Real-time 3D Collision Avoidance Method for Safe Human and Robot Coexistence

A novel solution to the three-dimensional dynamic human-robot collision problem is presented. Sphere-based geometric models are used for the human and robot due to the efficiency of the distance computation. The collision avoidance algorithm searches for collision-free paths by moving theend-effector along a set of pre-defined search directions. An optimization method is employed to select the search direction that balances between the robot approaching its goal location, and maximizing the distances between the human and robot models. The optimization incorporates predictions of the motions of the robot and human to reduce the negative effects of a non-instantaneous robot time response. The robot prediction is based on a transfer function model of its experimental time response at the joint level. The human prediction is performed at the sphere level using the weighted mean of past velocities. Predicting at the sphere level eliminates the difficulty introduced by the limbs moving in different directions. After describing the collision avoidance algorithm, a human walking towards a moving Puma robot arm is simulated. Captured motion data is used to make the human motion realistic. Monte Carlo simulations using 1000 random human walking paths passing through the robot workspace are used to evaluate the algorithm. The algorithm prevented all collisions due to the robot. The algorithm is deterministic and efficient enough to be used in real-time. On a 1.8GHz Pentium IV PC, a 40 Hz sampling rate was achieved.