Roger Pearce

Texas A&M University, Mitchell Institute

Papers

Total Citations

107

H-Index

About

Roger Pearce is a researcher whose work has advanced the field of sampling-based motion planning, a critical area for robotics, computer-aided design, and computational biology. His key contributions focus on improving the efficiency and adaptability of motion planning algorithms. Pearce is best known for developing RESAMPL, a Region-Sensitive Adaptive Motion Planner (2008, 72 citations), which introduced a novel approach to dynamically adjust planning strategies based on the complexity of different regions in the configuration space. This work significantly enhanced the performance of motion planners in high degree-of-freedom environments. He also pioneered the Incremental Map Generation (IMG) method (2008, 22 citations), enabling planners to build and refine maps of the environment progressively, reducing computational overhead. Additionally, his Structural Improvement Filtering Strategy for PRM (2008, 9 citations) provided a systematic way to refine Probabilistic Roadmap (PRM) methods by filtering out less useful samples, leading to more efficient pathfinding. Pearce’s research has been instrumental in making motion planning more adaptive and scalable, with his most cited work, RESAMPL, serving as a foundational reference for subsequent developments in adaptive sampling strategies. His contributions continue to influence modern robotics and autonomous systems.