An Efficient Path Planning and Control Algorithm for RUAV’s in Unknown and Cluttered Environments

摘要

This paper presents an efficient planning and execution algorithm for the navigation of an autonomous rotary wing UAV (RUAV) manoeuvering in an unknown and cluttered environment. A Rapidly-exploring Random Tree (RRT) variant is used for the generation of a collision free path and linear Model Predictive Control(MPC) is applied to follow this path. The guidance errors are mapped to the states of the linear MPC structure by using the nonlinear kinematic equations. The proposed path planning algorithm considers the run time of the planning stage explicitly and generates a continuous curvature path whenever replanning occurs. Simulation results show that the RUAV with the proposed methodology successfully achieves autonomous navigation regardless of its lack of prior information about the environment. Keywords Dynamic path planning - Model predictive control - Rapidly-exploring random trees - Small-scale helicopter This work is supported in part by the ARC Centre of Excellence programme, funded by the Australian Research Council (ARC) and the New South Wales State Government.