Papers
8
Total Citations
121
H-Index
6
About
James McMahon is a leading researcher in autonomous robotics, with a focus on motion planning, goal reasoning, and marine vehicle autonomy. His work bridges the gap between high-level task specifications and low-level control, enabling robots to operate intelligently in complex, dynamic environments. McMahon’s most influential contribution is his 2014 paper on sampling-based tree search with discrete abstractions for motion planning under temporal logic constraints (42 citations), which provides an efficient method for generating collision-free, dynamically feasible trajectories that satisfy complex task specifications. He has also pioneered goal-driven autonomy (GDA) in robotics, introducing iterative goal refinement and bounded expectations for discrepancy detection—work that has been applied to unmanned vehicles and autonomous underwater vehicles (AUVs). His 2022 paper on autonomous data collection with dynamic goals and communication constraints (21 citations) addresses critical challenges in marine robotics, where AUVs must collaborate with surface vehicles while avoiding obstacles and maintaining connectivity. McMahon’s research has practical impact, with at-sea tests demonstrating goal reasoning for AUVs responding to unexpected agents. His work on multitarget tracking using the Bayes factor further showcases his versatility in sensor-based estimation. With over 120 total citations, McMahon continues to advance the frontier of intelligent, autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 3Bounded Expectations for Discrepancy Detection in Goal-Driven Autonomy15 citations · 2014
- 4Iterative Goal Refinement for Robotics13 citations · 2014
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- 6Robot motion planning with task specifications via regular languages12 citations · 2015
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