Towards load-balanced de-congested multi-robotic agent traffic control by coordinated control at intersections

Abstract

This paper presents a methodology for the coordination of multiple robotic agents moving from one location to another in an environment embedded with a network of agents, placed at strategic locations such as intersections. These intersection agents, communicate with robotic agents and also with each other to route robots in a way as to minimize the congestion, thus resulting in the continuous flow of robot traffic. A robot’s path to its destination is computed by the network (in this paper, ‘Network’ refers to the collection of ‘Network agents’ operating at the intersections) in terms of the next waypoints to reach. The intersection agents are capable of identifying robots in their proximity based on signal strength. An intersection agent controls the flow of agent traffic around it with the help of the data it collects from the messages received from the robots and other surrounding intersection agents. The congestion of traffic is reduced using a two-layered hierarchical strategy. The primary layer operates at the intersection to reduce the time delay of robots crossing them. The secondary layer maintains coordination between intersection agents and routes traffic such that delay is reduced through effective load balancing. The objective at the primary level, to reduce congestion at the intersection, is achieved through assigning priorities to pathways leading to the intersection based on the robot traffic density. At the secondary level, the load balancing of robots over multiple intersections is achieved through coordination between intersection agents by communication of robot densities in different pathways. Extensive comparisons show the performance gain of the current method over existing ones. Theoretical analysis apart from simulation show the advantages of load-balanced traffic flow over uncoordinated allotment of robotic agents to pathways. Transferring the burden of coordination to the network releases more computational power for the robots to engage in critical assistive activities.