Research On Safe And Efficient Path Planning Method Of Mobile Robot In Dynamic Environment

With the rapid development of science and technology,autonomous mobile robots have been applied in many fields and become an indispensable part of social development.Path planning is an important issue in mobile robot navigation.Different types of robots serve various industries and need to adapt to different dynamic environments,thus the realization of safe and efficient path planning in a dynamic environment is a difficult problem in this research field.This paper studies the artificial potential field approach,analyzes the disadvantages of the traditional algorithm,and proposes an modified artificial potential field approach to improve the safety and efficiency of path planning in a dynamic environment.The main work includes the following aspects:1)The disadvantages of the traditional artificial potential field approach mainly include:?The path of the robot oscillates in the presence of obstacles due to the large moving step of the robot;?goal nonreachable with obstacles nearby(GNRON);?trapped in a local minimum region.Aiming at the classical local minimum problem in artificial potential field approach,this paper discusses its formation cause,detection scheme and common solutions,and selects the temporary virtual target point approach to improve the local minima problem.However,some classic schemes need to detect whether the robot is caught in the local minimum area,and then guide the robot to leave the area,which causes a low path efficiency.To solve the problem,the dynamic window approach is introduced.The dynamic window method simulates the trajectory through velocity sampling,and then uses the evaluation function to select the optimal path.With the dynamic approach,the artificial potential field method can be improved.First,the trajectory solved by the artificial potential field method is simulated,and according to the characteristics of the potential field method,evaluation criteria are set to select the optimal path.When the robot falls into the local minimum region,the optimal path in the simulated trajectory will be selected,so that the algorithm directly avoids the region without detection.The improved algorithm improves path efficiency,reduces detection links and eliminates path oscillation problems.2)In a dynamic environment,the path safety of the dynamic-window-improved artificial potential field approach is low,this paper proposes an artificial potential field approach based on the danger index to improve safety.By introducing the velocity potential field to further improve the repulsion function,the robot avoids dynamic obstacles not only depending on the positional relationship between the two.This paper introduces the idea of danger index,and uses the relationship between robots and dynamic obstacles to further improve the velocity repulsive potential field,and proposes an artificial potential field method based on improved danger index.In the danger index,the relative speed influence coefficient is calculated according to the speed relationship between the robot and the dynamic obstacle,so the different obstacle avoidance strategies are selected,and the speed repulsive potential field is established to improve the efficiency of the path.The relative distance influence factor is calculated according to the positional relationship between the two,and this factor controls the influence degree of the velocity repulsive potential field and increases the safety of the path.Under the same experimental conditions,the path planning algorithm in ROS is compared with the simulation algorithm in this paper.The experimental results show-that after the improvement based on the danger index,in a slow dynamic environment,a path equivalent to ROS efficiency can be planned.In a fast dynamic environment,compared with the ROS path,the algorithm in this paper not only ensures the security of the path,but also has obvious advantages in path efficiency.3)In this paper,the artificial potential field path planning algorithm based on the improved danger index is implemented on the mobile robot TIAGo,which combines various sensor data to obtain environmental information,and uses recognition and filtering algorithms to process the data to build a complete path planning system.Finally,a complex indoor dynamic environment was built and path planning experiments in this environment was conducted to prove the feasibility,safety and efficiency of the system.