Abstract In order to solve the path planning problem of mobile robot in the dynamic environment, a hybird strategy based on the global and local algorithms is proposed by combining the Informed RRT* and artificial potential field method. Firstly, the target offset method and adaptive step size method are designed in the process of informed RRT* random point selection to reduce the redundant search and unnecessary tree growth. Corridor optimization and time redistribution method are introduced to optimize the nodes of path for making the path more smooth.Secondly, the artificial potential field method based on subtarget points is employed for the local path planning, and the problem of easy to fall into local minimum and unreachable near the target point for the artificial potential field method is solved. The sub target point is set to make sure that the mobile robot can escape the local minimum and complete the mission. The simulation results show that the time to find a path in a static environment was 71.98% shorter by using adaptive step-length Informed-RRT* algorithm compared to the Informed-RRT* algorithm. Compared to the artificial potential field method,the search time and the path length are decreased 15.4% and 11.1%, respectively, by using the bybird algorithm.
Lavalle S M. Rapidly-Exploring Random Trees: A New Tool for Path Planning［R］. Iowa State Unixersity,1998.
Zhao C F, Li J H, Zhang D W. Robot Path Optimization Research Based on Improved Lmmune Genetic Optimization Ant Colony Algorithm［J］. Acta Metrologica Sinica, 2019, 40 (3):505-510.
Yang J C, Li S X, Cai Z Y. Research and Development of Path Planning Algorithm［J］. Control Engineering of China, 2017, 24 (7):1473-1480.
Jiang K, Ma S J. A Cable Path Planning Method Based on Improved A* Algorithm［J］. China Mechanical Engineering, 2019, 30 (6):699-708.
Li J H, Yan Y N, Peng D. Path Planning and Formation Research of Micro Mobile Robots［J］. Acta Metrologica Sinica, 2021, 42 (4):445-450.
［7］
Karaman S, Frazzoli E. Sampling-Based Algorithms for Optimal Motion Planning［J］. The International Journal of Robotics Research, 2011, 30 (7):846-894.
［8］
Gammell J D, Srinivass S, Barfoot T D. Informed RRT: Optimal Sampling-Based Path Planning Focused Via Direct Sampling of an Admissible Ellipsoidal Heuristic［C］//IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago: IEEE, 2014:2997-3004.
［10］
Hu B, Cao Z, Zhou M. An Efficient RRT-Based Framework for Planning Short and Smooth Wheeled Robot Motion Under Kinodynamic Constraints［J］. IEEE Transactions on Industrial Electronics, 2020, 68 (4):3292-3302.
［12］
Betono T F, Zulueta E, Fernandez G, et al. Predictive Dynamic Window Approach Development With Artificial Neural Fuzzy Inference Improvement［J］. IEEE Transactions on Industrial Electronics, 2019, 9 (8):935-954.
Zang Y, Song J Z, Zhang Y Q. Local Path Planning of Outdoor Cleaning Robot Based on an Improved DWA［J］. Robot, 2020, 42 (1):617-625.
［15］
Wang J, Wu Z X, Yan S Z, et al. Real-Time Path Planning and Following of a Gliding Robotic Dolphin Within a Hierarchical Framework［J］. IEEE Transactions on Vehicular Technology, 2021, 70 (4):3243-3255.
［16］
Yadollah R, Amir K, Shih-Ken C, et al. A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles［J］. IEEE Transactions on Intelligent Transportation Systems, 2017, 18 (5):1255-1267.
Yuan W, Li Z J. Brain Teleoperation Control of a Nonholonomic Mobile Robot Using Quadrupole Potential Function［J］. IEEE Transactions on Cognitive and Developmental Systems, 2019, 11 (4):527-538.
Wan F, Zhou F Y, Yin L, et al. Global Path Planning Algorithm of Mobile Robot Based on Electric Potential Field［J］. Robot, 2019, 41 (6):742-750.
［21］
Malone N, Chiang H T, Lesser K, et al. Hybrid Dynamic Moving Obstacle Avoidance Using a Stochastic Reachable Set-Based Potential Field［J］. IEEE Transactions on Robotics, 2017, 33 (5):1124-1138.
［23］
Bakdi A, Abdelfetah H, Boutamai H, et al. Optimal Path Planning and Execution for Mobile Robots Using Genetic Algorithm and Adaptive Fuzzy-Logic Control［J］. Robotics and Autonomous Systems, 2017, 89 (1):95-109.
［24］
Qureshi A H, Ayaz Y. Potential Functions Based Sampling Heuristic for Optimal Path Planning［J］. Autonomous Robots, 2016, 40 (6): 1079-1093.
Zou S J, Liu S H, Zhang Y, et al. Rapidly-Exploring Random Tree Algorithm for Path Re-Planning Based on Reinforcement Learning Under the Peculiar Environment［J］. Control Theory & Applications, 2020, 37 (8):1737-1748.
［27］
Gammell J D, Barfoot T D, Srinivasa S S. Informed Sampling for Asymptotically Optimal Path Planning［J］. IEEE Transactions on Robotics,2018, 34 (4):966-984.
Tan J H, Pan B, Wang Y N. Robot Path Planning Based on Improved RRT*FN Algorithm［J］. Control and Decision, 2021, 36 (8):1834-1840.
［9］
Salzman O, Dan H. Asymptotically Near-Optimal RRT for Fast, High-Quality, Motion Planning［J］. IEEE Transactions on Robotics, 2016, 32 (3):473-483.
［11］
Qi J, Yang H, Sun H. MOD-RRT′: A Sampling-Based Algorithm for Robot Path Planning in Dynamic Environment［J］. IEEE Transactions on Industrial Electronics, 2020, 68 (8):7244-7251.
Yang X, Wang R, Zhang T. Review of Unmanned Aerial Vehicle Swarm Path Planning Based on Intelligent Optimization［J］. Control Theory & Applications, 2020, 37 (11):2291-2302.
［17］
Zappulla R, Hyeongjun P, Virgili-Llop J. et al. Real-Time Autonomous Spacecraft Proximity Maneuvers and Docking Using an Adaptive Artificial Potential Field Approach［J］. IEEE Transactions on Control Systems Technology, 2018, 27 (6):2598-2605.
［19］
Liao Y, Jia Z, Zhang W, et al. Layered Berthing Method and Experiment of Unmanned Surface Vehicle Based on Multiple Constraints Analysis［J］. Applied Ocean Research, 2019, 86:47-60.
Wu Z F, Wu Z Y. Optimization of AUV Path Based on GPS/MATLAB［J］. Acta Metrologica Sinica, 2020, 41 (8):926-931.
Cao X, Zou X, Jia C, et al. RRT-based Path Planning for an Intelligent Litchi-Picking Manipulator［J］. Computers and electronics in agriculture, 2019,156:105-118.
2023, Vol. 44 
