We consider the problem of multi-robot sensor coverage, which deals with deploying a multi-robot team in an environment and optimizing the sensing quality of the overall environment. As real-world environments involve a variety of sensory information, and individual robots are limited in their available number of sensors, successful multi-robot sensor coverage requires the deployment of robots in such a way that each individual team member’s sensing quality is maximized. Additionally, because individual robots have varying complements of sensors and both robots and sensors can fail, robots must be able to adapt and adjust how they value each sensing capability in order to obtain the most complete view of the environment, even through changes in team composition. We introduce a novel formulation for sensor coverage by multi-robot teams with heterogeneous sensing capabilities that maximizes each robot's sensing quality, balancing the varying sensing capabilities of individual robots based on the overall team composition. We propose a solution based on regularized optimization that uses sparsity-inducing terms to ensure a robot team focuses on all possible event types, and which we show is proven to converge to the optimal solution. Through extensive simulation, we show that our approach is able to effectively deploy a multi-robot team to maximize the sensing quality of an environment, responding to failures in the multi-robot team more robustly than non-adaptive approaches.
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Sensor Placement for Globally Optimal Coverage of 3D-Embedded Surfaces
We carry out a structural and algorithmic study
of a mobile sensor coverage optimization problem targeting 2D
surfaces embedded in a 3D workspace. The investigated settings
model multiple important applications including camera net-
work deployment for surveillance, geological monitoring/survey
of 3D terrains, and UVC-based surface disinfection for the
prevention of the spread of disease agents (e.g., SARS-CoV-
2). Under a unified general “sensor coverage” problem, three
concrete formulations are examined, focusing on optimizing
visibility, single-best coverage quality, and cumulative quality,
respectively. After demonstrating the computational intractabil-
ity of all these formulations, we describe approximation schemes
and mathematical programming models for near-optimally
solving them. The effectiveness of our methods is thoroughly
evaluated under realistic and practical scenarios.
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- PAR ID:
- 10219063
- Date Published:
- Journal Name:
- IEEE International Conference on Robotics and Automation
- ISSN:
- 1049-3492
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
