Resilient Active Information Gathering with Mobile Robots

Abstract

Applications in robotics, such as multi-robot target tracking, involve the execution of information acquisition tasks by teams of mobile robots. However, in failure-prone or adversarial environments, robots get attacked, their communication channels get jammed, and their sensors fail, resulting in the withdrawal of robots from the collective task, and, subsequently, the inability of the remaining active robots to coordinate with each other. As a result, traditional design paradigms become insufficient and, in contrast, resilient designs against system-wide failures and attacks become important. In general, resilient design problems are hard, and even though they often involve objective functions that are monotone and (possibly) submodular, scalable approximation algorithms for their solution have been hitherto unknown. In this paper, we provide the first algorithm, enabling the following capabilities: minimal communication, i.e., the algorithm is executed by the robots based only on minimal communication between them; system-wide resiliency, i.e., the algorithm is valid for any number of denial-of-service attacks and failures; and provable approximation performance, i.e., the algorithm ensures for all monotone and (possibly) submodular objective functions a solution that is finitely close to the optimal. We support our theoretical analyses with simulated and realworld experiments, by considering an active information acquisition application scenario, namely, multi-robot target tracking. ).This research is partially supported by ARL CRA DCIST W911NF-17-2-0181 and the Rockefeller Foundation.• (Problem definition) We formalize the problem of resilient active information gathering with mobile robots against multi-robot denial-of-service attacks or failures. arXiv:1803.09730v3 [cs.RO] 2 Sep 2018 APPENDIX A PRELIMINARY LEMMAS AND DEFINITIONSNotation. In the appendix we use the following notation to support the proofs in this paper: in particular, consider a finite ground set V and a set function f : 2 V → R. Then, for any set X ⊆ V and any set X ⊆ V, the symbol f (X |X ) denotes the marginal value f (X ∪ X ) − f (X ). Moreover, the symbol κ f is the total curvature of f (Definition 3), and the symbol c f is the total curvature of f (Definition 4).This appendix contains lemmas that will support the proof of Theorem 1 in this paper; moreover, it contains a generalized description of the algorithm coordinate descent [20, Section IV] (to any non-decreasing information objective function in the active robot set), and a lemma, which will support the proof of Proposition 1 in this paper.

Multi-robot Coordination and Planning in Uncertain and Adversarial Environments

“…To this end, Tzoumas et al presented the first scalable resilient algorithm to counter any number of adversarial (specifically, denial-of-service) attacks or failures [69]. Building on this work, recent studies have investigated designing resilient information collection algorithms in target tracking [4] and information gathering scenarios [15]. Particularly, the resilient target tracking algorithm proposed by Zhou et al [4] guarantees a provably close-to-optimal team performance even though some robots in the team are attacked and their tracking cameras are blocked (Figure 1).…”

“…For example, an adversary can attack the system by spoofing fake identities [13] or sharing incorrect information [14 ••]. An adversary may also choose specific robots to attack so that the team encounters a worst-case loss in the performance [15]. This is especially crucial in applications such as surveillance and security where counter-unmanned aerial vehicle strategies are being developed [16].…”

“…Resilient coordination algorithms have shown the effectiveness in many robotics tasks, such as team formation [18], target estimation [21 •], and data collection [4,15]. 2.…”

Resilient Active Target Tracking With Multiple Robots

“…Overall, in this paper we go beyond non-resilient target tracking [5]- [12] by proposing resilient target tracking; and beyond resilient tracking with distinguishable and known targets [18] by proposing resilient tracking with targets that are (possibly) indistinguishable, and/or unknown.…”

Obstacle detection for autonomous systems using stereoscopic images and bacterial behaviour

“…Active robotic sensors have today become a high-performance tool with great acceptance at commercial and military level [1,2]. These are embedded systems equipped with sensors that provide specific primary data, from which a real-time processor produces information relevant to the tasks of the robot [3]. This kind of sensors has promoted research in information-driven strategies for the development of tasks with robots, as well as the implementation of algorithms for digital signal processing and control schemes oriented to these sensors [4].…”