An efficient algorithm for path planning is crucial for guiding autonomous surface vehicles(ASVs)through designated waypoints.However,current evaluations of ASV path planning mainly focus on comparing total path lengt...An efficient algorithm for path planning is crucial for guiding autonomous surface vehicles(ASVs)through designated waypoints.However,current evaluations of ASV path planning mainly focus on comparing total path lengths,using temporal models to estimate travel time,idealized integration of global and local motion planners,and omission of external environmental disturbances.These rudimentary criteria cannot adequately capture real-world operations.To address these shortcomings,this study introduces a simulation framework for evaluating navigation modules designed for ASVs.The proposed framework is implemented on a prototype ASV using the Robot Operating System(ROS)and the Gazebo simulation platform.The implementation processes replicated satellite images with the extended Kalman filter technique to acquire localized location data.Cost minimization for global trajectories is achieved through the application of Dijkstra and A*algorithms,while local obstacle avoidance is managed by the dynamic window approach algorithm.The results demonstrate the distinctions and intricacies of the metrics provided by the proposed simulation framework compared with the rudimentary criteria commonly utilized in conventional path planning works.展开更多
There are many challenges for robot navigation in densely populated dynamic environments.This paper presents a survey of the path planning methods for robot navigation in dense environments.Particularly,the path plann...There are many challenges for robot navigation in densely populated dynamic environments.This paper presents a survey of the path planning methods for robot navigation in dense environments.Particularly,the path planning in the navigation framework of mobile robots is composed of global path planning and local path planning,with regard to the planning scope and the executability.Within this framework,the recent progress of the path planning methods is presented in the paper,while examining their strengths and weaknesses.Notably,the recent developed Velocity Obstacle method and its variants that serve as the local planner are analyzed comprehensively.Moreover,as a model-free method that is widely used in current robot applications,the reinforcement learning-based path planning algorithms are detailed in this paper.展开更多
基金Supported by the funding from RMIT Internal Research Grant R1.
文摘An efficient algorithm for path planning is crucial for guiding autonomous surface vehicles(ASVs)through designated waypoints.However,current evaluations of ASV path planning mainly focus on comparing total path lengths,using temporal models to estimate travel time,idealized integration of global and local motion planners,and omission of external environmental disturbances.These rudimentary criteria cannot adequately capture real-world operations.To address these shortcomings,this study introduces a simulation framework for evaluating navigation modules designed for ASVs.The proposed framework is implemented on a prototype ASV using the Robot Operating System(ROS)and the Gazebo simulation platform.The implementation processes replicated satellite images with the extended Kalman filter technique to acquire localized location data.Cost minimization for global trajectories is achieved through the application of Dijkstra and A*algorithms,while local obstacle avoidance is managed by the dynamic window approach algorithm.The results demonstrate the distinctions and intricacies of the metrics provided by the proposed simulation framework compared with the rudimentary criteria commonly utilized in conventional path planning works.
文摘There are many challenges for robot navigation in densely populated dynamic environments.This paper presents a survey of the path planning methods for robot navigation in dense environments.Particularly,the path planning in the navigation framework of mobile robots is composed of global path planning and local path planning,with regard to the planning scope and the executability.Within this framework,the recent progress of the path planning methods is presented in the paper,while examining their strengths and weaknesses.Notably,the recent developed Velocity Obstacle method and its variants that serve as the local planner are analyzed comprehensively.Moreover,as a model-free method that is widely used in current robot applications,the reinforcement learning-based path planning algorithms are detailed in this paper.
