Joshua A. Marshall
Queen's University, Kingston University, University of Toronto, Carleton University
Papers
29
Total Citations
702
H-Index
14
About
Joshua A. Marshall is a prominent robotics researcher whose work spans mobile robotics, autonomous mining systems, and multi-robot coordination. With deep expertise in applying robotics to challenging real-world environments, Marshall has made lasting contributions to both the theoretical and practical dimensions of the field. His research is perhaps best known for bridging autonomous systems with industrial applications, particularly in mining robotics — a niche yet critically important domain where his 2016 survey "Robotics in Mining" helped define the field's trajectory. His work on admittance control for robotic excavation, tested on machines ranging from 1-tonne loaders to 14-tonne LHD machines, demonstrated remarkable real-world applicability and has garnered nearly 50 citations. Marshall has also advanced intelligent path-following through learning-based model predictive control using Gaussian processes, and contributed foundational work in multi-robot task planning and SLAM using LiDAR and time-of-flight sensors. His 2022 survey on mobile robot simulators and the multi-robot planning survey, each exceeding 100 citations, reflect his influence on how researchers and practitioners approach autonomous systems development. From early experiments in multirobot coordination dating to 2005, Marshall's career represents a sustained, impactful commitment to making robots smarter, safer, and genuinely useful in demanding environments.
Research Focus
Key Achievements
Top Papers
- 1Multiple Mobile Robot Task and Motion Planning: A Survey107 citations · 2022
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- 4Robotics in Mining50 citations · 2016
- 5Experiments in multirobot coordination48 citations · 2005
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- 8Iterative Learning-Based Admittance Control for Autonomous Excavation33 citations · 2019
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- 10Towards intensity-augmented SLAM with LiDAR and ToF sensors30 citations · 2015