Yang T, Jiang Z, Sun R et al (2020) Maritime search and rescue based on group mobile computing for unmanned aerial vehicles and unmanned surface vehicles. Proc Inst Mechan Eng Part D J Automob Eng 235(4):1113–1127 Wang H, Yuan S, Guo M et al (2021) Tactical driving decisions of unmanned ground vehicles in complex highway environments: a deep reinforcement learning approach. Wang Y, Goila A, Shetty R et al (2017) Obstacle avoidance strategy and implementation for unmanned ground vehicle using LIDAR. Song W, Zou S, Tian Y et al (2018) A CPU-GPU hybrid system of environment perception and 3D terrain reconstruction for unmanned ground vehicle. Sheng P, Ma J, Wang D et al (2019) Intelligent trajectory planning model for electric vehicle in unknown environment. Sebastian B, Ben-Tzvi P (2019) Physics based path planning for autonomous tracked vehicle in challenging terrain. Ren H, Chen S, Yang L et al (2020) Optimal path planning and speed control integration strategy for UGVs in static and dynamic environments. Na Z, Pan YH (2020) A Research on the classification of intelligence level of unmanned grain harvester. Mohamed A, El-Gindy M, Ren J (2018) Advanced control techniques for unmanned ground vehicle: literature survey. Lu H, Liu Q, Tian D et al (2019) The cognitive internet of vehicles for autonomous driving. Liu S, Wu Y, Yimu JI et al (2019) Research on security of key algorithms in intelligent driving system. Liu Y, Cui D (2019b) Path tracking control for inverse vehicle handling dynamics. Liu Y, Cui D (2019a) Application of optimal control method to path tracking problem of vehicle. Li J, Bao H, Han X et al (2017) Real-time self-driving car navigation and obstacle avoidance using mobile 3D laser scanner and GNSS. Li D, Gao H (2018) A hardware platform framework for an intelligent vehicle based on a driving brain. Proc Inst Mech Eng Part I J Syst Control Eng 235(2):222–236 Hang P, Huang S, Chen X et al (2021) Path planning of collision avoidance for unmanned ground vehicles: a nonlinear model predictive control approach. Hamid UZA, Saito Y, Zamzuri H et al (2018) A review on threat assessment, path planning and path tracking strategies for collision avoidance systems of autonomous vehicles. Gao H, Yu H, Xie G et al (2018) Hardware and software architecture of intelligent vehicles and road verification in typical traffic scenarios. IEEE Trans Intell Veh 4(3):425–436ĭong Y, Zhang Y, Ai J (2017) Experimental test of unmanned ground vehicle delivering goods using RRT path planning algorithm. IEEE Trans Veh Technol 68(8):8183–8190Ĭhen S, Chen Y, Zhang S et al (2019b) A novel integrated simulation and testing platform for self-driving cars with hardware in the loop. The experimental research results show that the obstacle avoidance trajectory planning method of redundant robots based on improved Bi-RRT proposed in this paper has good results and can effectively improve the efficiency of redundant robot trajectory planning.Ĭhen G, Chen S, Langari R et al (2019a) Driver-behavior-based adaptive steering robust nonlinear control of unmanned driving robotic vehicle with modeling uncertainties and disturbance observer. Finally, this paper verifies the effect through simulation experiments. In addition, this paper combines the obstacle avoidance requirements and path planning requirements of redundant robots to construct an intelligent model to realize automatic obstacle avoidance trajectory planning. Moreover, this paper studies the path planning algorithm based on RRT, and applies the Bi-RRT algorithm to perform path planning. In order to improve the effectiveness of the avoidance trajectory planning method of redundant robot, based on the simplification of the redundant seven-degree-of-freedom structure, this paper conducts kinematics and dynamics modeling of the manipulator, and analyzes the inverse kinematics of the seven-degree-of-freedom manipulator.
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