Improved Hybrid Path Planning Algorithm in Indoor Environment
In the path planning of robots in the indoor unstructured complex environment,problems often arise such as inaccessible target points,deflection in the planning process,and failure to avoid dynamic obstacles in time.To solve these problems,an improved hybrid algorithm for indoor path planning is proposed,which combines the improved global path planning with the improved local path planning algorithm.Firstly,the heurism factor of the traditional A—Star algorithm is optimized,the search range and nodes are reduced,and then the traditional A—Star algorithm is smoothed by the Angle bisector tangent point method.Secondly,with the combination of path and environment information,an improved artificial potential field algorithm is employed for local path planning,and then the repulsive field parameters are modified to solve the problem of unreachable target point.Also,the dynamic force field function is constructed which enables it to resolve dynamic obstacles.Finally,a path planning experiment was conducted on the hybrid algorithm in real environment.Compared with the traditional hybrid algorithm,by the hybrid algorithm proposed in this paper the path planning length is reduced by 10.3％,the running time by 12.5％,and 34 redundant nodes are cut out.The results show that the hybrid algorithm can effectively solve the problems in the indoor unstructured and complex path planning.
mobile robotpath planning technologyA-Star algorithmartificial potential field algorithmautonomous obstacle avoidanceconputer control
万方数据
维普
