NSF CARRER project: Topological Abstraction for Robot Path Planning
NSF CAREER project on Topological Abstraction for Robot Path Planning
High degree-of-freedom robotic systems such as those involving one or more flexible cables, multi-robot systems, robotics manipulators and soft robotic arms, are ubiquitous in automation industries, household robotics, medical robotics, field robotics and social robots. Cables can be used by multi-robot systems to tug or transport large loads, and robots can use fixed tethers for power supply or communication for long-term missions in GPS-denied environments. Multi-agent systems need new innovations for coordination-free planning in a sociopolitical environment in which privacy concerns are on the rise. Articulated robotic manipulators are used in a variety of industrial applications such as automated manufacturing, as well as in minimally invasive assisted laparoscopic surgery. This Faculty Early Career Development (CAREER) project addresses the fundamental question of complexity reduction for such systems with high-dimensional configuration spaces using techniques of topological abstraction with an aim of achieving fast & efficient planning. Integrated with the research objectives of this project are educational plans aimed at training current and future generation of engineers & researchers in the science and technologies of complex autonomous systems that are increasingly becoming part of our modern society. As an integral part of the project, STEM education will be advanced through establishment of a mentoring ecosystem involving graduate, undergraduate & K-12 students. Students from underrepresented groups will be involved in the research activities with an aim of developing a comprehensive & scalable education program.
In this project novel theoretical and algorithmic tools will be developed for achieving topological abstraction of high-dimensional configuration spaces with an aim of allowing efficient optimal path planning for high degree-of-freedom systems using Topological Path Planning methods. Homotopy invariants will be developed for joint configuration spaces of multiple robots navigating on planar domains with obstacles, resulting in complexity reduction in problems involving tethered multi-robot systems, constrained human-robot teams, and planning for large multi-agent systems without coordination. Novel algorithms for efficient computation of homotopy invariants in spatial domains will be developed, leading to direct amalgamation with search-based planning algorithms with application to spatial systems involving cables. The project goes beyond homotopy to develop the theoretical foundations of a deep generalization to the notion of homotopy, which will pave the way for the discovery and computation of novel topological constructions called Reeb structures that are suitable for dimensionality reduction of configuration spaces of articulated and soft robotic manipulators. All these fundamental advances will allow development of efficient path planning algorithms with completeness and optimality guarantees for high degree-of-freedom systems such as spatial, multi-robot systems & human-robot systems involving one or more flexible cables; coordination-free multi-agent systems; and articulated robotic arms & deformable manipulators. The developed algorithms will be implemented and evaluated on real robotic platforms.
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