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About

Arthur Richards is a leading researcher in autonomous robotics, specializing in motion planning, multi-robot coordination, and supervisory control. His most influential work, "Path-Planning with Avoidance Using Nonlinear Branch-and-Bound Optimization" (69 citations), introduced a globally optimal trajectory planning method that retains full nonlinear dynamics—a breakthrough for obstacle avoidance in unmanned vehicles. Richards also pioneered robust decentralized model predictive control (DMPC), explicitly accounting for computational and communication delays, demonstrated on wheeled robots (27 citations). His recent contributions include evolving behaviour trees for human supervision of robot swarms (14 citations) and reliability-aware coverage path planning for multi-UAV systems, where he developed methods for graceful degradation using failure-prone drones (14 and 5 citations). Richards has also advanced semi-autonomous drone control with safety analysis (7 citations) and wheel slip prediction for rover localization (6 citations). His work spans major venues like IEEE ICRA and AIAA, addressing real-world challenges from infrastructure inspection to GPS-denied navigation. With a career focused on bridging theoretical optimization and practical robotic systems, Richards’ research continues to shape how autonomous vehicles operate safely and efficiently in complex, uncertain environments.