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| Improved Path Planning Based on Rapidly-Exploring Random Tree for Mobile Robot in Unknown Environment |
| KANG Liang, ZHAO Chun-Xia, GUO Jian-Hui |
| College of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094 |
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Abstract An improved path planning algorithm is proposed by combining rapidly-exploring random tree (RRT) and rolling path planning. In this algorithm, the real-time local environment information detected by the robot is fully used and the on-line planning is performed in a rolling style. Therefore, the RRT algorithm can be used in both known and unknown environment. Only the local environmental map is calculated in the planning to improve the planning efficiency, and thus the planning in real time is guaranteed. The calculation of analytical expressions of the obstacle can be ignored. Hence, the memory is saved greatly. Based on the algorithm of rapidly-exploring random, the heuristic evaluation function is introduced into the improved algorithm, so that the exploring random tree can grow in the direction of target point. The regression analysis, which avoids local minimum, enhances the capability of searching unknown space. The simulation results verify the effectiveness of the improved algorithm.
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Received: 14 July 2008
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2009, Vol. 22 
