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Path Planning Algorithm for an Autonomous Electric Wheelchair in Hospitals

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A novel path planning algorithm for an autonomous electric wheelchair that can be used in hospitals is proposed and verified. The objective of this study is to develop an autonomous… Click to show full abstract

A novel path planning algorithm for an autonomous electric wheelchair that can be used in hospitals is proposed and verified. The objective of this study is to develop an autonomous path planning algorithm that satisfies body acceleration constraints for patient safety and follows paths suitable for hospitals. The wheelchair dynamics and equations of motion, including caster dynamics and the user’s influence on the wheelchair, are newly derived in this research. Since the proposed algorithm is based on two-wheel robot kinematics, it is applicable to most two-wheel actuated systems. When a wheelchair’s destination is set, a series of waypoints is computed and then the desired speed & attitude at each waypoint of the path are designed. Then, a speed profile is designed to minimize the travel time while adaptively changing the angular rate gain to minimize excessive lateral motion. As a result, the linear speed command of each actuated wheel is computed. Additionally, the hybrid reciprocal velocity obstacle algorithm is updated to guarantee that the body acceleration constraints are satisfied for applications with complicated dynamics. The performance of the proposed algorithm, with the full dynamics of the wheelchair, is demonstrated by extensive numerical simulations with conventional toy problems and then evaluated in two hospitals. The simulation results demonstrate that the proposed algorithm satisfies body acceleration constraints while following paths which are suitable for hospitals and that the proposed autonomous electric wheelchair could be applied in hospitals.

Keywords: autonomous electric; electric wheelchair; wheelchair; planning algorithm; algorithm autonomous; path planning

Journal Title: IEEE Access
Year Published: 2020

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