Luke B. Johnson

Distributed Planning Strategies to Ensure Network Connectivity for Dynamic Heterogeneous Teams

This paper presents a cooperative distributed planning algorithm that ensures network connectivity for a team of heterogeneous agents operating in dynamic and communication-limited environments. The algorithm, named CBBA with Relays, builds on the Consensus-Based Bundle Algorithm (CBBA), a distributed task allocation framework developed previously by the authors and their colleagues. Information available through existing consensus phases of CBBA is leveraged to predict the network topology and to propose relay tasks to repair connectivity violations. The algorithm ensures network connectivity during task execution while preserving the distributed and polynomial-time guarantees of CBBA. By employing under-utilized agents as communication relays, CBBA with Relays improves the range of the team without limiting the scope of the active agents, thus improving mission performance. The algorithm is validated through simulation trials and through experimental indoor and outdoor field tests, demonstrating the real-time applicability of the approach.

Decentralized task allocation with coupled constraints in complex missions

This paper presents a decentralized algorithm that creates feasible assignments for a network of autonomous agents in the presence of coupled constraints. The coupled constraints address complex mission characteristics that include assignment relationships, where the value of a task is conditioned on whether or not another task has been assigned, and temporal relationships, where the value of a task is conditioned on when it is performed relative to other tasks. The new algorithm is developed as an extension to the Consensus-Based Bundle Algorithm (CBBA), introducing the notion of pessimistic or optimistic bidding strategies and the relative timing constraints between tasks. This extension, called Coupled-Constraint CBBA (CCBBA), is compared to the baseline in a complex mission simulation and is found to outperform the baseline, particularly for task-rich scenarios.

Dynamic Mission Planning for Communication Control in Multiple Unmanned Aircraft Teams

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.

Improving the Eciency of a Decentralized Tasking Algorithm for UAV Teams with Asynchronous Communications

This work presents a decentralized task allocation algorithm for networked agents communicating through an asynchronous channel. The algorithm extends the Consensus-Based Bundle Algorithm (CBBA) to account for more realistic asynchronous communication protocols. Direct implementation of CBBA into such an asynchronous setting requires agents to frequently broadcast their information states, which would cause signicant communication overow. In contrast, the extension proposed in this paper, named Asynchronous CBBA (ACBBA), minimizes communication load while preserving the convergence properties. ACBBA applies a new set of local deconiction rules that do not require access to the global in

Asynchronous Decentralized Task Allocation for Dynamic Environments

This work builds on a decentralized task allocation algorithm for networked agents communicating through an asynchronous channel, by extending the Asynchronous ConsensusBased Bundle Algorithm (ACBBA) to account for more real time implementation issues resulting from a decentralized planner. This paper specically talks to the comparisons between global and local convergence in asynchronous consensus algorithms. Also a feature called asynchronous replan is introduced to ACBBA’s functionality that enables ecient updates to large changes in local situational awareness. A real-time software implementation using multiple agents communicating through the user datagram protocol (UDP) validates the proposed algorithm.

Distributed chance-constrained task allocation for autonomous multi-agent teams

This research presents a distributed chance-constrained task allocation framework that can be used to plan for multi-agent networked teams operating in stochastic and dynamic environments. The algorithm employs an approximation strategy to convert centralized problem formulations into distributable sub-problems that can be solved by individual agents. A key component of the distributed approximation is a risk adjustment method that allocates individual agent risks based on a global risk threshold. The results show large improvements in distributed stochastic environments by explicitly accounting for uncertainty propagation during the task allocation process.

Allowing non-submodular score functions in distributed task allocation

Submodularity is a powerful property that can be exploited for provable performance and convergence guarantees in distributed task allocation algorithms. However, some mission scenarios cannot easily be approximated as submodular a priori. This paper introduces an algorithmic extension for distributed multi-agent multi-task assignment algorithms which provides guaranteed convergence using non-submodular score functions. This algorithm utilizes non-submodular ranking of tasks within each agents internal decision making process, while externally enforcing that shared bids appear as if they were created using submodular score functions. Provided proofs demonstrate that all convergence and performance guarantees hold with respect to this apparent submodular score function. The algorithm allows significant improvements over heuristic approaches that approximate truly non-submodular score functions.

Multi-UAV network control through dynamic task allocation: Ensuring data-rate and bit-error-rate support

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.

Ensuring Network Connectivity for Decentralized Planning in Dynamic Environments

This work addresses the issue of network connectivity for a team of heterogeneous agents operating in a dynamic environment. The Consensus-Based Bundle Algorithm (CBBA), a distributed task allocation framework previously developed by the authors and their colleagues, is introduced as a methodology for complex mission planning, and extensions are proposed to address limited communication environments. In particular, CBBA with Relays leverages information available through already existing consensus phases to predict the network topology at select times and creates relay tasks to strengthen the connectivity of the network. By employing underutilized resources, the presented approach improves network connectivity without limiting the scope of the active agents, thus improving mission performance.

Hybrid Information and Plan Consensus in Distributed Task Allocation

Many missions envisioned in future operations will require that teams of autonomous agents (e.g., unmanned aerial vehicles) maintain a high degree of coordination to efficiently execute required tasks. Achieving these desired levels of coordination will be particularly challenging in contested environments in which the communications may, as a result of jamming, environment conditions, or terrain, be unavailable, unreliable, have high latency, or high cost. This paper introduces a new planning paradigm that combines plan consensus with implicit coordination to enable a higher degree of coordination and produce plans faster than would be available by either method separately. Results in the paper show reductions in number of messages, convergence iterations and number of overall plan conflicts during the algorithmic convergence process for certain planning environments.