesizes new and existing approaches to produce dynamic long-term motion while balancing the computational demand. A numerical process based upon quasi-static assumptions is first applied to deform the system into an unstable configuration, causing forward motion. The dynamical characteristics of the result are then altered via a few simple parameters to produce a small but diverse set of useful behaviors. The proposed approach takes advantage of identified symmetries on the prototypical spherical tensegrity robot, which reduce the number of needed gaits but allow motion along different directions. These gaits are first combined with a standard search method to achieve long-term planning in environments where the developed gaits are effective. For more complex environments, the various motion primitives are paired with the fall-back option of random valid actions and are used by an informed sampling-based kinodynamic motion planner with anytime properties. Evaluations using a physics-based model for the prototypical robot demonstrate that modest but efficiently applied search effort can unlock the utility of dynamic tensegrity motion to produce high-quality solutions.
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13

Zuhaib, K. M., J. Iqbal, A. M. Bughio, S. A. S. Bukhari, and K. Kanwar. "Collision Avoidance of a Kinodynamically Constrained System from Passive Agents." Engineering, Technology & Applied Science Research 11, no. 1 (February 6, 2021): 6760–65. http://dx.doi.org/10.48084/etasr.4022.

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Robot motion planning in dynamic environments is significantly difficult, especially when the future trajectories of dynamic obstacles are only predictable over a short time interval and can change frequently. Moreover, a robot’s kinodynamic constraints make the task more challenging. This paper proposes a novel collision avoidance scheme for navigating a kinodynamically constrained robot among multiple passive agents with partially predictable behavior. For this purpose, this paper presents a new approach that maps collision avoidance and kinodynamic constraints on robot motion as geometrical bounds of its control space. This was achieved by extending the concept of nonlinear velocity obstacles to incorporate the robot’s kinodynamic constraints. The proposed concept of bounded control space was used to design a collision avoidance strategy for a car-like robot by employing a predict-plan-act framework. The results of simulated experiments demonstrate the effectiveness of the proposed algorithm when compared to existing velocity obstacle based approaches.
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14

Harada, Kensuke, Mitsuharu Morisawa, Shin-ichiro Nakaoka, Kenji Kaneko, and Shuuji Kajita. "2A1-D11 Kinodynamic Motion Planning for a Humanoid Robot on Uneven Terrain." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2008 (2008): _2A1—D11_1—_2A1—D11_4. http://dx.doi.org/10.1299/jsmermd.2008._2a1-d11_1.

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15

Paden, Brian, Valerio Varricchio, and Emilio Frazzoli. "Verification and Synthesis of Admissible Heuristics for Kinodynamic Motion Planning." IEEE Robotics and Automation Letters 2, no. 2 (April 2017): 648–55. http://dx.doi.org/10.1109/lra.2017.2651157.

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16

Landry, Chantal, Wolfgang Welz, and Matthias Gerdts. "Combining discrete and continuous optimization to solve kinodynamic motion planning problems." Optimization and Engineering 17, no. 3 (November 4, 2015): 533–56. http://dx.doi.org/10.1007/s11081-015-9291-0.

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17

Boriero, Fabrizio, Nicola Sansonetto, Antonio Marigonda, Riccardo Muradore, and Paolo Fiorini. "Optimal Solution of Kinodynamic Motion Planning for the Cart-Pole System." IFAC-PapersOnLine 50, no. 1 (July 2017): 6308–13. http://dx.doi.org/10.1016/j.ifacol.2017.08.895.

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18

Zhou, Xuefeng, Li Jiang, Yisheng Guan, Haifei Zhu, Dan Huang, Taobo Cheng, and Hong Zhang. "Energy-optimal motion planning of a biped pole-climbing robot with kinodynamic constraints." Industrial Robot: An International Journal 45, no. 3 (May 21, 2018): 343–53. http://dx.doi.org/10.1108/ir-11-2017-0200.

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Purpose Applications of robotic systems in agriculture, forestry and high-altitude work will enter a new and huge stage in the near future. For these application fields, climbing robots have attracted much attention and have become one central topic in robotic research. The purpose of this paper is to propose an energy-optimal motion planning method for climbing robots that are applied in an outdoor environment. Design/methodology/approach First, a self-designed climbing robot named Climbot is briefly introduced. Then, an energy-optimal motion planning method is proposed for Climbot with simultaneous consideration of kinematic constraints and dynamic constraints. To decrease computing complexity, an acceleration continuous trajectory planner and a path planner based on spatial continuous curve are designed. Simulation and experimental results indicate that this method can search an energy-optimal path effectively. Findings Climbot can evidently reduce energy consumption when it moves along the energy-optimal path derived by the method used in this paper. Research limitations/implications Only one step climbing motion planning is considered in this method. Practical implications With the proposed motion planning method, climbing robots applied in an outdoor environment can commit more missions with limit power supply. In addition, it is also proved that this motion planning method is effective in a complicated obstacle environment with collision-free constraint. Originality/value The main contribution of this paper is that it establishes a two-planner system to solve the complex motion planning problem with kinodynamic constraints.
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19

Harada, Kensuke, Mitsuharu Morisawa, Shin-ichiro Nakaoka, Kenji Kaneko, and Shuuji Kajita. "Kinodynamic Planning for Humanoid Robots Walking on Uneven Terrain." Journal of Robotics and Mechatronics 21, no. 3 (June 20, 2009): 311–16. http://dx.doi.org/10.20965/jrm.2009.p0311.

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For the purpose of realizing the humanoid robot walking on uneven terrain, this paper proposes the kinodynamic gait planning method where both kinematics and dynamics of the system are considered. We can simultaneously plan both the foot-place and the whole-body motion taking the dynamical balance of the robot into consideration. As a dynamic constraint, we consider the differential equation of the robot's CoG. To solve this constraint, we use a walking pattern generator. We randomly sample the configuration space to search for the path connecting the start and the goal configurations. To show the effectiveness of the proposed methods, we show simulation and experimental results where the humanoid robot HRP-2 walks on rocky cliff with hands contacting the environment.
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20

Chiang, Hao-Tien Lewis, Jasmine Hsu, Marek Fiser, Lydia Tapia, and Aleksandra Faust. "RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators From RL Policies." IEEE Robotics and Automation Letters 4, no. 4 (October 2019): 4298–305. http://dx.doi.org/10.1109/lra.2019.2931199.

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21

Tang, Zhiling, Bowei Chen, Rushi Lan, and Simin Li. "Vector Field Guided RRT* Based on Motion Primitives for Quadrotor Kinodynamic Planning." Journal of Intelligent & Robotic Systems 100, no. 3-4 (July 30, 2020): 1325–39. http://dx.doi.org/10.1007/s10846-020-01231-y.

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22

Li, Linjun, Yinglong Miao, Ahmed H. Qureshi, and Michael C. Yip. "MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning Under Kinodynamic Constraints." IEEE Robotics and Automation Letters 6, no. 3 (July 2021): 4496–503. http://dx.doi.org/10.1109/lra.2021.3067847.

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23

Beschi, Manuel, Stefano Mutti, Giorgio Nicola, Marco Faroni, Paolo Magnoni, Enrico Villagrossi, and Nicola Pedrocchi. "Optimal Robot Motion Planning of Redundant Robots in Machining and Additive Manufacturing Applications." Electronics 8, no. 12 (December 1, 2019): 1437. http://dx.doi.org/10.3390/electronics8121437.

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The paper deals with the generation of optimal trajectories for industrial robots in machining and additive manufacturing applications. The proposed method uses an Ant Colony algorithm to solve a kinodynamic motion planning problem. It exploits the kinematic redundancy that is often present in these applications to optimize the execution of trajectory. At the same time, the robot kinematics and dynamics constraints are respected and robot collisions are avoided. To reduce the computational burden, the task workspace is discretized enabling the use of efficient network solver based on Ant Colony theory. The proposed method is validated in robotic milling and additive manufacturing real-world scenarios.
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24

Elbanhawi, Mohamed, and Milan Simic. "Randomised kinodynamic motion planning for an autonomous vehicle in semi-structured agricultural areas." Biosystems Engineering 126 (October 2014): 30–44. http://dx.doi.org/10.1016/j.biosystemseng.2014.07.010.

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25

Li, Yang, Rongxin Cui, Zhijun Li, and Demin Xu. "Neural Network Approximation Based Near-Optimal Motion Planning With Kinodynamic Constraints Using RRT." IEEE Transactions on Industrial Electronics 65, no. 11 (November 2018): 8718–29. http://dx.doi.org/10.1109/tie.2018.2816000.

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26

REIF, JOHN, and SAM SLEE. "ASYMPTOTICALLY OPTIMAL KINODYNAMIC MOTION PLANNING FOR A CLASS OF MODULAR SELF-RECONFIGURABLE ROBOTS." International Journal of Computational Geometry & Applications 21, no. 02 (April 2011): 131–55. http://dx.doi.org/10.1142/s0218195911003585.

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Self-reconfigurable robots are composed of many individual modules that can autonomously move to transform the shape and structure of the robot. The task of self-reconfiguration, transforming a set of modules from one arrangement to another specified arrangement, is a key problem for these robots and has been heavily studied. However, consideration of this problem has typically been limited to kinematics and so in this work we introduce analysis of dynamics for the problem. We characterize optimal reconfiguration movements in terms of basic laws of physics relating force, mass, acceleration, distance traveled, and movement time. A key property resulting from this is that through the simultaneous application of constant-bounded forces by a system of modules, certain modules in the system can achieve velocities exceeding any constant bounds. This delays some modules in order to accelerate others. To exhibit the significance of simultaneously considering both kinematic and dynamics bounds, we consider the following "x-axis to y-axis" reconfiguration problem. Given a horizontal row of n modules, reconfigure that collection into a vertical column of n modules. The goal is to determine the sequence of movements of the modules that minimizes the movement time needed to achieve the desired reconfiguration of the modules. In this work we prove tight [Formula: see text] upper and lower bounds on the movement time for the above reconfiguration problem. Prior work on reconfiguration problems which focused only on kinematic constraints kept a constant velocity bound on individual modules and so required time linear in n to complete problems of this type.
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27

Motonaka, Kimiko, Keigo Watanabe, and Shoichi Maeyama. "Offline gain optimization in kinodynamic motion planning based on a harmonic potential field." Artificial Life and Robotics 19, no. 1 (December 18, 2013): 47–54. http://dx.doi.org/10.1007/s10015-013-0129-6.

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28

Samaniego, Ricardo, Rodrigo Rodríguez, Fernando Vázquez, and Joaquín López. "Efficient Path Planing for Articulated Vehicles in Cluttered Environments." Sensors 20, no. 23 (November 29, 2020): 6821. http://dx.doi.org/10.3390/s20236821.

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Motion planning and control for articulated logistic vehicles such as tugger trains is a challenging problem in service robotics. The case of tugger trains presents particular difficulties due to the kinematic complexity of these multiarticulated vehicles. Sampling-based motion planners offer a motion planning solution that can take into account the kinematics and dynamics of the vehicle. However, their planning times scale poorly for high dimensional systems, such as these articulated vehicles moving in a big map. To improve the efficiency of the sampling-based motion planners, some approaches combine these methods with discrete search techniques. The goal is to direct the sampling phase with heuristics provided by a faster, precociously ran, discrete search planner. However, sometimes these heuristics can mislead the search towards unfeasible solutions, because the discrete search planners do not take into account the kinematic and dynamic restrictions of the vehicle. In this paper we present a solution adapted for articulated logistic vehicles that uses a kinodynamic discrete planning to bias the sampling-based algorithm. The whole system has been applied in two different towing tractors (a tricycle and a quadricycle) with two different trailers (simple trailer and synchronized shaft trailer).
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Zhang, Zhen, Jiaqing Yan, Xin Kong, Guangyao Zhai, and Yong Liu. "Efficient Motion Planning Based on Kinodynamic Model for Quadruped Robots Following Persons in Confined Spaces." IEEE/ASME Transactions on Mechatronics 26, no. 4 (August 2021): 1997–2006. http://dx.doi.org/10.1109/tmech.2021.3083594.

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30

Mahmoodi, Mostafa, Khalil Alipour, and Hadi Beik Mohammadi. "KidVO: a kinodynamically consistent algorithm for online motion planning in dynamic environments." Industrial Robot: An International Journal 43, no. 1 (January 18, 2016): 33–47. http://dx.doi.org/10.1108/ir-05-2015-0096.

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Purpose – The purpose of this paper is to propose an efficient method, called kinodynamic velocity obstacle (KidVO), for motion planning of omnimobile robots considering kinematic and dynamic constraints (KDCs). Design/methodology/approach – The suggested method improves generalized velocity obstacle (GVO) approach by a systematic selection of proper time horizon. Selection procedure of the time horizon is based on kinematical and dynamical restrictions of the robot. Toward this aim, an omnimobile robot with a general geometry is taken into account, and the admissible velocity and acceleration cones reflecting KDCs are derived, respectively. To prove the advantages of the suggested planning method, its performance is compared with GVOs, the so-called Hamilton-Jacobi-Bellman equation and the rapidly exploring random tree. Findings – The obtained results of the presented scenarios which contain both computer and real-world experiments for complicated crowded environments indicate the merits of the suggested methodology in terms of its near-optimal behavior, successful obstacle avoidance both in static and dynamic environments and reaching to the goal pose. Originality/value – This paper proposes a novel method for online motion planning of omnimobile robots in dynamic environments while considering the real capabilities of the robot.
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31

Aine, Sandip, and P. B. Sujit. "Integrating Planning and Control for Efficient Path Planning in the Presence of Environmental Disturbances." Proceedings of the International Conference on Automated Planning and Scheduling 26 (March 30, 2016): 441–49. http://dx.doi.org/10.1609/icaps.v26i1.13797.

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Path planning for nonholonomic robots in real-life environments is a challenging problem, as the planner needs to consider the presence of obstacles, the kinematic constraints, and also the environmental disturbances (like wind and currents). In this paper, we develop a path planning algorithm called Control Based A* (CBA*), which integrates search-based planning (on grid) with a path-following controller, taking the motion constraints and external disturbances into account. We also present another algorithm called Dynamic Control Based A* (DCBA*), which improves upon CBA* by allowing the search to look beyond the immediate grid neighborhood and thus makes it more flexible and robust, especially with high resolution grids. We investigate the performance of the new planners in different environments under different wind disturbance conditions and compare the performance against (i) finding a path in the discretized grid and following it with a nonholonomic robot, and (ii) a kinodynamic sampling-based path planner. The results show that our planners perform considerably better than (i) and (ii), especially in difficult situations such as in cluttered spaces or in presence of strong winds/currents. Further, we experimentally validate the approach using a quadrotor in the outdoor environment.
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32

Kontoudis, George P., and Kyriakos G. Vamvoudakis. "Kinodynamic Motion Planning With Continuous-Time Q-Learning: An Online, Model-Free, and Safe Navigation Framework." IEEE Transactions on Neural Networks and Learning Systems 30, no. 12 (December 2019): 3803–17. http://dx.doi.org/10.1109/tnnls.2019.2899311.

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Hu, Biao, Zhengcai Cao, and MengChu Zhou. "An Efficient RRT-Based Framework for Planning Short and Smooth Wheeled Robot Motion Under Kinodynamic Constraints." IEEE Transactions on Industrial Electronics 68, no. 4 (April 2021): 3292–302. http://dx.doi.org/10.1109/tie.2020.2978701.

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34

Dutta, Praneet, Rashmi Ranjan Das, Rupali Mathur, and Deepika Rani Sona. "OPP approach for multi degree of freedom robotic arm Based on Kinematics and Dynamics of Robot." IAES International Journal of Robotics and Automation (IJRA) 4, no. 4 (December 1, 2015): 284. http://dx.doi.org/10.11591/ijra.v4i4.pp284-291.

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This paper deals with the trajectory and path generation of the industrial manipulator. The trajectory is obtained using the equations of motion and also the optimal path planning (OPP) approach under kinodynamic constraints. The optimal control problem is defined for the minimum cost function and to obtain the necessary conditions. Here we have used pontrygain’s minimum principle to obtain the limiting value of joint angle and also the joint velocity and torque. In this paper we have used the “Two degree of freedom (DOF) manipulator” for analysis and designing the optimal control for multi link and multi degree of freedom manipulator. For analysis purposes, simulation software has been used to formulate the trajectory and minimize the cost function involved.
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Liu, Hongyan, Daokui Qu, Fang Xu, Zhenjun Du, Kai Jia, and Mingmin Liu. "An Efficient Online Trajectory Generation Method Based on Kinodynamic Path Search and Trajectory Optimization for Human-Robot Interaction Safety." Entropy 24, no. 5 (May 6, 2022): 653. http://dx.doi.org/10.3390/e24050653.

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With the rapid development of robot perception and planning technology, robots are gradually getting rid of fixed fences and working closely with humans in shared workspaces. The safety of human-robot coexistence has become critical. Traditional motion planning methods perform poorly in dynamic environments where obstacles motion is highly uncertain. In this paper, we propose an efficient online trajectory generation method to help manipulator autonomous planning in dynamic environments. Our approach starts with an efficient kinodynamic path search algorithm that considers the links constraints and finds a safe and feasible initial trajectory with minimal control effort and time. To increase the clearance between the trajectory and obstacles and improve the smoothness, a trajectory optimization method using the B-spline convex hull property is adopted to minimize the penalty of collision cost, smoothness, and dynamical feasibility. To avoid the collisions between the links and obstacles and the collisions of the links themselves, a constraint-relaxed links collision avoidance method is developed by solving a quadratic programming problem. Compared with the existing state-of-the-art planning method for dynamic environments and advanced trajectory optimization method, our method can generate a smoother, collision-free trajectory in less time with a higher success rate. Detailed simulation comparison experiments, as well as real-world experiments, are reported to verify the effectiveness of our method.
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Qu, Yue, and Wenjun Yi. "Three-Dimensional Obstacle Avoidance Strategy for Fixed-Wing UAVs Based on Quaternion Method." Applied Sciences 12, no. 3 (January 18, 2022): 955. http://dx.doi.org/10.3390/app12030955.

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This work provides a generalization of the three-dimensional velocity obstacle (VO) collision avoidance strategy for nonlinear second-order underactuated systems in three-dimensional dynamic uncertain environments. A hierarchical architecture is exploited to deal with conflicting multiple subtasks, which are defined as several rotations and are parameterized by quaternions. An improved VO method considering the kinodynamic constraints of a class of fixed-wing unmanned aerial vehicles (UAV) is proposed to implement the motion planning. The position error and velocity error can be mapped onto one desired axis so that, only relying on an engine, UAVs can achieve the goal of point tracking without collision. Additionally, the performance of the closed-loop system is demonstrated through a series of simulations performed in a three-dimensional manner.
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37

Shkolnik, Alexander, Michael Levashov, Ian R. Manchester, and Russ Tedrake. "Bounding on rough terrain with the LittleDog robot." International Journal of Robotics Research 30, no. 2 (December 7, 2010): 192–215. http://dx.doi.org/10.1177/0278364910388315.

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A motion planning algorithm is described for bounding over rough terrain with the LittleDog robot. Unlike walking gaits, bounding is highly dynamic and cannot be planned with quasi-steady approximations. LittleDog is modeled as a planar five-link system, with a 16-dimensional state space; computing a plan over rough terrain in this high-dimensional state space that respects the kinodynamic constraints due to underactuation and motor limits is extremely challenging. Rapidly Exploring Random Trees (RRTs) are known for fast kinematic path planning in high-dimensional configuration spaces in the presence of obstacles, but search efficiency degrades rapidly with the addition of challenging dynamics. A computationally tractable planner for bounding was developed by modifying the RRT algorithm by using: (1) motion primitives to reduce the dimensionality of the problem; (2) Reachability Guidance, which dynamically changes the sampling distribution and distance metric to address differential constraints and discontinuous motion primitive dynamics; and (3) sampling with a Voronoi bias in a lower-dimensional “task space” for bounding. Short trajectories were demonstrated to work on the robot, however open-loop bounding is inherently unstable. A feedback controller based on transverse linearization was implemented, and shown in simulation to stabilize perturbations in the presence of noise and time delays.
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38

Sharma, Bibhya, Jito Vanualailai, and Avinesh Prasad. "A dϕ-Strategy: Facilitating Dual-Formation Control of a Virtually Connected Team." Journal of Advanced Transportation 2017 (2017): 1–17. http://dx.doi.org/10.1155/2017/9213805.

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This paper describes the design of new centralized acceleration-based controllers for the multitask problem of motion planning and control of a coordinated lead-carrier team fixed in a dual-formation within an obstacle-ridden environment. A dϕ-strategy, where d and ϕ are Euclidean measures with respect to the lead robot, is developed to ensure virtual connectivity of the carrier robots to the lead robot. This connectivity, built into the system itself, inherently ensures globally rigid formation between each lead-carrier pair of the team. Moreover, a combination of target configuration, dϕ-strategy, orientation consensus, and avoidance of end-effector of robots results in a second, locally rigid formation (not infinitesimally rigid). Therefore, for the first time, a dual-formation control problem of a lead-carrier team of mobile manipulators is considered. This and other kinodynamic constraints have been treated simultaneously via the overarching Lyapunov-based control scheme, essentially a potential field method favored in the field of robotics. The formulation of this new scheme, demonstrated effectively via computer simulations, is timely, given that the current proposed engineering solutions, allowing autonomous vehicles on public roads, include the development of special lanes imbued with special sensors and wireless technologies.
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39

Liu, Guoli, and Yadong Liang. "Research on attack and defence control of martial arts arena robot based on kinodynamics." International Journal of Advanced Robotic Systems 17, no. 2 (March 1, 2020): 172988142091032. http://dx.doi.org/10.1177/1729881420910322.

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In the research of intelligent mobile robot, autonomous mobility is a very key problem. It is very important to accurately and quickly respond to the surrounding environment, avoid obstacles in the process of moving in real time and move to the destination without interruption. Most of the existing path planning and obstacle avoidance algorithms do not consider the kinematic and geometric constraints of the robot parameters, there will be physical feasibility and versatility problems in the actual robot application; the existing martial arts challenge arena robot attack and defence control strategy, mainly rely on experience and try, accuracy and flexibility and other aspects are inadequate. In view of this, this article proposes a dynamic model based on virtual force, and applies it to the martial arts challenge arena robot, develops a set of attack and defence control strategy of martial arts challenge arena robot, completes the simulation experiment and the actual robot experiment, analyse the experimental results, and puts forward the improvement scheme. The experimental results show that the motion model proposed in this article can help the robot to complete the obstacle avoidance planning task, and the attack and defence control model based on the motion modelling can effectively improve the obstacle avoidance efficiency and attack and defence intensity of the martial arts challenge arena robot, and enhance the competitiveness of its confrontation.
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40

Tang, Yongxing, Zhanxia Zhu, and Hongwen Zhang. "A Reachability-Based Spatio-Temporal Sampling Strategy for Kinodynamic Motion Planning." IEEE Robotics and Automation Letters, 2022, 1–8. http://dx.doi.org/10.1109/lra.2022.3226032.

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41

Verginis, Christos K., Dimos V. Dimarogonas, and Lydia E. Kavraki. "KDF: Kinodynamic Motion Planning via Geometric Sampling-Based Algorithms and Funnel Control." IEEE Transactions on Robotics, 2022, 1–20. http://dx.doi.org/10.1109/tro.2022.3208502.

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42

Zhang, Xinglong, Yan Jiang, Yang Lu, and Xin Xu. "A Receding-Horizon Reinforcement Learning Approach for Kinodynamic Motion Planning of Autonomous Vehicles." IEEE Transactions on Intelligent Vehicles, 2022, 1. http://dx.doi.org/10.1109/tiv.2022.3167271.

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43

Primatesta, Stefano, Abdalla Osman, and Alessandro Rizzo. "MP-RRT#: a Model Predictive Sampling-based Motion Planning Algorithm for Unmanned Aircraft Systems." Journal of Intelligent & Robotic Systems 103, no. 4 (November 9, 2021). http://dx.doi.org/10.1007/s10846-021-01501-3.

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AbstractThis paper introduces a kinodynamic motion planning algorithm for Unmanned Aircraft Systems (UAS), called MP-RRT#. MP-RRT# joins the potentialities of RRT# with a strategy based on Model Predictive Control to efficiently solve motion planning problems under differential constraints. Similar to other RRT-based algorithms, MP-RRT# explores the map constructing an asymptotically optimal graph. In each iteration the graph is extended with a new vertex in the reference state of the UAS. Then, a forward simulation is performed using a Model Predictive Control strategy to evaluate the motion between two adjacent vertices, and a trajectory in the state space is computed. As a result, the MP-RRT# algorithm eventually generates a feasible trajectory for the UAS satisfying dynamic constraints. Simulation results obtained with a simulated drone controlled with the PX4 autopilot corroborate the validity of the MP-RRT# approach.
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