Research On Path Planning Algorithm Of Mobile Robot

In recent years,with the rapid development of technology,the application of mobile robots has involved all aspects of production and life.As a hot research direction,the path planning of mobile robots has attracted a large number of scholars to study it.The path planning of a mobile robot refers to the analysis and processing of a priori information such as maps and sensor-perceived data,and searches for a collision-free path from a starting point to a target point according to a set strategy.According to the different degree of perception of the surrounding environment by mobile robots,path planning can be divided into global path planning and local path planning.Global path planning is planned based on known global environment information,and local path planning is based on unknown environment and sensor Perceived data builds local environment models and plans.The A* algorithm is used in the global path planning.Aiming at the problem that the traditional A* algorithm planning path has many turning points and does not satisfy the global optimality,an A* algorithm based on adaptive neighborhood search and steering cost is proposed;the DWA algorithm is used in the local path planning.Aiming at the problem that the DWA algorithm combined with the global path cannot reach the target point quickly,a DWA algorithm based on the turning point guidance of the global path is proposed.Innovative work mainly includes the following two parts:(1)A* algorithm based on adaptive neighborhood search and steering cost.In view of the problem that the traditional A* algorithm planning path has many turning points and the constraints of the 8-neighbor search node strategy lead to the problem of not satisfying the global optimality,first,an adaptive neighborhood search node strategy is designed,so that the algorithm can be based on the surrounding obstacle information,adaptively select the appropriate neighborhood to search for the optimal child node;then by establishing a mobile robot steering cost model,the steering cost is introduced into the evaluation function of the A* algorithm;based on the adaptive neighborhood search and steering cost the A* algorithm breaks through the constraints of 8 neighborhood search nodes,shortens the path length,and uses the steering cost to search for the global optimal path with fewer turning points.(2)DWA algorithm based on global path turning point guidance.Aiming at the problem that the DWA algorithm combined with the global path frequently decelerates at the local subtarget points and causes the mobile robot to travel too long,a DWA algorithm based on the turning point guidance of the global path is proposed.First,the turning point in the global path is extracted and used as the local sub-target point of the DWA algorithm;then the evaluation function of the DWA algorithm is optimized,and the evaluation sub-function close to the global path is added to make the planned path more closely fit the global path,and the path direction evaluation sub-function makes the direction of the mobile robot more stable.The path planned by the DWA algorithm based on the turning point of the global path meets the global optimality,and the travel time is greatly shortened by reducing the number of local sub-target points.Experimental results show that compared with the traditional A* algorithm,the total path cost of the A* algorithm based on adaptive neighborhood search and steering costs is reduced by 19.3%,and the number of turning points is reduced by 44.4%;compared with the DWA algorithm combined with the global path.The travel time of the DWA algorithm for global path turning point guidance has been shortened by 41.9%.