| With the application of mobile robots becoming more and more widely,higher requirements are put forward for the degree of autonomy and navigation efficiency of robots.It is required that robots can quickly carry out independent exploration and mapping,and efficiently plan out the driving path.However,the current mainstream independent exploration methods are based on boundaries.Random sampling method has been studied extensively in recent years because it does not need to process the whole map when extracting boundaries and can be used in three-dimensional environment.However,there are still problems such as low detection efficiency of boundary points and repeated exploration in multi-obstacle and narrow environments.The global path planning algorithm A* has higher efficiency than other algorithms,but the planned path contains many redundant nodes and inflection points,and the search efficiency is low in large-scale environment.The Dynamic Window Algorithm(DWA)in local path planning is widely used in all kinds of robots because it conforms to dynamic constraints.However,there are redundant paths in the travel path,which reduces the navigation efficiency.Therefore,this paper studies the above problems:(1)In global path planning,aiming at the low planning efficiency of A* and its improved algorithm in large-scale environment,a global path planning algorithm based on ray and obstacle contour extension is proposed.Firstly,the ray model was introduced to replace the traditional neighborhood search,and then the obstacle contour expansion strategy based on direction information was proposed.Only the nodes outside the obstacle contour were searched,which greatly reduced the number of nodes involved in the calculation and improved the planning efficiency.Secondly,aiming at the problem of incomplete path search and repeated search,a new close table assignment method is designed to ensure the search completeness and search efficiency of the algorithm.Finally,Pycharm simulation and bobac2 robot experiment were used to verify the feasibility and effectiveness of the algorithm.(2)In local path planning,adaptive velocity term weight DWA algorithm is proposed to solve the redundant path problem existing in DWA algorithm.Firstly,the causes of redundant paths and the influence of velocity on redundant paths are analyzed.Then,considering both path quality and navigation efficiency,an evaluation function of adaptive velocity term weight is designed,which makes the robot not only adjust course at a small speed to reduce redundant paths,but also move towards the target point at a large speed,which improves navigation efficiency and reduces redundant paths.Finally,the feasibility and effectiveness of this strategy were verified by Pycharm simulation and bobac2 robot experiment.(3)In the autonomous exploration,aiming at the problem of low detection efficiency and repeated exploration of boundary points under the condition of multiple obstacles and narrow environment by the fast extended random tree algorithm,the autonomous exploration algorithm of double-region detection boundary points is proposed.Firstly,random trees are established in unknown and known regions to grow opposite each other,and the multi-node strategy is used to accelerate the extraction speed of boundary points.Secondly,a tree node fusion strategy is proposed to add the newly explored random tree nodes of unknown regions into the known region random tree to increase the speed of the random tree covering the map.Furthermore,the sampling range and the maximum number of nodes of the local boundary detector are limited to reduce the occurrence of repeated exploration.Finally,Robot Operation System(ROS)simulation and bobac2 robot experiment are used to verify the effectiveness of the algorithm. |
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