A prey-predator model for efficient robot tracking

Abstract

Tracking is a common topic in various areas of robotics research. Motivated by the hunting behavior of predators in nature, we propose a prey-predator model for efficient robot tracking. The head direction and speed of the pursuer is automatically adjusted according to the position and velocity of the prey. Under the situation with perception uncertainty, where the actual location of the prey is not observable, the pursuer predicts the location of the prey according to simple inference, an online adaptive autoregressive model, or an online adaptive echo state network. Simulation results demonstrate that the proposed prey-predator model is able to control the pursuer and to track the prey efficiently, even under perception uncertainty. Simple inference gives better results when the motion of the target is piecewise linear, while echo state network is more suitable when the dynamics of the target are more complex. The proposed prey-predator model thus provides an efficient method tracking targets with various statistical nature of trajectories for applications such as underwater robot tracking, human tracking and team formation.