Nature-inspired dynamic control for pursuit-evasion of robots

The pursuit-evasion problem is widespread in nature, engineering and societal applications. It is commonly observed in nature that a predator runs faster than its prey but it has less agile maneuverability. Over millions of years of evolution, animals have developed effective and efficient pursuit and evasion strategies. In this paper, we provide a dynamic framework for pursuit-evasion of unicycle systems from a nature-inspired perspective. Firstly, for the problem with one pursuer and one evader, we propose an Alert-Turn control strategy which consists of two efficient ingredients: the suddenly turning maneuver and the alert condition for starting and maintaining the maneuver. We present and analyze the escape and capture results at a lower level of a single run and at a higher level with respect to parameters' changes. A theorem with sufficient condition for capture is also given. Then, the Alert-Turn strategy is combined with aggregation control laws and a target-changing mechanism to model more complex phenomenons with multiple pursuers and evaders. By adjusting a selfish parameter, the aggregation control commands can achieve different escape patterns of evaders: cooperative mode, selfish mode, as well as their combinations, and the influence of the selfish parameter is quantified. We present the effects of the number of pursuers and the target-changing mechanism from a statistical perspective. Our findings are largely in line with observations in nature. Furthermore, our control strategies are verified by numerical simulations that replicate some chasing behaviors of animals in nature.