Education

Master Thesis Supervision

As a daily supervisor, I have the pleasure to supervise amazing master students. Please check out their thesis and published code for more information.

Improving indoor navigation through cluttered rooms using movability estimation

By Joris Weeda

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. %This work represents a significant advancement in NAMO planning, offering a robust solution for navigating complex environments with movable obstacles.

The thesis is available at: LINK, and code at: LINK

Motion planning for mobile manipulators in multi-agent settings using MPC

By Danning Zhao

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, particularly in real-world environments. Extending motion planning to multi-mobile manipulators introduces challenges in real-time performance, collision avoidance, and coordination. To address these, this thesis proposes a decentralized Model Predictive Control (MPC) framework with a double integrator as dynamic model, denoted as MPC-d, tailored for multi-mobile manipulators operating in shared workspaces. It integrates optimization-based planning with robust state estimation, ensuring effective collision avoidance. Furthermore, a prioritized heuristic is introduced, leveraging the prediction horizon of MPC to resolve potential livelocks. The framework is validated through simulations and real-world experiments. Simulations compare MPC-d with MPC using a triple-integrator model (MPC-t) and a state-of-the-art geometric planner, called Geometric Fabrics (GF). Results demonstrate that MPC-d achieves comparable task success rates and collision avoidance compared to GF in pick-and-place scenarios while requiring less computation time than MPC-t. Real-world experiments confirm the framework's viability, showcasing effective collision avoidance, enhanced efficiency from the prioritized heuristic, and consistency with simulation outcomes. Although MPC-d incurs higher computational costs than reactive geometric methods, it provides reliable performance and motion prediction of other agents in multi-agent settings.

Code: LINK

Switched geometric motion planning

By Leon Kehler

In progress, stay tuned!