Abstract：Against at the problems of oscillation and poor adaptability of robot motion state in the path planning of traditional unstructured environment, a new path planning strategy based on Glowworm Swarm Optimization algorithm of Scene understanding (SGSO) was proposed. The initialization was realized based on the regularity, randomness and generalization of chaotic systems, and golden section method was used for later optimization, which improved the diversity of the population, suppressed the premature and local convergence of the algorithm. And, by introducing scene understanding of glowworm "natural enemy", the selection mechanism of glowworm swarm was optimized to solve the grounding phenomenon of glowworm in the process of tracing under unstructured environment, which enhanced the adaptability and robustness of the algorithm. The simulation results of four test functions show that, the proposed algorithm is superior to the basic Glowworm Swarm Optimization (GSO) algorithm in solving precision and convergence efficiency. The proposed algorithm was applied to the path planning of mobile robots in unstructured environment, the test results show that the planning path based on SGSO was shorter and the corner was more smooth, which could effectively avoid the additional load on power system caused by large angle steering of robot, verifying the feasibility and effectiveness of the proposed algorithm.
罗天洪, 梁爽, 何泽银, 张霞. 基于情景萤火虫算法的机器人路径规划[J]. 计算机应用, 2017, 37(12): 3608-3613.
LUO Tianhong, LIANG Shuang, HE Zeyin, ZHANG Xia. Path planning of robot based on glowworm swarm optimization algorithm of scene understanding. Journal of Computer Applications, 2017, 37(12): 3608-3613.
[1] LI C, JIANG X, WANG W, et al. A simplified car-following model based on the artificial potential field[J]. Procedia Engineering, 2016, 137:13-20. [2] KRISHNANAND K N, GHOSE D. A glowworm swarm optimization based multi-robot system for signal source localization[M]//Design and Control of Intelligent Robotic Systems. Berlin:Springer, 2009:49-68. [3] 郁书好,苏守宝.混沌萤火虫优化算法的研究及应用[J].计算机科学与探索,2014,8(3):352-358.(YU S H, SU S B. Research and application of chaotic glowworm swarm optimization algorithm[J]. Journal of Frontiers of Computer Science and Technology,2014, 8(3):352-358.) [4] LIAO W H, KAO Y C, LI Y S. A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks[J]. Expert Systems with Applications, 2011, 38(10):12180-12188. [5] MARINAKI M, MARINAKIS Y. Glowworm swarm optimization algorithm for the vehicle routing problem with stochastic demands[J]. Expert Systems With Applications, 2016, 46(C):145-163. [6] ZHANG H, LIU Y B, ZHAO J, et al. Development of a bionic hexapod robot for walking on unstructured zerrain[J]. Journal of Bionic Engineering, 2014, 11(2):176-187. [7] 于乃功,郑宇凌,徐丽,等.基于光流的非结构化环境中移动机器人避障方法[J].北京工业大学学报,2017,43(1):65-69.(YU N G, ZHENG Y L, XU L, et al. Optical flow based mobile robot obstacle avoidance method in unstructured environment[J]. Journal of Beijing Universityof Technology, 2017, 43(1):65-69.) [8] 刘佳,梁秋丽,王书青,等.基于模拟退火算法的萤火虫群优化算法研究[J].计算机仿真,2014,31(5):284-288.(LIU J, LIANG Q L, WANG S Q, et al. Research on improved glowworm swarm optimization algorithm based on simulated annealing algorithm[J]. Computer Simulation, 2014, 31(5):284-288.) [9] ORAMUS P. Improvements to glowworm swarm optimization algorithm[J]. Computer Science, 2010, 11:7-20. [10] 罗天洪,陈才,李富盈.基于时变萤火虫群算法的冗余机器人手臂逆解[J].计算机集成制造系统,2016,22(2):576-582.(LUO T H, CHEN C, LI F Y. Inverse solution of redundant robot arm based on glow-worm swarm optimization algorithm of time-varying[J]. Computer Integrated Manufacturing Systems, 2016, 22(2):576-582.) [11] DING S, AN Y, ZHANG X, et al. Wavelet twin support vector machines based on glowworm swarm optimization[J]. Neurocomputing, 2017, 225(C):157-163. [12] CUI H, FENG J, GUO J, et al. A novel single multiplicative neuron model trained by an improved glowworm swarm optimization algorithm for time series prediction[J]. Knowledge-Based Systems, 2015, 88(C):195-209. [13] 孙波,陈卫东,席裕庚.基于粒子群优化算法的移动机器人全局路径规划[J].控制与决策,2005,20(9):1052-1055,1060.(SUN B, CHEN W D, XI Y G. Particle swarm optimization based global path planning for mobile robots[J]. Control and Decision, 2005, 20(9):1052-1055, 1060.)
2017, Vol. 37 
