A Goal-Biased RRT Path Planning Approach for Autonomous Ground Vehicle
Xianjian Jin, Zeyuan Yan, Hang Yang, Qikang Wang, Guodong Yin
20202020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)20 citationsDOI
Abstract
For the application of autonomous ground vehicle (AGV) operating in unstructured environment, a path planning method based on an improved goal-biased Rapidly-exploring Random Trees (bias-RRT) is proposed. The algorithm combines random sampling with numerical optimization to achieve fast convergence speed and satisfy constraints. KD-Tree and potential field of the environment are implemented to increase the sampling efficiency, and cubic B-splines are used to smooth the path for better tracking performance. The algorithm improves the efficiency of searching while guarantee safety and quality of the planned path. Simulation results verify the effectiveness of the proposed method.
Topics & Concepts
Random treeMotion planningPath (computing)Computer scienceConvergence (economics)Mathematical optimizationSampling (signal processing)Unmanned ground vehicleTree (set theory)Real-time computingMathematicsRobotArtificial intelligenceMathematical analysisProgramming languageEconomic growthComputer visionFilter (signal processing)EconomicsRobotic Path Planning AlgorithmsRobotics and Sensor-Based LocalizationHuman Pose and Action Recognition