In this paper, we focus on using path planning and inter-agent measurements to improve the security of multi-robot systems against possible takeovers from cyber-attackers. We build upon recent trajectory optimization approaches where introspective measurement capabilities of the robots are used in an co-observation schedule to detect deviations from the preordained routes. This paper proposes additional constraints that can be incorporated in the previous trajectory optimization algorithm based on Alternating Direction Method of Multipliers (ADMM). The new constraints provide guarantees that a compromised robot cannot reach a designed safety zone between observations despite adversarial movement by the attacker. We provide a simulation showcasing the new components of the formulation in a multi-agent map exploration task with several safety zones.
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Multi-Agent Path Planning Under Observation Schedule Constraints
We consider the problem of enhanced security of multi-robot systems to prevent cyber-attackers from taking control of one or more robots in the group. We build upon a recently proposed solution that utilizes the physical measurement capabilities of the robots to perform introspection, i.e., detect the malicious actions of compromised agents using other members of the group. In particular, the proposed solution finds multi-agent paths on discrete spaces combined with a set of mutual observations at specific locations to detect robots with significant deviations from the preordained routes.
In this paper, we develop a planner that works on continuous configuration spaces while also taking into account similar spatio-temporal constraints.
In addition, the planner allows for more general tasks that can be formulated as arbitrary smooth cost functions to be specified. The combination of constraints and objectives considered in this paper are not easily handled by popular path planning algorithms (e.g., sampling-based methods), thus we propose a method based on the Alternating Direction Method of Multipliers (ADMM). ADMM is capable of finding locally optimal solutions to problems involving different kinds of objectives and non-convex temporal and spatial constraints, and allows for infeasible initialization. We benchmark our proposed method on multi-agent map exploration with minimum-uncertainty cost function, obstacles, and observation schedule constraints.
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- Award ID(s):
- 1932162
- NSF-PAR ID:
- 10288860
- Date Published:
- Journal Name:
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Page Range / eLocation ID:
- 6990 to 6997
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IROS45743.2020.9340747
