Resilient Active Target Tracking with Multiple Robots

by   Lifeng Zhou, et al.

The problem of target tracking with multiple robots consists of actively planning the motion of the robots to track the targets. A major challenge for practical deployments is to make the robots resilient to failures. In particular, robots may be attacked in adversarial scenarios, or their sensors may fail or get occluded. In this paper, we introduce planning algorithms for multi-target tracking that are resilient to such failures. In general, resilient target tracking is computationally hard. Contrary to the case where there are no failures, no scalable approximation algorithms are known for resilient target tracking when the targets are indistinguishable, or unknown in number, or with unknown motion model. In this paper we provide the first such algorithm, that also has the following properties: First, it achieves maximal resiliency, since the algorithm is valid for any number of failures. Second, it is scalable, as our algorithm terminates with the same running time as state-of-the-art algorithms for (non-resilient) target tracking. Third, it provides provable approximation bounds on the tracking performance, since our algorithm guarantees a solution that is guaranteed to be close to the optimal. We quantify our algorithm's approximation performance using a novel notion of curvature for monotone set functions subject to matroid constraints. Finally, we demonstrate the efficacy of our algorithm through MATLAB and Gazebo simulations, and a sensitivity analysis; we focus on scenarios that involve a known number of distinguishable targets.


Resilient Non-Submodular Maximization over Matroid Constraints

Applications in control, robotics, and optimization motivate the design ...

Active Information Acquisition under Arbitrary Unknown Disturbances

Trajectory optimization of sensing robots to actively gather information...

Robust Multi-Robot Active Target Tracking Against Sensing and Communication Attacks

The problem of multi-robot target tracking asks for actively planning th...

Resilient Active Information Gathering with Mobile Robots

Applications in robotics, such as multi-robot target tracking, involve t...

Adaptive and Risk-Aware Target Tracking with Heterogeneous Robot Teams

We consider a scenario where a team of robots with heterogeneous sensors...

Decentralized Risk-Aware Tracking of Multiple Targets

We consider the setting where a team of robots is tasked with tracking m...

Resilient Monotone Sequential Maximization

Applications in machine learning, optimization, and control require the ...

Please sign up or login with your details

Forgot password? Click here to reset