Consensus-Based Auction Approaches for Decentralized Task Assignment

Abstract

This paper addresses task assignment in the coordination of a fleet of unmanned vehicles by presenting two decentralized algorithms: consensus-based auction algorithm (CBAA) and its generalization to the multi-assignment problem, consensus-based bundle algorithm (CBBA). These algorithms utilize a market-based decision strategy as the mechanism for decentralized task selection, and use a consensus routine based on local communication as the conflict resolution mechanism by achieving agreement on the winning bid values. The conflict resolution process of CBBA is further enhanced to address the dependency of the score value on the previously selected tasks in the multi-assignment setting. This work shows that the proposed algorithms, under reasonable assumptions on the scoring scheme and network connectivity, guarantee convergence to a conflict-free assignment. Also, the converged solutions are shown to guarantee 50% optimality in the worst-case and to exhibit provably good performance on average. Moreover, the proposed algorithms produce a feasible assignment even in the presence of inconsistency in situational awareness across the fleet, and even when the score functions varies with time in some standard manner. Numerical experiments verify quick convergence and good performance of the presented methods for both static and dynamic assignment problems.

Fast Scheduling of Multi-Robot Teams with Temporospatial Constraints

“…Auction methods (e.g. [5]) also rely on decomposition of problem structure and treat the optimization of each agent's schedule as independent of the other agents' schedules. These techniques preclude explicit coupling in each agent's contribution to the MILP objective function.…”

“…For example, if a task is originally designated to take between five and fifteen minutes but the assigned robot can complete it no faster than ten minutes, we tighten the interval from [5,15] to [10,15].…”

“…Fast computation is desirable because it provides the capability for on-the-fly replanning in response to schedule disturbances [2], [6], [27]. These works boost computational performance by decomposing plan constraints and contributions to the objective function among agents [5]. However, these methods break down when agents' schedules become tightly intercoupled, as they do when multiple agents are maneuvering in close physical proximity.…”

“…Various decentralized or distributed approaches achieve fast computation and good scalability characteristics [5], [6], [9], [24], [31]. Fast computation is desirable because it provides the capability for on-the-fly replanning in response to schedule disturbances [2], [6], [27].…”

A method of planning vehicle group movement using dynamic repellers and task assignment

“…В [24] для гетерогенной группы беспилотных летательных аппаратов (БЛА) рассматривается задача целераспределения в динамических и неопре-деленных средах в реальном масштабе времени с применением гибридного алгоритма аукциона и согласования из [25]. В данном подходе каждый БЛА присваивает каждой цели обновляющееся с течением времени значение, ко-торое определяется как разность между доходом, получаемым БЛА при вы-боре данной цели, и глобальной ценой за эту цель.…”