The Rapidly-exploring Random Tree(RRT)algorithm is an efficient path-planning algorithm based on random sampling.The RRT^(*)algorithm is a variant of the RRT algorithm that can achieve convergence to the optimal solut...The Rapidly-exploring Random Tree(RRT)algorithm is an efficient path-planning algorithm based on random sampling.The RRT^(*)algorithm is a variant of the RRT algorithm that can achieve convergence to the optimal solution.However,it has been proven to take an infinite time to do so.An improved Quick-RRT^(*)(Q-RRT^(*))algorithm based on a virtual light source is proposed in this paper to overcome this problem.The virtual light-based Q-RRT^(*)(LQRRT^(*))takes advantage of the heuristic information generated by the virtual light on the map.In this way,the tree can find the initial solution quickly.Next,the LQRRT^(*)algorithm combines the heuristic information with the optimization capability of the Q-RRT^(*)algorithm to find the approximate optimal solution.LQRRT^(*)further optimizes the sampling space compared with the Q-RRT^(*)algorithm and improves the sampling efficiency.The efficiency of the algorithm is verified by comparison experiments in different simulation environments.The results show that the proposed algorithm can converge to the approximate optimal solution in less time and with lower memory consumption.展开更多
基金This work was supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China[grant number 19KJB510022]the Startup Research Foundation for Advanced Talents[grant number JSPIGKZ/2911119220].
文摘The Rapidly-exploring Random Tree(RRT)algorithm is an efficient path-planning algorithm based on random sampling.The RRT^(*)algorithm is a variant of the RRT algorithm that can achieve convergence to the optimal solution.However,it has been proven to take an infinite time to do so.An improved Quick-RRT^(*)(Q-RRT^(*))algorithm based on a virtual light source is proposed in this paper to overcome this problem.The virtual light-based Q-RRT^(*)(LQRRT^(*))takes advantage of the heuristic information generated by the virtual light on the map.In this way,the tree can find the initial solution quickly.Next,the LQRRT^(*)algorithm combines the heuristic information with the optimization capability of the Q-RRT^(*)algorithm to find the approximate optimal solution.LQRRT^(*)further optimizes the sampling space compared with the Q-RRT^(*)algorithm and improves the sampling efficiency.The efficiency of the algorithm is verified by comparison experiments in different simulation environments.The results show that the proposed algorithm can converge to the approximate optimal solution in less time and with lower memory consumption.
