15 research outputs found
Resilience of multi-robot systems to physical masquerade attacks
The advent of autonomous mobile multi-robot systems has driven innovation in both the industrial and defense sectors. The integration of such systems in safety-and security-critical applications has raised concern over their resilience to attack. In this work, we investigate the security problem of a stealthy adversary masquerading as a properly functioning agent. We show that conventional multi-agent pathfinding solutions are vulnerable to these physical masquerade attacks. Furthermore, we provide a constraint-based formulation of multi-agent pathfinding that yields multi-agent plans that are provably resilient to physical masquerade attacks. This formalization leverages inter-agent observations to facilitate introspective monitoring to guarantee resilience.Accepted manuscrip
A Competitive Analysis of Online Multi-Agent Path Finding
We study online Multi-Agent Path Finding (MAPF), where new agents are
constantly revealed over time and all agents must find collision-free paths to
their given goal locations. We generalize existing complexity results of
(offline) MAPF to online MAPF. We classify online MAPF algorithms into
different categories based on (1) controllability (the set of agents that they
can plan paths for at each time) and (2) rationality (the quality of paths they
plan) and study the relationships between them. We perform a competitive
analysis for each category of online MAPF algorithms with respect to
commonly-used objective functions. We show that a naive algorithm that routes
newly-revealed agents one at a time in sequence achieves a competitive ratio
that is asymptotically bounded from both below and above by the number of
agents with respect to flowtime and makespan. We then show a counter-intuitive
result that, if rerouting of previously-revealed agents is not allowed, any
rational online MAPF algorithms, including ones that plan optimal paths for all
newly-revealed agents, have the same asymptotic competitive ratio as the naive
algorithm, even on 2D 4-neighbor grids. We also derive constant lower bounds on
the competitive ratio of any rational online MAPF algorithms that allow
rerouting. The results thus provide theoretical insights into the effectiveness
of using MAPF algorithms in an online setting for the first time.Comment: Published at ICAPS 202
Multi AGV coordination tolerant to communication failures
Most path planning algorithms used presently in multi-robot systems are based on offline planning. The Timed Enhanced A* (TEA*) algorithm gives the possibility of planning in real
time, rather than planning in advance, by using a temporal estimation of the robot’s positions at
any given time. In this article, the implementation of a control system for multi-robot applications
that operate in environments where communication faults can occur and where entire sections of
the environment may not have any connection to the communication network will be presented.
This system uses the TEA* to plan multiple robot paths and a supervision system to control communications. The supervision system supervises the communication with the robots and checks
whether the robot’s movements are synchronized. The implemented system allowed the creation
and execution of paths for the robots that were both safe and kept the temporal efficiency of the TEA*
algorithm. Using the Simtwo2020 simulation software, capable of simulating movement dynamics
and the Lazarus development environment, it was possible to simulate the execution of several
different missions by the implemented system and analyze their results.info:eu-repo/semantics/publishedVersio
Travel time models for the Rack-moving Mobile Robot System
The rack-moving mobile robot (RMMR) system is a special parts-to-picker automated warehousing system that uses hundreds of rack-moving machines to accomplish the repetitive tasks of storing and retrieving parts by lifting and transporting unit racks autonomously. This paper investigates the operation cycle of the rack-moving machine for storage and retrieval from the perspective of the lane depth, especially exploring the particularity of the RMMR system in multi-deep lanes, and proposes expected travel time models of the rack-moving machine for single- and multi-deep layouts of the RMMR system. To validate the effectiveness of the proposed models, an experimental simulation was conducted with a 1–4-deep layout under six scenarios of different numbers of aisles and layers, and results were compared with results obtained using proposed models. The paper presents useful guidelines for the configuration of the RMMR system layout including the determination of the optimal lane depth