Research On Path Planning For Mobile Robots In Indoor Environments

With the continuous development of industrial and computer technologies,mobile robots play an increasingly important role in logistics,education,manufacturing and other fields,and have become a hot topic for current research.Path planning is a critical component of mobile robot navigation and can be applied to autonomous cruising,real-time obstacle avoidance,and target search.Mobile robots can sense their environment using both a static map and their own sensing modules,enabling them to improve path efficiency and obstacle avoidance through path algorithm optimization.Path planning can be divided into global planning and local planning according to different scenarios,however,in practical applications due to the limitations of single path planning,using only global or local planning algorithms often fails to meet the demand for optimal paths.Therefore,this thesis focuses on the path planning problem of indoor mobile robots and proposes a hybrid path planning method using the improved A*algorithm and the improved Timed Elastic Bands(TEB)algorithm to achieve better results,as follows:Firstly,the thesis presents the research background and significance of the topic,as well as an overview of the development of mobile robotics and path planning algorithms at home and abroad.And the method of constructing environment maps in mobile robots is introduced in detail,the kinematic model of differential drive robots is constructed,and the classical algorithms of global and local path planning are analyzed.Secondly,to address issues of low search efficiency,large steering angle,and too many path turning points associated with A* algorithm for path planning in static environment.In this thesis,the heuristic function is improved by introducing dynamic weights,so that the first stage of search tends to search efficiency and the later stage of search tends to find the optimal path.The algorithm adopts a bidirectional search strategy to improve search speed,and path smoothing is achieved using the cubic B-spline curve.Next,to overcome abnormal velocity jumps and body jitter due to sudden changes in acceleration of the robot in a complex obstacle environment,this thesis optimizes the objective function of the traditional TEB algorithm.The optimization involves introducing acceleration change rate constraints and optimizing obstacle constraints.Furthermore,a topology-based parallel planning module is introduced to construct local topological maps,filter homologous paths,and optimize paths to avoid local minima.The improved algorithm’s dynamic obstacle avoidance capability is verified in the Stage simulation environment.Finally,this thesis presents the verification of the effectiveness of a hybrid path planning algorithm based on the improved A* algorithm and the improved TEB algorithm.To validate the algorithm,static and dynamic obstacles were constructed in the Gazebo simulation environments of the ROS system.After simulation verification,the algorithm was implemented on a four-wheel differential speed robot and tested in a real indoor environment.The experimental results show that the robot can achieve real-time accurate obstacle avoidance in complex environments and can run at a smooth speed,effectively solving the speed oscillation problem.