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Paper ThAT13.2

Rebhuhn, Carrie (Oregon State University), Skeele, Ryan (Oregon State University), Chung, Jen Jen (Oregon State University), Hollinger, Geoffrey (Oregon State University), Tumer, Kagan (Oregon State University)

Learning to Trick Cost-Based Planners into Cooperative Behavior

Scheduled for presentation during the Regular session "Path Planning for Mobile Robots or Agents" (ThAT13), Thursday, October 1, 2015, 08:45−09:00, Saal 8

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sept 28 - Oct 03, 2015, Congress Center Hamburg, Hamburg, Germany

This information is tentative and subject to change. Compiled on July 19, 2019

Keywords Path Planning for Multiple Mobile Robots or Agents, Learning Control, Unmanned Aerial Systems

Abstract

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.

 

 

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