Research On Path Planning Method Of Search And Rescue Robot In Complex Environment

At present,search and rescue robots are widely used in war,natural disaster,NBCR and other field investigation and casualty search and rescue missions.Different from the common structured environment,various obstacles and unstructured terrain in the complex post-disaster environment will bring great challenges to the autonomous movement of the robot.Therefore,it is of great significance to improve the autonomous navigation and path planning capability of search and rescue robots in unstructured terrain and other complex environments to improve the rescue efficiency of search and rescue robots and enhance their survival ability in unknown environment.Finding the shortest or optimal path from the starting point to the target point is the key to improve the autonomous navigation ability of search and rescue robots.This paper takes search and rescue robot as the target carrier platform and combines the characteristics of unstructured environment to study the static global path planning based on ant colony algorithm and the dynamic local path planning based on dynamic window method.On this basis,the static global path planning under three-dimensional environment is completed.The main research work is as follows:(1)Aiming at the problems of search stagnation and slow convergence in traditional ant colony algorithm,an improved ant colony algorithm is proposed.By combining the heuristic function of A* algorithm and b-spline curve method,the initial pheromone concentration and path turning node of the algorithm are improved,and the convergence of the algorithm and the smoothness of the generated path are improved.The turning cost is added into the pheromone update rule to reduce the turning point in the path,and the pheromone volatilization factor is adjusted adaptively to avoid the stagnation of the algorithm.Finally,simulation and experimental verification show that the improved algorithm proposed in this paper reduces the path length by 9.5%,the number of turns by 60% and the number of iterations by 72% compared with the traditional algorithm.The experimental results show that the improved ant colony algorithm proposed in this paper is more feasible and effective than the traditional ant colony algorithm.(2)A set of collision avoidance strategies based on dynamic window method is designed,and the simulation results show that the algorithm has good obstacle avoidance performance.Completed the improved ant colony algorithm with dynamic window method of a hybrid path planning algorithm,and is verified by the simulation in a static environment known obstacles but the presence of dynamic obstacles under complicated environment,hybrid path algorithm can make the search and rescue robot dynamic obstacles in a timely manner to make the corresponding behavioral decision,smooth to arrive at the target point.(3)Aiming at the path planning problem of the robot in the three-dimensional environment,the three-dimensional space was divided into planes,and then each plane was rasterized.The three-dimensional land environment modeling for the disaster sites such as NBCR for the land search and rescue robot and the urban environment modeling for the search and rescue UAV were completed.In view of the complicated three-dimensional environment and changeable geomorphology,obstacle avoidance strategy is increased and pseudo-random probability is adopted to improve the efficiency of early ant search.After each iteration,the pheromone of the worst ant path is weakened by updating the rule according to the good ant.Deadlock point fallback guarantees the diversity of solutions.In the three-dimensional urban environment model,the target point of bidirectional A* algorithm is improved to speed up the algorithm's search speed.The optimal path is quadratic optimized to reduce the turning point and path length of the optimal path.To sum up,this paper studies the path planning method of search and rescue robots in complex environments,and focuses on the problem of insufficient path planning ability of search and rescue robots in complex environments,thus forming a set of practicable path planning algorithms in complex environments.First of all,the ant colony algorithm with global path planning ability is optimized,pheromone rules and pheromone volatile factors are improved,A* algorithm heuristic function and B-spline curve method are integrated,and the experimental results show that the performance of the algorithm is greatly improved compared with the traditional algorithm.Then the collision avoidance strategy of dynamic window method is designed,and the fusion and simulation experiment of global algorithm and local algorithm are completed to verify the path planning effect under complex environment.Finally,based on the two-dimensional map,the verification of the global path planning algorithm in the three-dimensional environment is completed,and the path planning algorithm of the three-dimensional search and rescue UAV based on the A* algorithm is designed.The relevant research results in this paper can complete the path planning of search and rescue robots in the complex unknown environment,effectively improve the autonomous navigation ability of search and rescue robots,and make contributions to further improve the actual combat performance of search and rescue robots when performing rescue tasks.