A multi-UAV system relies on communications to operate. Failure to communicate remotely sensed mission data to the base may render the system ineffective, and the inability to exchange command and control messages can lead to system failures. This paper describes a unique method to control network communications through distributed task allocation to engage under-utilized UAVs to serve as communication relays and to ensure that the network supports mission tasks. This work builds upon a distributed algorithm previously developed by the authors, CBBA with Relays, which uses task assignment information, including task location and proposed execution time, to predict the network topology and plan support using relays. By explicitly coupling task assignment and relay creation processes, the team is able to optimize the use of agents to address the needs of dynamic complex missions. In this work, the algorithm is extended to explicitly consider realistic network communication dynamics, including path loss, stochastic fading, and information routing. Simulation and flight test results validate the proposed approach, demonstrating that the algorithm ensures both data-rate and interconnectivity bit-error-rate requirements during task execution.
This paper presents the outdoor flight test results of a decentralized multi-UAV system supervised by a human operator. The system balances the roles of the human operator and the UAV autonomous behaviors with the objective of maximizing the execution performance. The operator manages the mission by inputting and modifying tasks instead of controlling individual UAVs. The Consensus-Based Bundle Algorithm (CBBA) is used as a real-time, scalable, dynamic multi-agent multi-task planning algorithm to allocate tasks approved by the operator to UAVs. A team of three quadrotors and one fixed wing UAV collaborated in an operationally relevant scenario supporting a cargo UAV resupply mission. Thirteen of fourteen multi-UAV outdoor flight test trials successfully accomplished the mission objectives. The framework was shown to be robust to system failures and degradations commonly encountered during field testing primarily because of health monitoring and management tools that were incorporated in the design. Instances of task allocation and path planning churning were observed which are linked to uncertainties of operating outdoors. Lessons learned during flight test operations are highlighted as they are relevant to other similar types of systems and missions.
Abstract-A multi-UAV system relies on communications to operate. Failure to communicate remotely sensed mission data to the base may render the system ineffective, and the inability to exchange command and control messages can lead to system failures. This paper describes a unique method to control communications through distributed task allocation to engage under-utilized UAVs to serve as communication relays and to ensure that the network supports mission tasks. The distributed algorithm uses task assignment information, including task location and proposed execution time, to predict the network topology and plan support using relays. By explicitly coupling task assignment and relay creation processes the team is able to optimize the use of agents to address the needs of dynamic complex missions. The framework is designed to consider realistic network communication dynamics including path loss, stochastic fading, and information routing. The planning strategy is shown to ensure that agents support both datarate and interconnectivity bit-error-rate requirements during task execution. System performance is characterized through experiments both in simulation and in outdoor flight testing with a team of three UAVs.
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