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Mitchell Wills
Researcher at Worcester Polytechnic Institute
Publications - 4
Citations - 107
Mitchell Wills is an academic researcher from Worcester Polytechnic Institute. The author has contributed to research in topics: Mobile robot & Mobile robot navigation. The author has an hindex of 3, co-authored 4 publications receiving 90 citations.
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Proceedings ArticleDOI
Robot Web Tools: Efficient messaging for cloud robotics
Russell Toris,Julius Kammerl,David V. Lu,Jihoon Lee,Odest Chadwicke Jenkins,Sarah Osentoski,Mitchell Wills,Sonia Chernova +7 more
TL;DR: These efforts with Robot Web Tools are described to advance: 1) human-robot interaction through usable client and visualization libraries for more efficient development of front-end human- robot interfaces, and 2) cloud robotics through more efficient methods of transporting high-bandwidth topics.
Proceedings ArticleDOI
Hierarchical Navigation Architecture and Robotic Arm Controller for a Sample Return Rover
TL;DR: A three tier navigation architecture and inverse Jacobian based robot arm controller and cascade classifier for sample search and identification on a space exploration rover are presented.
Proceedings ArticleDOI
A cyber physical system testbed for assistive robotics technologies in the home
TL;DR: A cyber-physical system (CPS) testbed is built in a lab environment with initial capabilities allowing for the testing of both individual systems and collections of systems to enable the rapid development, testing, and deployment of assistive robotics technologies in the home of elderly individuals.
Proceedings ArticleDOI
Realization of vision-based navigation and object recognition algorithms for the sample return challenge
TL;DR: The improvements to AERO, the Autonomous Exploration Rover, developed for the 2014 NASA Sample Return Robot competition are presented, enabling more robust and reliable autonomous operation for sample return rovers and providing a roadmap for the integration of multiple heterogeneous systems in a shared control framework to enable efficient exploration of large unknown environments.