M.Sc. Thesis: Map-based and map-less mobile navigation in crowded dynamic environments

Supervisor: Linus Nwankwo, M.Sc.;
Univ.-Prof. Dr Elmar Rückert
Start date: 5th September 2022

Theoretical difficulty: mid
Practical difficulty: mid


For over 20 years today, the simultaneous localisation and mapping (SLAM) approach has been widely used to achieve autonomous navigation objectives. The SLAM problem is the problem of building a map of the environment while simultaneously estimating the robot’s position relative to the map given noisy sensor observations and a series of control data.  Recently, the 

mapless-based approach with deep reinforcement learning has been proposed. For this approach, the agent (robot) learns the navigation policy given only sensor data and a series of control data without a prior map of the task environment. In the context of this thesis, we will evaluate the performance of both approaches in a crowded dynamic environment using our differential drive open-source open-shuttle mobile robot.

Tentative Work Plan

To achieve our objective, the following concrete tasks will be focused on:

  • Literature research and a general understanding of the field
    • mobile robotics and industrial use cases
    • Overview of map-based autonomous navigation (SLAM & Path planning)
    • Overview of mapless-based autonomous navigation approach with deep reinforcement learning
  • Setup and familiarize with the simulation environment
    • Build the robot model (URDF) for the simulation (optional if you wish to use the existing one)
    • Setup the ROS framework for the simulation (Gazebo, Rviz)
    • Recommended programming tools: C++, Python, Matlab
  • Intermediate presentation:
    • Presenting the results of the literature study
    • Possibility to ask questions about the theoretical background
    • Detailed planning of the next steps
  • Define key performance/quality metrics for evaluation:
    • Time to reach the desired goal
    • Average/mean speed
    • Path smoothness
    • Obstacle avoidance/distance to obstacles
    • Computational requirement
    • success rate
    • e.t.c
  • Assessment and execution:
    • Compare the results from both map-based and map-less approaches on the above-defined evaluation metrics.
  • Validation:
    • Validate both approaches in a real-world scenario using our open-source open-shuttle mobile robot.
  • Furthermore, the following optional goals are planned:
    • Develop a hybrid approach combining both the map-based and the map-less methods.
  • M.Sc. thesis writing
  • Research paper writing (optional)

Related Work

[1] Xue, Honghu; Hein, Benedikt; Bakr, Mohamed; Schildbach, Georg; Abel, Bengt; Rueckert, Elmar, “Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics“, In: Applied Sciences (MDPI), Special Issue on Intelligent Robotics, 2022.

[2] Han Hu; Kaicheng Zhang; Aaron Hao Tan; Michael Ruan; Christopher Agia; Goldie Nejat “Sim-to-Real Pipeline for Deep Reinforcement Learning for Autonomous Robot Navigation in Cluttered Rough Terrain”,  IEEE Robotics and Automation Letters ( Volume: 6, Issue: 4, October 2021).

[3] Md. A. K. Niloy; Anika Shama; Ripon K. Chakrabortty; Michael J. Ryan; Faisal R. Badal; Z. Tasneem; Md H. Ahamed; S. I. Mo, “Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review”, IEEE Access ( Volume: 9), February 2021.

[4]  Ning Wang, Yabiao Wang, Yuming Zhao, Yong Wang and Zhigang Li , “Sim-to-Real: Mapless Navigation for USVs Using Deep Reinforcement Learning”, Journal of Marine Science and Engineering, 2022, 10, 895. https://doi.org/10.3390/jmse10070895