TY - GEN
T1 - Improved neural network 3D space obstacle avoidance algorithm for mobile robot
AU - Tong, Yuchuang
AU - Liu, Jinguo
AU - Liu, Yuwang
AU - Ju, Zhaojie
PY - 2019/8/3
Y1 - 2019/8/3
N2 - Path planning problems are classical optimization problems in many fields, such as computers, mathematics, transportation, robots, etc., which can be described as an optimization problem in mathematics. In this paper, the mathematical model of obstacle environment is established. The characteristics of neural network algorithm, simulated annealing algorithm and adaptive variable stepsize via linear reinforcement are studied respectively. A new neural network 3D space obstacle avoidance algorithm for mobile robot is proposed, which solves the problem of the computational duration and minimum distance of the traditional neural network obstacle avoidance algorithm in solving the optimal path. According to the characteristics of the improved neural network algorithm, it is fused with a variety of algorithms to obtain the optimal path algorithm that achieves the shortest path distance and meets the requirements of obstacle avoidance security. The simulation experiment of the algorithm is simulated by Matlab. The results show that the improved neural network spatial obstacle avoidance algorithm based on the multiple algorithms proposed in this paper can effectively accelerate the convergence speed of path planning, realize the minimum path distance, and achieve very good path planning effect.
AB - Path planning problems are classical optimization problems in many fields, such as computers, mathematics, transportation, robots, etc., which can be described as an optimization problem in mathematics. In this paper, the mathematical model of obstacle environment is established. The characteristics of neural network algorithm, simulated annealing algorithm and adaptive variable stepsize via linear reinforcement are studied respectively. A new neural network 3D space obstacle avoidance algorithm for mobile robot is proposed, which solves the problem of the computational duration and minimum distance of the traditional neural network obstacle avoidance algorithm in solving the optimal path. According to the characteristics of the improved neural network algorithm, it is fused with a variety of algorithms to obtain the optimal path algorithm that achieves the shortest path distance and meets the requirements of obstacle avoidance security. The simulation experiment of the algorithm is simulated by Matlab. The results show that the improved neural network spatial obstacle avoidance algorithm based on the multiple algorithms proposed in this paper can effectively accelerate the convergence speed of path planning, realize the minimum path distance, and achieve very good path planning effect.
KW - global path planning
KW - obstacle avoidance algrithm
KW - improved neural network algorithm
KW - adaptive variable stepsixe
KW - simulated annealing
U2 - 10.1007/978-3-030-27538-9_10
DO - 10.1007/978-3-030-27538-9_10
M3 - Conference contribution
SN - 978-3-030-27537-2
T3 - Lecture Notes in Computer Science
SP - 105
EP - 117
BT - Intelligent Robotics and Applications
A2 - Yu, Haibin
A2 - Liu, Jinguo
A2 - Liu, Lianqing
A2 - Ju, Zhaojie
A2 - Liu, Yuwang
A2 - Zhou, Dalin
PB - Springer
ER -