M.Sc. Thesis: Christopher Steer on Performance Evaluation of Map-based and Mapless Mobile Navigation (M^3N) in Crowded Dynamic Environment

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

 

Theoretical difficulty: mid
Practical difficulty: mid

Abstract

For over 20 years today, the simultaneous localisation and mapping (SLAM) method has been widely used to achieve autonomous navigation objectives. The robot is required to build the map of its work environment given the estimate of its state, sensor observation and series of control and simultaneously localise itself relative to the map. However, a mapless-based approach with deep reinforcement learning

has been proposed in recent years. For this, the agent (robot) learns the navigation policy given only sensor data and a series of control without a prior map of the task environment. In the context of this thesis, we evaluate the performance of both approaches in a crowded dynamic environment using our 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 mapless 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 mapless methods.
    •  
  • M.Sc. thesis writing
  • Research paper writing (optional)

 

Related Work

[1] Han HuKaicheng ZhangAaron Hao TanMichael RuanChristopher AgiaGoldie 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).

[2] Md. A. K. NiloyAnika ShamaRipon K. ChakraborttyMichael J. RyanFaisal R. BadalZ. TasneemMd H. AhamedS. I. Mo, “Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review”, IEEE Access ( Volume: 9), February 2021.

[3]  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

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