Model predictive control of an autonomous wheelchair considering pedestrian reaction using social force model for collision avoidance

In the emerging aging society, the expectation for sufficiently safe and reliable mobility tools, like automated wheelchairs, is growing to keep the lives of physically needed persons active and comfortable. An essential intelligence required for an automated wheelchair is interacting with pedestrians in a crowded pathway. This paper addresses this issue and proposes an optimal control approach for interactive trajectory generation based on a pedestrian behavior model in typical pedestrian interaction scenarios. Specifically, we propose a novel way to incorporate pedestrian behavior near the wheelchair using the social force model (SFM) under the predictive optimal control framework without requiring extra computational burden. Such incorporation of SFM enables the wheelchair to intelligently predict the multiple pedestrian interactions when the wheelchair emerges in their range of vision in complex environments. Finally, control decisions in the linear model predictive control framework, which considers conditional safety constraints, ensure optimal performance even in adaptive or changing pedestrian environments.