James Pace
Papers
2
Total Citations
10
H-Index
2
About
James Pace is a robotics researcher whose work focuses on bridging the gap between motion planning and real-world terrain constraints for legged and wheeled mobile robots. His key contributions center on developing kinematic models and optimal navigation strategies for platforms operating in unstructured environments. In his most cited work, "Kinematic modeling of a RHex-type robot using a neural network" (2017, 8 citations), Pace addresses the critical challenge of motion planning for legged robots—a domain where traditional physics-based models fall short. By employing a neural network to capture the complex kinematics of RHex-type platforms, he provides a practical alternative that enables more reliable locomotion planning. His follow-up study, "Experimental verification of distance and energy optimal motion planning on a skid-steered platform" (2017, 2 citations), extends this work by demonstrating how terrain awareness can be integrated into path selection—choosing between shortest-path and energy-efficient routes depending on ground conditions. Together, these contributions highlight Pace’s commitment to making field robots more adaptive and efficient, offering foundational insights for researchers tackling motion planning in challenging, real-world terrains.
Research Focus
Key Achievements
Top Papers
- 1Kinematic modeling of a RHex-type robot using a neural network8 citations · 2017
- 2