A Distributed System for Collaboration and Control of UAV Groups: Experiments and Analysis

Mark F. Godwin (), Stephen C. Spry and J. Karl Hedrick
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Mark F. Godwin: University of California
Stephen C. Spry: University of California
J. Karl Hedrick: University of California

A chapter in Cooperative Systems, 2007, pp 139-156 from Springer

Abstract: Summary This chapter describes a distributed system for collaboration and control of a group of unmanned aerial vehicles (UAVs). The system allows a group of vehicles to work together to accomplish a mission via an allocation mechanism that works with a limited communication range and is tolerant to agent failure. This system could be used in a number of applications including mapping, surveillance, search and rescue operations. The user provides a mission plan containing a set of tasks and an obstacle map of the operating environment. An estimated mission state, described in a high level language, is maintained on each agent and shared between agents whenever possible. This language represents each task as a set of subtasks. Each subtask maintains a state with information on the subtask status, an agent ID, a timestamp, and the cost to complete the subtask. The estimated mission states are based on each agent’s current knowledge of the mission and are updated whenever new information becomes available. In this chapter, each subtask is associated with a point in space, although the system methodology can be expanded to more general subtask types. The agents employ a three-layer hierarchical decision and control process. The upper layer contains transition logic and a communication process. The transition logic manages transitions between tasks and between subtasks, which determine the behavior of the agent at any given time. The communication process manages the exchange of mission state information between agents. Among other capabilities, the subtask transition rules provide time-based fault management; if an agent is disabled or stops communicating, others will assume its subtask after a mission-dependent timeout period. The middle layer contains a trajectory planner that uses a modified potential field method to generate a safe trajectory for a UAV based on the obstacle map and the current subtask objective. The lower layer contains a trajectory-tracking controller that produces heading and airspeed commands for the UAV. Properties of the system are analyzed and the methodology is illustrated through an example mission simulation.

Keywords: Transition Logic; Multiagent System; Unmanned Aerial Vehicle; Mission State; Transition Rule (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1007/978-3-540-48271-0_9

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