https://hdl.handle.net/1721.1/37333 Drafts of documents written by laboratory members Fri, 03 Apr 2026 20:09:04 GMT 2026-04-03T20:09:04Z https://hdl.handle.net/1721.1/71738 Information-Theoretic Motion Planning for Constrained Sensor Networks Levine, Daniel; Luders, Brandon; How, Jonathan P. This paper considers the problem of online informative motion planning for a network of heterogeneous sensing agents, each subject to dynamic constraints, environmental constraints, and sensor limitations. Prior work has not yielded algorithms that are amenable to such general constraint characterizations. In this paper, we propose the Information-rich Rapidly-exploring Random Tree (IRRT) algorithm as a solution to the constrained informative motion planning problem that embeds metrics on uncertainty reduction at both the tree growth and path selection levels. IRRT possesses a number of beneficial properties, chief among them being the ability to find dynamically feasible, informative paths on short timescales, even subject to the aforementioned constraints. The utility of IRRT in efficiently localizing stationary targets is demonstrated in a progression of simulation results with both single-agent and multiagent networks. These results show that IRRT can be used in real-time to generate and execute information-rich paths in tightly constrained environments. Fri, 20 Jul 2012 00:00:00 GMT https://hdl.handle.net/1721.1/71738 2012-07-20T00:00:00Z https://hdl.handle.net/1721.1/69058 Planning under Uncertainty using Nonparametric Bayesian Models Campbell, Trevor; Ponda, Sameera; Chowdhary, Girish; How, Jonathan Wed, 01 Aug 2012 00:00:00 GMT https://hdl.handle.net/1721.1/69058 2012-08-01T00:00:00Z https://hdl.handle.net/1721.1/49418 An Intelligent Cooperative Control Architecture How, Jonathan; Choi, Han-Lim; Undurti, Aditya; Redding, Joshua This paper presents an extension of existing cooperative control algorithms that have been developed for multi-UAV applications to utilize real-time observations and/or performance metric(s) in conjunction with learning methods to generate a more intelligent planner response. We approach this issue from a decentralized cooperative control perspective and embed elements of feedback control and active learning, resulting in an new intelligent Cooperative Control Architecture (iCCA). We describe this architecture, discuss some of the issues that must be addressed, and present illustrative examples of cooperative control problems where iCCA can be applied effectively. Wed, 07 Oct 2009 00:00:00 GMT https://hdl.handle.net/1721.1/49418 2009-10-07T00:00:00Z