Distributed Heterogeneous Multi-robot Task Allocation in Communication-limited Environments
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Author(s)
Chen, Shengkang
Advisor(s)
Arkin, Ronald C.
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Abstract
Effective coordination enables heterogeneous multi-robot systems to complete a wide range of missions effectively. Multi-robot task allocation (MRTA), a key component of this coordination, aims to assign tasks to robots appropriately. Given that communication can be unreliable or limited in real-world settings, it is crucial to develop task allocation strategies for these situations.
This dissertation introduces two heterogeneous task allocation approaches for different scenarios using the Speeding-Up and Slowing-Down (SUSD) strategy [1], a derivative-free optimization technique that achieves convergence toward the gradient direction through local function evaluations. SUSD is suitable for MRTA problems, which can be treated as integer programming issues with ill-defined gradients, as it does not require explicit gradient calculations.
In the first scenario, each task is completed by a single robot, with a base station available. We present a hybrid SUSD-based task allocation algorithm. Initially, robots use a market-based task allocation algorithm, well-suited for environments with limited communication, to generate initial allocation results. The base station then employs the SUSD strategy to improve these results. Testing in simulated underwater sensing missions demonstrated significant improvement in total makespan (mission completion time) for medium-scale problems (fewer than 20 robots and 50 tasks) compared to existing market-based approaches.
In the second scenario, multiple robots cooperatively complete tasks, with each robot able to select multiple tasks. To avoid complex coordination mechanisms, we formulate the task allocation as a game where robots share their task selections and update them to minimize individual costs. We developed a distributed task selection algorithm using the SUSD strategy, enabling convergence toward a Nash equilibrium. Aligning robots' cost functions with collective goals ensures effective coordination. Validation through simulated underwater cooperative missions showed significant energy consumption reduction for medium-scale problems compared to existing methods.
[1] Al-Abri, S., Lin, T. X., Tao, M., & Zhang, F. (2021). A Derivative-Free Optimization Method with Application to Functions with Exploding and Vanishing Gradients. IEEE Control Systems Letters, 5(2), 587–592.
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Date
2024-07-12
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Dissertation