Jack Collier

Defence Research and Development Canada

Papers

14

Total Citations

332

H-Index

9

About

Jack Collier is a robotics researcher whose career has spanned foundational challenges in autonomous unmanned ground vehicle (UGV) navigation, perception, and control. Working primarily within Defence Research and Development Canada (DRDC), Collier has made enduring contributions to mobile robot path tracking, environment mapping, and intelligent control systems. His most influential work, "Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking" (2015), has garnered 195 citations and demonstrates how learned disturbance models can dramatically enhance off-road navigation performance. His early contributions to UGV path tracking using the Pure Pursuit Algorithm (2005) and outdoor terrain mapping with cost-effective lidar hardware (2006) helped establish practical baselines for the field. Collier also advanced multi-sensor place recognition by extending Bag-of-Words methods beyond vision to range sensors, and developed experience-based speed scheduling to push robots toward time-optimal performance. More recently, his work on lidar-based gesture recognition for robot teleoperation highlights an ongoing commitment to human-robot interaction. Across more than two decades, Collier's research reflects a practitioner's instinct for deployable solutions, bridging theoretical robotics with real-world Canadian Forces operational requirements.

Research Focus

Key Achievements

9
H-Index
14
Papers
332
Total Citations
24
Avg Citations/Paper
🏆 Most Cited Paper
Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking
195 citations · 2015
📈 Most Prolific Year: 2009 (2 Papers)
🤝 Key Collaborators: 21
🏛 Institutions: Defence Research and Development Canada

Top Papers

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    The robotics experience
    9 citations · 2009
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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago