Entries by Lily Hoot

Resilience in multi-robot multi-target tracking with unknown number of targets through reconfiguration

A recent paper by members of the DCIST alliance addresses the problem of maintaining resource availability in a networked multi-robot team performing distributed tracking of an unknown number of targets. Robots receive and process sensor measurements locally and exchange information to cooperatively track a set of targets using a distributed Probability Hypothesis Density (PHD) filter. […]

Distributed Certifiably Correct Pose-Graph Optimization

Recent work by members of the DCIST alliance presents the first certifiably correct algorithm for distributed pose-graph optimization (PGO), the backbone of modern collaborative simultaneous localization and mapping (CSLAM) and camera network localization (CNL) systems. The proposed method is based upon a sparse semidefinite relaxation that provably provides globally-optimal PGO solutions under moderate measurement noise […]

Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems

Recent work by members of the DCIST alliance presents Kimera-Multi, a multi-robot system that (i) is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures resulting from perceptual aliasing, (ii) is fully distributed and only relies on local (peer-to-peer) communication to achieve distributed localization and mapping, and (iii) builds a globally […]

Learning and Leveraging Environmental Features to Improve Robot Awareness

A recent paper by the members of the DCIST alliance studies how global dynamics and knowledge of high-level features can inform decision-making for robots in flow-like environments. Specifically, they investigate how coherent sets, an environmental feature found in these environments, inform robot awareness within these scenarios. The proposed approach is an online environmental feature generator […]

Coding for Distributed Multi-Agent Reinforcement Learning

A recent paper by the members of the DCIST alliance develops a multi-agent reinforcement learning (MARL) algorithm which uses coding theory to mitigate straggler effects in distributed training. Stragglers are delayed, non-responsive or compromised compute nodes, which occur commonly in distributed learning systems, due to communication bottlenecks and adversarial conditions. Coding techniques have been utilized […]

Intermittent Interactions on Multi-Agent Systems: Diffusion of Information and Consensus Control

Recent works by members of the DCIST alliance investigate methods to handle consensus and broadcast of information tasks in networks of mobile robots subject to intermittent communication. The effort of this work is to alleviate the restriction of an all-time connected network, letting agents interact periodically and taking into consideration the uncertainty associated with those […]

Intermittently Connected Mobile Robot Networks with Information Propagation Guarantees

DCIST researchers pioneered strategies for teams of mobile robots to form intermittently connected communication networks by leveraging their mobility.  Robots assigned to monitor and patrol large urban environments can leverage their movements to carry information to other robots that are not within their communication ranges. Our work shows intermittent connectivity between pairs of robots can […]

Learning Connectivity-Maximizing Network Configurations

A recent paper by members of the DCIST alliance develops a data-driven method for providing mobile wireless infrastructure on demand to multi-robot teams requiring communication in order to collaboratively achieve a common objective. While a considerable amount of research has been devoted to this problem, existing solutions do not scale in a manner suitable for […]

Dynamic Defender-Attacker Resource Allocation Game

A recent paper by members of the DCIST alliance proposes a new resource allocation game that studies a dynamic, adversarial resource allocation problem in environments modeled as graphs. By combining ideas from Colonel Blotto games with a population dynamics model, the proposed formulation incorporates: (i) dynamic reallocation in time-varying situations, and (ii) the presence of […]

Cooperative Systems Design in Adversarial Environments

The Colonel Blotto game describes a scenario where two opposing Colonels strategically allocate their limited resources across multiple battlefields. The game is compelling for a multitude of reasons, having numerous applications in military strategy. Optimal strategies in the Colonel Blotto game are highly complex – the game does not admit pure strategy equilibria in settings […]