A Blockchain Framework for Equitable and Secure Task Allocation in Robot Swarms
Hanqing Zhao, Alexandre Pacheco, Giovanni Beltrame, Xue Liu, Marco Dorigo, Gregory Dudek
AI summary
Problem
Existing blockchain-based swarm consensus methods assume homogeneous robots and fail to differentiate tasks based on individual capabilities, leading to inefficient workloads and poor resilience in heterogeneous systems.
Approach
The authors deploy a blockchain smart contract as a meta-controller that decomposes missions into a task hierarchy, evaluates robot performance using domain-specific metrics, and redistributes crypto assets to incentivize capability-aligned task selection.
Key results
- Equitable workload distribution across robots with varying odometry accuracy
- Enhanced landmark mapping accuracy and exploration efficiency in simulated SLAM
- Robust resilience against Byzantine and malicious robot behaviors
- Dynamic crypto asset redistribution driven by hierarchical task performance
Why it matters
Provides a secure, incentive-driven coordination mechanism for heterogeneous multi-robot systems, advancing reliable swarm robotics in fault-prone environments.
Abstract
Recent studies demonstrate the potential of blockchain to enable robots in a swarm to achieve secure consensus about the environment, particularly when robots are homogeneous and perform identical tasks. Typically, robots receive rewards for their contributions to consensus achieve- ment, but no studies have yet targeted heterogeneous swarms, in which the robots have distinct physical capabilities suited to different tasks. We present a novel framework that leverages domain knowledge to decompose the swarm mission into a hierarchy of tasks within smart contracts. This allows the robots to reach a consensus about both the environment and the action plan, allocating tasks among robots with diverse capabilities to improve their performance while maintaining security against faults and malicious behaviors. We refer to this concept as equitable and secure task allocation. Validated in Simultaneous Localization and Mapping missions, our approach not only achieves equitable task allocation among robots with varying capabilities, improving mapping accuracy and efficiency, but also shows resilience against malicious attacks.