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About

B. Donald is a pioneering roboticist whose research centers on multi-robot coordination, sensor-based planning, and principled approaches to uncertainty in autonomous systems. His most influential work, "Moving furniture with teams of autonomous robots" (276 citations), fundamentally advanced the understanding of decentralized manipulation, demonstrating how multiple robots can coordinate pushing to reposition objects without requiring global control or explicit communication. This work laid critical groundwork for cooperative robotics. Donald also made foundational contributions to robot motion planning under uncertainty, introducing a formal framework for error detection and recovery (EDR) in his highly cited 1986 paper (44 citations). His concept of "perceptual equivalence classes" provided a rigorous mathematical structure for sensor interpretation and task-directed planning, enabling robots to cope with incomplete or noisy information. Beyond these theoretical advances, Donald pursued experimental validation of his EDR planner on physical force-controlled robots, bridging theory and practice. His work on constructive recognizability further developed a principled vocabulary for task-level robot programming, emphasizing information flow through sensing and action. With over 400 total citations across his key publications, Donald’s research remains essential reading for students and researchers working in multi-robot systems, manipulation, and sensor-based planning.