Jorge L. Piovesan

University of New Mexico, K&A Wireless (United States)

Papers

Total Citations

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About

Jorge L. Piovesan is a roboticist specializing in control theory, with a focus on nonlinear model predictive control (MPC) and randomized optimization for autonomous navigation. His most influential work, "Randomized Receding Horizon Navigation" (2010, 32 citations), pioneers the integration of weak control Lyapunov functions with randomized algorithms to solve complex, constrained robot navigation problems. This approach relaxes the need for strict definiteness in control functions, enabling more flexible and computationally efficient path planning in cluttered environments. Building on this, his 2009 paper (26 citations) applies randomized MPC to mobile robots, using a navigation function as a Lyapunov function to guarantee stability while handling state and input constraints through probabilistic sampling. Piovesan’s earlier work on leader-follower control (2007) addresses practical challenges in multi-robot systems by analyzing odometry error propagation, ensuring robust relative positioning. While his citation counts reflect a focused, technical audience, his contributions are foundational for researchers working at the intersection of optimization, control theory, and robotics—particularly those seeking real-time solutions for autonomous systems under uncertainty. His work remains a key reference for randomized methods in safety-critical navigation.