Educational activities

Abstract: Navigation Among Movable Obstacles (NAMO) poses a challenge for traditional path-planning methods when obstacles block the path, requiring push actions to reach the goal. We propose a framework that enables movability-aware planning to overcome this challenge without relying on explicit obstacle placement. Our framework integrates a global Semantic Visibility Graph and a local Model Predictive Path Integral (SVG-MPPI) approach to efficiently sample rollouts, taking into account the continuous range of obstacle movability. A physics engine is adopted to simulate the interaction result of the rollouts with the environment, and generate trajectories that minimize contact force. In qualitative and quantitative experiments, SVG-MPPI outperforms the existing paradigm that uses only binary movability for planning, achieving higher success rates with reduced cumulative contact forces.

Abstract: Mobile manipulators, which integrate a robotic arm on a mobile base, are increasingly being explored and deployed in sectors such as healthcare, logistics, and aerospace. While motion planning for these systems has been studied in single-agent scenarios, the use of multiple robots to enhance efficiency and accelerate task completion in multi-agent settings remains largely unexplored. This thesis proposes a decentralized Model Predictive Control (MPC) framework with a double integrator dynamic model (MPC-d) tailored for multi-mobile manipulators operating in shared workspaces. It integrates optimization-based planning with robust state estimation and a prioritized heuristic to resolve potential livelocks. The framework is validated in simulation and real-world experiments, demonstrating effective collision avoidance and improved efficiency.

Abstract: As robots increasingly operate in human environments, their controllers must ensure safe and reliable behavior under real-time constraints. Although optimization-based motion planners can enforce hard safety constraints, their computational demands limit their use on complex robotic platforms. This thesis introduces a runtime verification algorithm, Scenario-Shield, that adapts the geometric planner’s underlying dynamical system to expand its finite-time region of attraction by sampling nearby configurations and using statistical uncertainty quantification for efficient membership testing. Implementations and experiments demonstrate improved reliability over baseline controllers.