In this paper, we propose a distributed coverage control algorithm for mobile sensing networks that can account for bounded uncertainty in the location of each sensor. Our algorithm is capable of safely driving mobile sensors towards areas of high information distribution while having them maintain coverage of the whole area of interest. To do this, we propose two novel variants of the Voronoi diagram. The first, the convex uncertain Voronoi (CUV) diagram, guarantees full coverage of the search area. The second, collision avoidance regions (CARs), guarantee collision-free motions while avoiding deadlock, enabling sensors to safely and successfully reach their goals. We demonstrate the efficacy of these algorithms via a series of simulations with different numbers of sensors and uncertainties in the sensors’ locations. The results show that sensor networks of different scales are able to safely perform optimized distribution corresponding to the information distribution density under different localization uncertainties
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Collision-Free Distributed Multi-Target Tracking Using Teams of Mobile Robot with Localization Uncertainty
Accurately tracking dynamic targets relies on robots accounting for uncertainties in their own states to share information and maintain safety. The problem becomes even more challenging when there are an unknown and time-varying number of targets in the environment. In this paper we address this problem by introducing four new distributed algorithms that allow large teams of robots to: i) run the prediction and ii) update steps of a distributed recursive Bayesian multitarget tracker, iii) determine the set of local neighbors that must exchange data, and iv) exchange data in a consistent manner. All of these algorithms account for a bounded level of localization uncertainty in the robots by leveraging our recent introduction of the convex uncertainty Voronoi (CUV) diagram, which extends the traditional Voronoi diagram to account for localization uncertainty. The CUV diagram introduces a tessellation over the environment, which we use in this work both to distribute the multi-target tracker and to make control decisions about where to search next. We examine the efficacy of our method via a series of simulations and compare them to our previous work which assumed perfect localization.
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- Award ID(s):
- 1830419
- NSF-PAR ID:
- 10194716
- Date Published:
- Journal Name:
- Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems
- ISSN:
- 2153-0858
- 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.
