The software of behaviour-based algorithm~ was parted to several functional modules which represented different behaviours with different priorities. A basic algorithm with S-type arbiter and an improved algorithm wit...The software of behaviour-based algorithm~ was parted to several functional modules which represented different behaviours with different priorities. A basic algorithm with S-type arbiter and an improved algorithm with I-type arbiter were compared. The improved algorithm can reduce judging time and avoid some mistakes of the basic one. In mapping obstacles, the robot adjusted the spread angle according to different distances to obstacles in scaled vector field histogram （SVFH） algorithm, and then the robot turned more sharply in near obstacles than in far obstacles, which made the robot move more safely and smoothly in a cluttered room.展开更多
A novel method is proposed to dynamically control the path following of a ground Ackerman steering robot to avoid a collision.The method consists of collision prediction module,collision avoidance module and global pa...A novel method is proposed to dynamically control the path following of a ground Ackerman steering robot to avoid a collision.The method consists of collision prediction module,collision avoidance module and global path following module.The elliptic repulsive potential field method（ER-PFM）and the enhanced vector polar histogram method（VPH＋）based on the Ackerman steering model are proposed to predict the collision in a dynamic environment.The collision avoidance is realized by the proposed cost function and speed control law.The global path following process is achieved by pure pursuit.Experiments show that the robot can fulfill the dynamic path following task safely and efficiently using the proposed method.展开更多
Robotic navigation in unknown environments is challenging due to the lack of high-definition maps.Building maps in real time requires significant computational resources.Nevertheless,sensor data can provide sufficient...Robotic navigation in unknown environments is challenging due to the lack of high-definition maps.Building maps in real time requires significant computational resources.Nevertheless,sensor data can provide sufficient environmental context for robots’navigation.This paper presents an interpretable and mapless navigation method using only two-dimensional(2D)light detection and ranging(LiDAR),mimicking human strategies to escape from dead ends.Unlike traditional planners,which depend on global paths or vision-based and learning-based methods,requiring heavy data and hardware,our approach is lightweight and robust,and it requires no prior map.It effectively suppresses oscillations and enables autonomous recovery from local minimum traps.Experiments across diverse environments and routes,including ablation studies and comparisons with existing frameworks,show that the proposed method achieves map-like performance without a map—reducing the average path length by 50.51%when compared to the classical mapless Bug2 algorithm and increasing it by only 17.57%when compared to map-based navigation.展开更多
基金National Natural Science Foundation of China(No.60975059)Leading Academic Discipline Project of Shanghai Municipal Education Commission,China(No.J513032)Innovation Program of Shanghai Municipal Education Commission,China(No.09YZ343)
文摘The software of behaviour-based algorithm~ was parted to several functional modules which represented different behaviours with different priorities. A basic algorithm with S-type arbiter and an improved algorithm with I-type arbiter were compared. The improved algorithm can reduce judging time and avoid some mistakes of the basic one. In mapping obstacles, the robot adjusted the spread angle according to different distances to obstacles in scaled vector field histogram （SVFH） algorithm, and then the robot turned more sharply in near obstacles than in far obstacles, which made the robot move more safely and smoothly in a cluttered room.
基金Supported by the National Natural Science Foundation of China(91420203)
文摘A novel method is proposed to dynamically control the path following of a ground Ackerman steering robot to avoid a collision.The method consists of collision prediction module,collision avoidance module and global path following module.The elliptic repulsive potential field method（ER-PFM）and the enhanced vector polar histogram method（VPH＋）based on the Ackerman steering model are proposed to predict the collision in a dynamic environment.The collision avoidance is realized by the proposed cost function and speed control law.The global path following process is achieved by pure pursuit.Experiments show that the robot can fulfill the dynamic path following task safely and efficiently using the proposed method.
基金Project supported by the Ling Chuang Research Project of China National Nuclear Corporation(No.CNNC-LCKY-2025-098)the Gansu Province Science and Technology Major Project-Industrial Project(Nos.22ZD6GA048 and 23ZDGA006)。
文摘Robotic navigation in unknown environments is challenging due to the lack of high-definition maps.Building maps in real time requires significant computational resources.Nevertheless,sensor data can provide sufficient environmental context for robots’navigation.This paper presents an interpretable and mapless navigation method using only two-dimensional(2D)light detection and ranging(LiDAR),mimicking human strategies to escape from dead ends.Unlike traditional planners,which depend on global paths or vision-based and learning-based methods,requiring heavy data and hardware,our approach is lightweight and robust,and it requires no prior map.It effectively suppresses oscillations and enables autonomous recovery from local minimum traps.Experiments across diverse environments and routes,including ablation studies and comparisons with existing frameworks,show that the proposed method achieves map-like performance without a map—reducing the average path length by 50.51%when compared to the classical mapless Bug2 algorithm and increasing it by only 17.57%when compared to map-based navigation.
