Agent‐based simulation framework and consensus algorithm for observing systems with adaptive modularity

Abstract

In this era of the “big data revolution,” the desired capabilities of Earth Observing Systems are growing fast: we need ever more frequent data sets, covering a larger part of the frequency spectrum, with lower latency, and higher spatial resolution. To better address these needs, the space systems community has been exploring the value of shifting from highly monolithic architectures, in which large and isolated spacecraft carry multiple instruments with synergistic and complementary goals, toward more distributed architectures, where the functions of these large systems are partitioned into a larger number of smaller satellites. In this paper, we present an agent‐based simulation framework that can help systems engineers assess whether or not it makes sense to be able to “change system modularity during operations” by means of temporary coalitions. Systems of observing autonomous vehicles work together to perform a set of observational tasks. The vehicles can decide to form physical coalitions with other vehicles for collective sensing of a target, when no agent alone can carry out the task, or individual observation results in degraded satisfaction. The framework extends the well‐known decentralized Coupled‐Constraint Consensus‐Based Bundle Algorithm to multivehicle single‐task allocation and introduces constraints on the formation of coalitions, so that agents can create or split a coalition depending on the benefits and costs associated with these actions. The framework is described in detail and demonstrated on a case study.

Anytime and Efficient Multi-agent Coordination for Disaster Response

“…Since no current algorithm that solves the CFSTP is simultaneously anytime, efficient and with convergence guarantee ("Introduction"), CTS is the first of its kind. (11) d max ⋅ (|V| + |A| log |A|) .…”

“…Nonetheless, since both use binary decision variables, they require a pre-processing phase with exponential time to solve CFSTP instances with n-ary decision variables. Multi-agent approaches that solve problems similar to the CFSTP make use of social insects [8], automated negotiation [11,12,39] and evolutionary computation [41], but without considering the anytime property. In the iTax taxonomy of Korsah et al [20], the CFSTP is defined as a Cross-schedule Dependent Single-Task Multi-Robot Time-extended Assignment (XD [ST-MR-TA]) problem [20].…”

A survey of task allocation techniques in MAS

“…The recent approaches found include improvements of the PI (performance impact) algorithm, like PI-MaxAss [14] and [35]. Moreover, other techniques are improvements of the CBBA algorithm, like modified CCBBA [38], G-CBBA [40] and [41].…”

Real-Time Optimal Negotiation Mode Selection Based on Three-Way Decision for Decentralized Remote Sensing Satellite Cluster

“…Iacopino et al [16] designed an innovative self-organizing multiagent ground-based automated planning and scheduling architecture, inspired by ant colony optimization algorithms. Gallud and Selva [17] presented an agent-based simulation framework. The systems of observing autonomous vehicles performing a set of observational tasks were verified on the framework.…”