Abstract
The home environment is a typical dynamic environment with moveable obstacles. The social robots working in home need to search for feasible paths in this complex dynamic environment. In this work, we propose an improved RRT algorithm to plan feasible path in home environment. The algorithm pre-builds a tree that covers the whole map and maintains the effectiveness of all nodes with branch pruning, reconnection, and regrowth process. The method forms a path by searching the nearest node in the tree and then quickly accessing the nodes near the destination. Due to the effectiveness-maintaining process, the proposed method can effectively deal with the complex dynamic environment where the destination and multiple moving obstacles change simultaneously. In addition, our method can be extended to the path-planning problem in 3D space. The simulation experiments verify the effectiveness of the algorithm.
Highlights
The co-existence of human and robot will become the main pattern of home environment in the future [1]
We propose an improved rapidly-exploring random tree (RRT) algorithm to plan a feasible path in home environment
The proposed method is much more efficient than the extended RRT (ERRT) and SRRT to generate a feasible path in complex environments
Summary
The co-existence of human and robot will become the main pattern of home environment in the future [1]. Running in the same space with human, the robots are not allowed to collide with human and not expected to interfere with human movement [2]. The capacity of motion path planning is pivotal for a robot to run in the home environment. The purpose of the motion path planning is to find a feasible path from the robot’s current position to the target position without collision with obstacles. The robot will build a complete map through SLAM [3,4], including the shape of the room, the position of the furniture, and the walkable area, which are relatively fixed
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