Posts

This paper presents a novel occlusion-aware Consistent Model Predictive Control (CMPC) strategy for mobile robots navigating in environments with occluded obstacles — situations where obstacles are hidden from the robot’s field of view but pose potential collision risks. The method strikes an effective balance between safety (avoiding collisions with occluded obstacles) and performance (maintaining smooth, consistent motion), enabling robots to navigate confidently in cluttered, partially observable environments.

Human driver unpredictability forces a false choice between excessive caution and collision risk. We introduce event-triggered online learning that dynamically refines behavior estimates, adjusting safety margins based on observed actions. Our dual-trajectory framework with FRS-based barriers achieves zero collisions, 64% lower lateral jerk, and 14-20% higher speeds than conservative approaches—proving safety and efficiency can coexist without compromise.

Autonomous vehicles navigating occluded intersections face unseen hazards. Our solution co-optimizes exploration and safety-fallback trajectories with a shared initial segment, enabling smooth transitions. Using reachability-based risk assessment and consensus ADMM, we achieve 30% faster traversal, non-stop motion, and 90% faster computation than baselines—validated on real robotic platforms.

One key underlying factor to the safety concern stems from uncertainties in both internal system models and external environments. These uncertainties become critical in contingency scenarios, where potential risks cannot be predicted with certainty.

Present a real-time fail-operational controller for autonomous driving in the presence of time-varying environmental disturbances. This controller is designed to guide autonomous vehicles back to a predefined safe state asymptotically, while upholding task efficiency.

Present a real-time parallel trajectory optimization method for the autonomous vehicles to achieve high travel efficiency in dynamic and congested environments.

We propose a learning-based tracking control scheme based on a feedback linearization controller and Gaussian Processes.

Propose a new learning-based framework based on incremental bayesian learning and control theory.

A learning-based safety-preserving cascaded quadratic programming control policy for safe trajectory tracking under wind disturbances.

Time reallocation for trajectory replanning.