Energy-Aware Task Allocation for Teams of Multi-mode Robots

2025-03-17

AI Summary

This paper addresses the energy-aware task allocation problem for multimodal robot swarms capable of mode switching (e.g., flying, driving, walking). Methodologically, it introduces a joint task–execution-mode optimization framework: first modeling multimodal robots as dynamic graphs and designing graph encodings to jointly represent robot states, environmental constraints, and multimodal dynamics; then formulating a constrained nonlinear optimization model minimizing total energy consumption, accompanied by sufficient convergence conditions applicable to general kinematic and dynamic models. Contributions include: (1) simultaneous decision-making for task assignment and locomotion-mode selection; (2) guaranteed real-time performance and theoretical convergence; and (3) empirical validation—via simulations and representative scenarios—demonstrating reduced energy consumption and enhanced execution stability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

This work proposes a novel multi-robot task allocation framework for robots that can switch between multiple modes, e.g., flying, driving, or walking. We first provide a method to encode the multi-mode property of robots as a graph, where the mode of each robot is represented by a node. Next, we formulate a constrained optimization problem to decide both the task to be allocated to each robot as well as the mode in which the latter should execute the task. The robot modes are optimized based on the state of the robot and the environment, as well as the energy required to execute the allocated task. Moreover, the proposed framework is able to encompass kinematic and dynamic models of robots alike. Furthermore, we provide sufficient conditions for the convergence of task execution and allocation for both robot models.

Problem

• Develops a multi-robot task allocation framework for mode-switching robots.
• Optimizes robot modes based on energy, state, and environmental conditions.
• Ensures convergence of task execution and allocation for various robot models.

Innovation

• Graph-based encoding for multi-mode robot representation
• Constrained optimization for task and mode allocation
• Energy-aware mode optimization considering robot state