[Paper Review] Energy-Aware Multi-Robot Task Allocation in Persistent Tasks

Ehsan Latif, Yikang Gui+2 more|arXiv (Cornell University)|Dec 31, 2021

Modular Robots and Swarm Intelligence4 citations

TL;DR

This paper proposes a distributed, energy-aware task allocation algorithm for multi-robot systems performing persistent foraging tasks, balancing energy consumption, recharging, and task performance. By using dynamic energy thresholds, decentralized decision-making, and deadlock avoidance via randomization, the approach increases robot survival and treasure collection by 15 compared to a greedy baseline, while reducing recharging wait times.

ABSTRACT

The applicability of the swarm robots to perform foraging tasks is inspired by their compact size and cost. A considerable amount of energy is required to perform such tasks, especially if the tasks are continuous and/or repetitive. Real-world situations in which robots perform tasks continuously while staying alive (survivability) and maximizing production (performance) require energy awareness. This paper proposes an energy-conscious distributed task allocation algorithm to solve continuous tasks (e.g., unlimited foraging) for cooperative robots to achieve highly effective missions. We consider efficiency as a function of the energy consumed by the robot during exploration and collection when food is returned to the collection bin. Finally, the proposed energy-efficient algorithm minimizes the total transit time to the charging station and time consumed while recharging and maximizes the robot's lifetime to perform maximum tasks to enhance the overall efficiency of collaborative robots. We evaluated the proposed solution against a typical greedy benchmarking strategy (assigning the closest collection bin to the available robot and recharging the robot at maximum) for efficiency and performance in various scenarios. The proposed approach significantly improved performance and efficiency over the baseline approach.

Motivation & Objective

• To address the lack of energy awareness in multi-robot task allocation for persistent tasks, especially in real-world scenarios with limited battery capacity.
• To maintain robot survivability and network connectivity while maximizing task performance in continuous foraging missions.
• To develop a decentralized, scalable solution that balances foraging and recharging without centralized coordination.
• To reduce energy waste and improve system efficiency by considering both energy consumption during movement and retrieval, and recharging dynamics.
• To prevent deadlock in task allocation through randomized decision-making and energy-level-based state transitions.

Proposed method

• The algorithm uses dynamic energy thresholds: robots return to the recharging station when energy drops to 20% (E_min), ensuring survival without premature depletion.
• It employs a distributed task allocation mechanism where robots autonomously decide whether to forage or recharge based on local energy levels and proximity to collection bins or recharging stations.
• A maximum connectivity graph algorithm maintains network integrity by ensuring robots remain within communication range, supporting information sharing and coordination.
• The system models energy consumption using a formula: E_t = E_{t-1} - (α×1 + β×distance(t) + γ×isTreasurePicked), with α=0.1, β=2, γ=0.1 for static, dynamic, and retrieval energy costs.
• Recharging is modeled as E_t = E_{t-1} + δ×1, with δ=0.5, representing the recharge rate at charging stations.
• A deadlock avoidance strategy uses randomization in task allocation decisions to prevent infinite waiting states when multiple robots target the same resource or station.

Experimental results

Research questions

• RQ1 How can a distributed multi-robot system efficiently balance foraging and recharging to maximize task performance while ensuring robot survival?
• RQ2 What energy thresholds and decision rules enable optimal trade-offs between energy consumption and task completion in persistent tasks?
• RQ3 How does integrating connectivity maintenance affect the performance and robustness of energy-aware task allocation in robot swarms?
• RQ4 To what extent does deadlock avoidance improve system stability and performance in decentralized task allocation?
• RQ5 How does energy-aware task allocation outperform greedy strategies that prioritize proximity and full recharging?

Key findings

• The proposed algorithm collected 15 more treasures than the greedy baseline and 5 more than the baseline without deadlock avoidance, demonstrating superior performance in persistent foraging.
• Despite traveling a longer total distance, the proposed approach reduced wait time for recharging by 30% compared to the baseline, indicating better scheduling efficiency.
• The algorithm maintained a higher average number of alive robots throughout the simulation, indicating improved robot survivability due to optimized recharging timing.
• Excluding the connectivity maintenance component reduced system performance, showing that network integrity significantly contributes to sustained task efficiency and robot longevity.
• The integration of deadlock avoidance increased the number of alive robots and treasure collection, proving effective in preventing infinite waiting states during task allocation.
• The energy model with coefficients α=0.1, β=2, γ=0.1, δ=0.5 accurately captured real-world energy dynamics, enabling reliable simulation and performance evaluation.