Hybrid system design for formations of autonomous vehicles

Abstract

Cooperative control of multiple unmanned aerial vehicles (UAVs) poses significant theoretical and technical challenges. Recent advances in sensing, communication and computation enable the conduct of cooperative multiple-UAV missions deemed impossible in the recent past. We are interested in solving the Formation Reconfiguration Planning (FRP) problem which is focused on determining a nominal state and input trajectory for each vehicle such that the group can start from the given initial configuration and reach its given final configuration at the specified time while satisfying a set of given iiiter-and intra-vehicle constraints. Each solution of a FRP problem represents a distinct reconfiguration mode. When coupled with formation 'keeping modes, they can form a hybrid automaton of formation maneuvers in which a transition from one formation maneuver to another formation maneuver is governed by a finite automaton. This paper focuses on the implementation of the optimized hybrid system approach to formation reconfiguration for a group of 1 real and 3 virtual UAVs. Experimental results performed in the Richmond Field Station by using a helicopter-based Berkeley Aerial Robot are presented.

On the controllability of nearest neighbor interconnections

“…The role of the interconnection topology on information flow and formation stability was investigated in a number of papers [10], [11], [12]. A hybrid system approach was adopted in [13] whereas in [14] graph theory and LMIs are being used. Location optimization problems were examined within a cooperative control framework in [15].…”

A collision-free formation reconfiguration control approach for Unmanned Aerial Vehicles

“…Many of these studies focus on the utilization of nonlinear programming and graph search in the problem of reconfiguration. For instance, [6] and [7] utilize nonlinear programming to obtain a nominal input trajectory for each vehicle that satisfies the reconfiguration parameters and at the same time the set of inter-and intra-vehicle constraints. [8] uses Voronoi graph search and a two-step path-planning algorithm to generate a stealthy path through a set of enemy radar sites of known location.…”

Agent-Based Cooperative Control

“…The method also leaves questions regarding establishing a connection between the CLF and the optimal return function. Within the realm of cooperative control of mobile robots, work has been done on formation and swarm control utilizing potential field ideas [t35,t50,t57], employing stability arguments [58,27,65,97], graph theory [21,74], invariant manifold techniques [78], decentralized approaches [54,62,55], leader-follower approaches [48], platooning [31,103,36,84,95], and hybrid and multi-modal control approaches [25,106,50,49].…”