A Deep-ConvLSTM Collision Prediction Model for Manipulators in Dynamic Environment

Abstract

Obstacle avoidance is one of the fundamental problems in human-robot collaboration (HRC) studies. The close proximity between robots and human usually leaves robots a short period of time to re-plan a safe motion, especially when facing non-static obstacles. Therefore, to identify collisions in advance and mitigate the computational efforts regarding robot motion re-planning, this paper proposes a network-based stop-go algorithm that uses only images capturing the states of the robot arm and a non-static obstacle without accessing any robot dynamics. In particular, a deep convolutional long-short-term memory (ConvLSTM) neural network is first developed to learn the spatial features of images, and predict both the robot arm and the non-static obstacle states five steps in advance. Next, the predictions are set back to the robot arm so that the robot arm would halt the current task when a potential future collision is identified. Eventually, the robot arm resumes the task after the non-static obstacle is clear. Extensive numerical studies have been conducted to validate the effectiveness of the proposed trajectory prediction scheme in presence of obstacles.