Riccardo Bonalli

Nanjing University of Aeronautics and Astronautics, Stanford University, Centre National de la Recherche Scientifique

Papers

Total Citations

281

H-Index

About

Riccardo Bonalli is a leading researcher at the intersection of optimization, control theory, and autonomous systems, with a primary focus on trajectory generation for robotic and aerospace platforms. His most significant contribution is a highly cited tutorial (233 citations) on convex optimization for trajectory generation, which systematically explains how lossless convexification and sequential convex programming can produce dynamically feasible trajectories both reliably and efficiently. This work has become a foundational reference for practitioners seeking to deploy optimization-based methods in real-time autonomous systems. Bonalli has also advanced the state of the art in computational efficiency through his work on learning-based warm-starting for sequential convex programming, demonstrating how machine learning can accelerate trajectory optimization by providing high-quality initial guesses. More recently, he has tackled the critical challenge of decision-making under uncertainty, developing risk-averse trajectory optimization methods using sample average approximation that can handle nonlinear dynamics, non-convex constraints, and complex uncertainty correlations. His research is notable for bridging theoretical rigor with practical deployability, making advanced optimization techniques accessible for real-world autonomous systems ranging from drones to spacecraft.