A Global Path Planning Algorithm for Robots Using Reinforcement Learning Path planning is the key technology for autonomous mobile robots. In view of the shortage of paths found by traditional best first search (BFS) and rapidlyexploring random trees (RRT) algorithm which are not short and smooth enough for robot navigation, a new global planning algorithm combined with reinforcement learning is presented for robots. In our algorithm, a path graph is established firstly, in which the paths collided with the obstacles are removed directly. Then a collisionfree path will be found by QLearning from starting point to the goal. The experiment results illustrate that it can generate shorter and smoother paths, compared with the BFS and RRT algorithm.

Locally Guided Multiple BiRRTxe2x88x97 for Fast Path Planning in Narrow Passages Rapidlyexploring Random Tree Star (RRTxe2x88x97) and its variants can provide a collisionfree and asymptotic optimal solution for many path planning problems. However, it is inefficient for many RRTxe2x88x97 based variants to rapidly find one initial solution in a clustered environment with narrow passages, since consuming high memory as well as time, due to a large number of iterations in sampling critical nodes. To overcome this problem, the paper proposes the Locally Guided Multiple BiRRTxe2x88x97 (LGMBRRTxe2x88x97) method, which can provide a fast solution by incorporating an improved bridgetest and a novel search strategy based on local guidance. It ensures an accelerated success rate and more efficient memory utilization compared with Bidirectional RRTxe2x88x97(BRRTxe2x88x97), and it is easy for implementation as well. It was verified on different types of scenarios in terms of efficiency and success rate. The results demonstrated that LGMBRRTxe2x88x97 is beneficial for fast path planning in a clustered environment with narrow passages.