A mixed autonomy coordination methodology for multi-robotic traffic control

We present a method for coordinating multir-obotic/multi-agent traffic control at intersections. The robotic agents (RA) move guided by a potential field along the lanes. At the intersections an intersection agent (IA) controls the flow of traffic by assigning priorities to the agents that are about to enter the intersection. The priorities are computed based on the density of RA in a lane and the flow rate of traffic in those lanes. The RAs integrate these assigned priorities into their potential field computations. The modified potential field computations help the RAs to move through the intersection avoiding collisions. An elegant mixed autonomy scheme is thereby achieved where the IAs decide upon the priorities at the intersection while the low level collision avoidance maneuvers are left with the individual RAs. This scheme preserves the distributed nature and the autonomy of potential field maneuvers while simultaneously balancing the computation load between the IA and RAs. We compare this method with a method where the RAs navigate the intersection without a superior direction from the IAs through priorities or when IAs direct the RAs based on priorities computed on a first come first served basis. We show performance gain over both these methods in simulations.