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A Decentralized Approach to Space Deconfliction



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A Decentralized Approach to Space Deconfliction Paul Scerri Sean Owens Bin Yu and Katia Sycara School of Computer Science Carnegie Mellon University Pittsburgh PA 15213 USA pscerri owens byu katia cs cmu edu Abstract This paper presents a decentralized approach to path planning for large numbers of autonomous vehicles in sparse environments Unlike existing approaches which are either computationally expensive or communication intensive the presented approach allows large numbers of vehicles to plan independently with low communication overhead The key to the algorithm is to observe that in sparse environments collisions are exceptional and that most of the time vehicles will simply not hit each other Hence it is reasonable to allow vehicles to plan independently and then resolve the small number of conflicts We operationalize this by having each vehicle send their planned paths to a small number of their team mates via tokens Each team member is required to check for conflicting paths that they have been informed about via a token and inform those involved when any conflict is detected Both analytic and empirical results show that the approach has very high probability of detecting all potential collisions for large numbers of vehicles in both 2D and 3D environments I I NTRODUCTION In emerging applications large numbers of cooperative autonomous vehicles such as autonomous ground vehicles 1 or unmanned aerial vehicles UAVs 2 will be required to share the same physical space Of particular interest are domains where many sensor assets are simultaneously working in a shared environment A critical challenge in such applications is to plan paths that ensure collisions do not occur as vehicles move around While there are many applications with different specific requirements this paper focuses on ones where there are up to 100s of vehicles performing time critical missions e g disaster response 3 4 and battlefield operations 5 6 in a relatively sparse 2D or 3D environment



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