Papers
11
Total Citations
105
H-Index
7
About
Troy McMahon is a robotics researcher whose work sits at the intersection of motion planning, robot manipulation, and machine learning. He is best known for developing the theory of **reachable volumes**, a geometric framework that characterizes the set of points reachable by a robot's end effector. His foundational 2014 paper established that a chain's reachable volume equates to the Minkowski sum of its links' reachable volumes, earning 24 citations and spawning a productive line of follow-on work applying the concept to high-degree-of-freedom manipulators, closed-chain systems, and constrained planning problems. McMahon has also made meaningful contributions to probabilistic roadmap methods, demonstrating that local randomization in neighbor selection measurably improves roadmap quality. More recently, he has turned toward integrating machine learning with sampling-based planners, co-authoring a widely read survey on the topic and developing learned controllers that improve kinodynamic planning over challenging terrains. His work on affordance wayfields further extends his reach into task and motion planning. Collectively, his research has garnered nearly 100 citations, reflecting a consistent and growing influence on how the robotics community approaches complex, constrained planning challenges.
Research Focus
Key Achievements
Top Papers
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- 4Local randomization in neighbor selection improves PRM roadmap quality12 citations · 2012
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- 6Sampling Based Motion Planning with Reachable Volumes8 citations · 2016
- 7Affordance Wayfields for Task and Motion Planning7 citations · 2018
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