Real-Time Placement Of A Wheelchair-Mounted Robotic Arm

Picking up an object with a wheelchair mounted robotic arm can be decomposed into a wheelchair navigation task designed to position the robotic arm such that the object is "easy to reach", and the actual grasp performed by the robotic arm. A convenient definition of the notion of ease of reach can be given by creating a score (ERS) that relies on the number of distinct ways the object can be picked up from a given location. Unfortunately, the accurate calculation of ERS must rely on repeating the path planning process for every candidate position and grasp type, in the presence of obstacles. In this paper we use the bootstrap aggregation over hand-crafted, domain specific features to learn a model for the estimation of ERS. In a simulation study, we show that the estimated ERS closely matches the actual value and the speed of estimation is fast enough for real-time operation, even in the presence of a large number of obstacles in the scene.