Distributed Online Task Assignment via Inexact ADMM for unplanned online tasks and its Applications to Security

TL;DR

This paper introduces a distributed, optimization-based task assignment method for multi-robot systems that handles unplanned online tasks securely using inexact ADMM, security analysis, and control barrier functions.

Contribution

It presents a novel inexact ADMM-based distributed algorithm for dynamic task assignment with security considerations in multi-robot systems.

Findings

Effective online task handling demonstrated in simulations.

Maintains security guarantees during task reallocation.

Enables secure response to unplanned tasks in multi-robot systems.

Abstract

In multi-robot system (MRS) applications, efficient task assignment is essential not only for coordinating agents and ensuring mission success but also for maintaining overall system security. In this work, we first propose an optimization-based distributed task assignment algorithm that dynamically assigns mandatory security-critical tasks and optional tasks among teams. Leveraging an inexact Alternating Direction Method of Multipliers (ADMM)-based approach, we decompose the task assignment problem into separable and non-separable subproblems. The non-separable subproblems are transformed into an inexact ADMM update by projected gradient descent, which can be performed through several communication steps within the team. In the second part of this paper, we formulate a comprehensive framework that enables MRS under plan-deviation attacks to handle online tasks without compromising security. The process begins with a security analysis that determines whether an online task can be executed securely by a robot and, if so, the required time and location for the robot to rejoin the team. Next, the proposed task assignment algorithm is used to allocate security-related tasks and verified online tasks. Finally, task fulfillment is managed using a Control Lyapunov Function (CLF)-based controller, while security enforcement is ensured through a Control Barrier Function (CBF)-based security filter. Through simulations, we demonstrate that the proposed framework allows MRS to effectively respond to unplanned online tasks while maintaining security guarantees.