Energy-Aware Multi-Robot Task Allocation in Persistent Tasks
This addresses energy management for swarm robots in persistent tasks like foraging, which is incremental as it builds on existing task allocation methods with a focus on energy efficiency.
The paper tackles the problem of energy-aware task allocation for swarm robots performing continuous foraging tasks, proposing a distributed algorithm that minimizes transit and recharging time while maximizing robot lifetime, and shows significant performance and efficiency improvements over a greedy baseline.
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.