Optimizing Lifelong Multi-Agent Pathfinding Using Reinforcement and Imitation Learning

Research topic/area

Mobile Agents and Robotic Systems

Type of thesis

Master

Start time

01.11.2025

Application deadline

30.04.2026

Duration of the thesis

6 Monate

Description

Field:
Mobile robotics is one of the fastest-growing and most dynam-ic areas in intralogistics. As fleet sizes rapidly increase, the challenge shifts from single-robot navigation to large-scale, cooperative fleet coordination. Efficient and adaptive management of these fleets is essential to ensure high system throughput, reliability, and productivi-ty. At the IFL, we are at the forefront of this development, actively contributing to the industry standard VDA 5050, which defines com-munication between mobile robots and fleet management systems.

Problem Statement:
Traditional Lifelong Multi-Agent Pathfinding (LMAPF) algorithms rely on fixed heuristics and lack adaptability to dynamic, real-world environments. In practice, robot fleets must op-erate continuously under uncertainty, delays, and changing task de-mands—conditions where static methods quickly reach their limits. This thesis explores learning-based approaches such as Reinforce-ment Learning, Imitation Learning, or hybrid methods to enable scal-able and adaptive coordination. The goal is to develop policies that learn cooperative behaviors, reduce congestion and deadlocks, and improve overall system throughput and robustness.

Requirement

Requirements for students

• Experience with Python or a similar programming language.
• Familiarity with machine learning frameworks such as PyTorch or TensorFlow is an advantage.
• Knowledge of graph theory and pathfinding algorithms is a plus.
• Problem-solving mindset and an independent working style.

Faculty departments

• Engineering sciences
  Informatics
  Mechanical engineering
  Mechatronics & information technologies
  Mechanical Engineering
• Economic & law sciences
  Information Engineering
  Business management

Supervision

Title, first name, last name

M. Sc. Marvin Rüdt

Organizational unit

Institute for Material Handling and Logistics (IFL)

Email address

marvin.ruedt@kit.edu

Link to personal homepage/personal page

Website

Application via email

Application documents

• Cover letter
• Curriculum vitae
• Grade transcript

E-Mail Address for application
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an marvin.ruedt@kit.edu


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