Haptic-Guided Path Generation for Remote Car-Like Vehicles

Abstract

Despite significant advances in robot autonomy, manual intervention by a human operator is necessary in many situations. This usually requires qualified staff and some robot-specific input device even for the comparatively simple case of platform locomotion. For this reason, we propose a novel path generation method applicable to car-like vehicles. With this method, the operator “draws” a desired 2D path by walking in a large-scale haptic interface while a guiding force is exerted, which ensures that the generated path can later be accurately followed by a path tracking controller running offline on a remote robot. We present a local optimization-based path planner, a higher-level path generation algorithm utilizing the aforementioned planner, and a force feedback law. Experiments show improved feasibility of the generated paths without affecting the operator's ability to make decisions independently.