Collision-avoided Tracking Control of UAV Using Velocity-adaptive 3D Local Path Planning

This study proposes a novel velocity-adaptive 3D local path planning algorithm (3DLPP) for single unmanned aerial vehicle (UAV) flying along a reference trajectory with obstacle collision avoidance (CA) in realtime. In the local path planning, local path candidates are generated in three-dimensional space to avoid obstacles in real time using cost functions. Velocity and acceleration profiles are estimated in real-time using model predictive control law (MPC) with the well-defined cost function minimizing centripetal acceleration. It is accentuated that the velocity and acceleration profiles are not pre-defined, but optimally determined in the current local path. Then, the UAV is controlled to fly along the selected desired trajectory using sliding mode control (SMC) and Lyapunov stability theory-based control law (LSTC) with the updated velocity and acceleration profiles. Finally, the simulation confirmed that the UAV tracked the baseline and avoided all obstacles by moving in the omni-direction suppressing the centripetal acceleration of UAV during maneuvering.

Automated summary

This study proposes a novel velocity-adaptive 3D local path planning algorithm (3DLPP) for single unmanned aerial vehicle (UAV) flying along a reference trajectory with obstacle collision avoidance (CA) in realtime. In the local path planning, local path candidates are generated in three-dimensional space to avoid obstacles in real time using cost functions. The simulation confirmed that the UAV tracked the baseline and avoided all obstacles by moving in the omni-direction suppressing the centripetal acceleration of UAV during maneuvering.