Path planning in changing environments by using optimal path segment search

This paper presents a novel planner for manipulators and robots in changing environments. When environments are complicated, it's always difficult to find a completely valid path solution, which is essential for many methods. However, our planner searches for several path segments to make robot move towards its goal as much as possible even though such a complete solution doesn't exist currently. In the learning phase, the planner begins by building a roadmap that captures the topological structure of the configuration space in a workspace without obstacles. In the query phase, the planner searches for a solution path in the roadmap with the A* algorithm and performs roadmap updating using the lazy evaluation idea concurrently with the solution search process. If a completely valid solution is found, it will be adopted immediately. Otherwise the planner will collect a set of maximum valid path segments and then select the optimal one for planning in the execution process. The searching and execution process will be repeatedly performed until a goal configuration is reached. In plentiful experiments, our planner shows promising performances.

Authors

• Hong Liu 0008
• He Wen
• Yan Li

Keywords

• Path planning
• Intelligent robots
• Orbital robotics
• Performance evaluation
• USA Councils
• Helium
• Manipulators
• Buildings
• Process planning
• Tree data structures
• Optimal Segmentation
• Path Segment
• Learning Phase
• Complete Solution
• Solution Path
• Valid Path
• Increase In Size
• Urban Planning
• Energy Cost
• Real-time Performance
• First Search
• Mechanical Parameters
• Goal State
• Local Connectivity
• Human-robot Interaction
• Robot Motion
• Collision Detection
• Static Environment
• Accurate Energy
• Rapidly-exploring Random Tree
• Planning Problem
• Reference Count
• Query Time
• Inverse Mapping
• Dynamic Planning