| In recent years,with the rapid advancement of the national intelligent development strategy,more and more intelligent devices have entered various industries,which has brought great convenience to people’s production and life.Mowing robot integrates positioning,environmental perception,path planning and decision-making control.It is not only used for lawn pruning in municipal green space,airport,golf course and other venues,but also used for weeding in cultivated land and forest land.Therefore,it has attracted extensive attention.As one of the key technologies of mowing robot,path planning has become a key research object,which includes two important links: full coverage traversal path planning and obstacle avoidance planning.At present,the full coverage traversal path planning of mowing robots on the market pays more attention to the coverage of the target area than the research on the traversal repetition rate.Reducing the traversal repetition rate can not only improve the work efficiency,but also reduce the loss of the robot.At the same time,the traditional obstacle avoidance method has limitations.The robot is easy to fall into local minima and cannot reach the target position.The main work of this thesis is summarized as follows:Combined with the actual situation of the working environment,the grid method is used for environmental modeling.Taking the municipal green space as the environmental object,by analyzing the actual situation of the environment,giving reasonable assumptions and establishing the grid map environmental model.Because there are some obstacles in the environment map model whose edges do not coincide with the grid cell boundary,it is easy to make the intelligent algorithm fall into local optimization and the robot fall into a dead corner.To solve this problem,the expansion operation in binary morphology is used to expand the edge area of the obstacle,so that it coincides with the boundary of the grid unit,and a regular grid map is obtained,which can prepare for the smooth implementation of the subsequent full coverage traversal path planning.Aiming at the problems of low coverage,high repetition rate and weak universality of traversal path planning when mowing robot works in a large area,a full coverage traversal algorithm based on the combination of improved A* algorithm and DFS algorithm is proposed.Firstly,according to the known global information of the environment,the target area is divided into multiple sub areas without obstacles by cattle ploughing decomposition method;Secondly,the connected graph is established according to the adjacent relationship between sub regions,and the traversal order of sub regions is planned by DFS algorithm;Then,the improved A* algorithm is used for cross region path transfer and reciprocating traversal along the long side.Simulation results show that the coverage rate of the full coverage traversal algorithm proposed in this thesis is 100% and the traversal repetition rate is 0%.The traversal algorithm has the characteristics of high coverage,low repetition rate and strong universality.At the same time,through the optimization of path smoothness and the improvement of adding anti-collision safety spacing,the planned cross regional connection path length and steering times of A* algorithm are reduced by 3.26% and 62.5%respectively compared with those before the improvement of the algorithm.The path planned by the improved A* algorithm is smoother,safer and shorter.Aiming at the target unreachable problem of the artificial potential field method in the obstacle avoidance of the lawn mower robot,the distance between the robot and the target point and its distance adjustment factor are introduced into the original repulsion potential field function,so that the improved repulsion force follows the distance between the robot and the target point.The distance decreases and becomes smaller,ensuring that the direction of the force on the robot always tends to the target point,thereby solving the problem of target unreachability.Simulation experiments show that the improved artificial potential field method overcomes the problem of unreachable targets and successfully completes the task of obstacle avoidance planning. |
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