Devin Connell

Papers

Total Citations

165

H-Index

About

Devin Connell is a robotics researcher whose work centers on autonomous navigation, motion planning, and dynamic path planning for mobile robots and multi-robot systems. His research addresses one of the field's most persistent challenges: enabling robots to efficiently navigate environments filled with unpredictable, moving obstacles in real time. Connell's most significant contributions involve the development and extension of Rapidly-exploring Random Tree (RRT) algorithms for dynamic settings. His 2017 paper, "Dynamic Path Planning and Replanning for Mobile Robots using RRT," has garnered 97 citations, establishing itself as a key reference for researchers tackling real-world autonomous navigation problems. Building on this foundation, his 2018 follow-up introduced an extended RRT framework capable of supporting not only individual robots but entire multi-robot teams operating in dynamic environments, accumulating 60 additional citations. His complementary work applying the optimized RRT* variant further demonstrates his commitment to refining planning efficiency and path quality. Collectively, Connell's publications have shaped how the robotics community approaches replanning under uncertainty. With over 165 combined citations, his research remains highly relevant for students and engineers designing autonomous systems that must operate reliably in complex, ever-changing real-world conditions.