Learning to trick cost-based planners into cooperative behavior

In this paper we consider the problem of routing autonomously guided robots by manipulating the cost space to induce safe trajectories in the work space. Specifically, we examine the domain of UAV traffic management in urban airspaces. Each robot does not explicitly coordinate with other vehicles in the airspace. Instead, the robots execute their own individual internal cost-based planner to travel between locations. Given this structure, our goal is to develop a high-level UAV traffic management (UTM) system that can dynamically adapt the cost space to reduce the number of conflict incidents in the airspace without knowing the internal planners of each robot. We propose a decentralized and distributed system of high-level traffic controllers that each learn appropriate costing strategies via a neuro-evolutionary algorithm. The policies learned by our algorithm demonstrated a 16.4% reduction in the total number of conflict incidents experienced in the airspace while maintaining throughput performance.