Papers
15
Total Citations
223
H-Index
8
About
Álvaro Prado is a robotics and control systems researcher whose work sits at the intersection of autonomous mobile robotics, model predictive control, and field robotics applications in agriculture and mining. His research is predominantly focused on skid-steer mobile robots (SSMRs), a platform notorious for complex wheel-terrain interactions and slip dynamics that challenge conventional control strategies. Prado's most influential contribution, "Tube-based nonlinear model predictive control for autonomous skid-steer mobile robots with tire–terrain interactions" (2020, 59 citations), established a robust theoretical framework for handling terramechanical disturbances in robot navigation, a theme he has consistently expanded upon through distributed and adaptive MPC architectures. Beyond control theory, Prado has made meaningful contributions to agricultural robotics, developing integrated route and path planning strategies that incorporate crop scheduling, terrain traversability, and real-world production constraints — areas largely overlooked by traditional planners. More recently, his research has embraced deep reinforcement learning, combining LSTM-enhanced architectures with classical control methods to tackle autonomous navigation in unstructured mining environments. With over 200 cumulative citations, Prado's body of work reflects a sustained commitment to bridging theoretical control design with the practical demands of harsh, real-world robotic deployments.
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