Robot-to-human handover with obstacle avoidance via continuous time Recurrent Neural Network

Parallel with the development of service robots, it is vital for the robots to carry out handovers autonomously. Robot-to-human handover is a coordination in time and space for a robot to deliver an object to human. A good robot-to-human handover should consider human safety and preference, natural motion planning that mimics human and adaptability to the changes of the environment. Conventional handover motion mostly rely on sampling-based algorithms that emphasizes on kinematic and dynamic analysis. This kind of motion planning could become complicated and slow in response if the handover motion is implemented in a dynamic environment where real time motion planning is required. To simplify the implementation of robot-to-human handover, a motion learning and generation framework that based on Continuous Time Recurrent Neural Network(CTRNN) is proposed. The proposed framework is equipped with the capabilities of object recognition, motion generation based on past learning experience and obstacle adaptation. As compared with conventional method, the proposed framework could be easily extended to handover motion with high dimensional configuration spaces as the motion can be generated from the learnt experience. In the proposed framework, the handover behaviour can be learnt via human-guided motion teaching which provides an intuitive and visible solution for motion planning. The proposed framework has been experimentally evaluated on a customized design robot via robotto-human handover testing. Based on the testing, the feasibility of the proposed framework had been justified.