Motion Planning for a Robotic Wheelchair with SLERP MPC Local Planner
Daifeng Wang, Wenjing Cao, Bo Zhang, Masakazu Mukai · 2023
This paper presents a motion planning approach for a four-wheeled robotic wheelchair that ensures safe and comfortable movement in an unknown scenario. To address this problem, a dual quaternion-based spherical linear interpolation (SLERP) model predictive control (MPC) local path planner is developed and implemented. First, the four-wheeled robotic wheelchair is described using the unicycle model. Our approach utilizes a simultaneous localization and mapping (SLAM) method to achieve the environment in which the robotic wheelchair operates. Additionally, we use a two-layer path planner to ensures smooth and comfortable motion for the wheelchair user, enhancing their overall experience. The A* algorithm is ultilized to generate global planning path in the established grid environment. Then, an online SLERP-MPC local planner is implemented for the navigation and control of robotic wheelchairs. The simulation environment were built within the Robot Operating System (ROS). Simulation results demonstrate the effectiveness of our proposed strategy, allowing the robotic wheelchair to move safely and smoothly from its initial position to a target location. In conclusion, our work presents a validated and effective motion planning design for robotic wheelchair navigation.