Virtual Spring-Based 3D Multi-Agent Group Coordination

Abstract

As future personal vehicles start enjoying the ability to fly, tackling safe transportation coordination can be a tremendous task, far beyond the current challenge on radar screen monitoring of the already saturated air traffic control. Our focus is on the distributed safe-distance coordination among a group of autonomous flying vehicle agents, where each follows its own current straight-line direction in a 3D space with variable speeds. A virtual spring-based model is proposed for the group coordination. Within a specified neighborhood radius, each vehicle forms a virtual connection with each neighbor vehicle by a virtual spring. As the vehicle changes its position, speed and altitude, the total resultant forces on each virtual spring try to maintain zero by moving to the mechanical equilibrium point. The agents then add the simple total virtual spring constraints to their movements to determine their next positions individually. Together, the multi-agent vehicles reach a group behavior, where each of them keeps a minimal safe-distance with others. A new safe behavior thus arises in the group level. With the proposed virtual spring coordination model, the vehicles need no direct communication with each other, require only minimum local processing resources, and the control is completely distributed. New behaviors can now be formulated and studied based on the proposed model, e.g., how a fast driving vehicle can find its way though the crowd by avoiding the other vehicles effortlessly 1 .

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

“…However, new connectivity models may be needed for topology control in such harsh environments to guarantee efficient communication amongst mobile drones. Also, augmenting the techniques in [187,188] [ 189,190], through combining topology control problems with target coverage and tracking is indeed key for improved performance of the multi-UAV system. Also, leveraging MEC for video analysis and processing in a cross-layer design to optimize end-to-end delay and energy consumption of multi-UAV systems is key.…”

“…To ensure coordination and intra-swarm navigation among deployed UAVs, several approaches of swarm models are presented in the literature [160,[187][188][189][190]. However, these approaches are meant to maintain certain formations among the UAVs with collision avoidance, but do not consider network connectivity nor distributed coverage.…”