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M.Sc. Thesis – Pratheesh Nair: 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

Abstract

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 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).

[3] 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.

[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

10.11.2022 – Innovative Research Discussion

Meeting notes on the 20th of October, 2022

Location: Chair of CPS

Date & Time: 10th November, 2022, 1:30 pm to 2:28 pm

Participants: Univ.-Prof. Dr. Elmar Rueckert, Linus Nwankwo, M.Sc.

 

Agenda

  1. General Discussion
  2. Discussion on the data set from Privatklinik Graz
  3. Next action

General Discussion

  1.  New cards are added to the Deck app, check the ones that required actions.
  2.  There will be a ROS2 meeting with Nils by 5 pm on 11.11.2022.

Data-set from Privitklinik Graz

  1. Reproduce the failed experiments.

  2. Evaluate the S-PTAM and ORB-SLAM visual SLAM algorithms on the recorded data.

  3. Evaluate Hector SLAM and GMapping algorithms on the recorded dataset(s) with a limited field of view of the lidar data.

    • Remove 90° in the frontal direction
    • Remove 120° in the frontal direction
    • Remove 90 or 120° in the frontal and in the backwards direction

Do next

  1. Implement a filter node that filters out the noise from the data.
  2. Build the map from the hospital’s building plan (bp)

20.10.2022 – Innovative Research Discussion

Meeting notes on the 20th of October, 2022

Location: Chair of CPS

Date & Time: 20th October, 2022, 12:35 pm to 1:38 pm

Participants: Univ.-Prof. Dr. Elmar Rueckert, Linus Nwankwo, M.Sc.

 

Agenda

  1. General Discussion
  2. Update on Conference Paper
  3. Do-It-Lab

General Discussion

  1.  Add dates to all the meeting notes
  2. Add publication to the home page
  3. Contact Christopher for his presentation date and update our calendar accordingly
  4. Use the Deck app to communicate updates of current, completed and yet-to-be-done tasks

Update on Conference Paper

  1.  Compare map-based and map-less indoor SLAM methods.
  2. Focus on indoor navigation.
  3. Evaluate 1 – 3 lidar-based, 1 – 3 visual-based, and 1- 3 deep learning SLAM methods.
  4. Pick up some ideas from the referenced papers in the Deck app.

Do-It-Lab

  1. Organise the students into four groups
  2.  Give the students the questionnaire after the lab to fill out and submit. You could generate a barcode using the web link to the form.

17.10.2022 – Introduction to CAD Software

Why do I need CAD Software?

  • Computer-Aided Design (CAD) is the cornerstone of how you design and build things. It allows the user to digitally create, visualise, and simulate 2D or 3D models of real-world products before it is being manufactured.
  • CAD models allow users to iterate and optimize designs to meet design intent.
  • The use of CAD software facilitates the testing of real-world conditions, loads, and constraints, which increases the quality of the product.
  • CAD software helps to explore ideas and visualise the concept.
  • Improve the quality, precision of the design, and communication in the design process.
  • Analyse real-world scenarios by computer-aided analysis
  • Create a database for product development and manufacturing.

Some Practical Applications of CAD Software

Source: https://learnsolidworks.com/
Source: https://automation.siemens.com/
Source: https://leocad.org/

Automobile parts can be modelled, visualised, revised, and improved on the screen before being manufactured.

Electrical schematics, control circuit diagrams, PCBs, and integrated circuits (ICs)  can be designed and developed with ECAD software 

With CAD software, architects can visualise and simulate their entire project using real-world parameters, without needing to build any physical structuress or models. 

What CAD software do I need?

Something free

  • FreeCAD
  •  TinkerCAD
  • Fusion 360
  • Onshape
  • Solid Edge
  • Blender
  • SketchUp

My design goes with me wherever I go (cloud-based)

  • Onshape
  • TinkerCAD
  • AutoCAD Web
  • SelfCAD
  • Vectary
  • SketchUp

Something more advanced and professional

  • AutoCAD
  • Autodesk Inventor
  • SolidWorks
  • Fusion 360
  • Solid Edge
  • CATIA
  • Onshape
  • Shapr3D
  • Creo

Windows OS

  • AutoCAD
  • Autodesk Inventor
  • Solidworks
  • Fusion 360
  • CATIA
  • Creo
  • Solid Edge
  • Shapr3D
  • Blender

Linux OS

  • NX Advanced Designer
  • Blender

MacOS

  • AutoCAD
  • Autodesk Inventor
  • Fusion 360
  • Shapr3D
  • Blender
  • NX Advanced Designer

iOS, Android

  • AutoCAD
  • Autodesk Inventor
  • Shapr3D

Where can I learn CAD?

07.10.2022 – Innovative Research Discussion

Meeting notes on the 4th of October, 2022

Location: Chair of CPS

Date & Time: 7th October, 2022, 09:15 am to 10:25 pm

Participants: Univ.-Prof. Dr. Elmar Rueckert, Linus Nwankwo, M.Sc.

Agenda

  1. Discussion on  ROS-Mobile Control
  2. Discussion on ODrive torque control

ROS-Mobile and ODrive torque control

  1.  Re-implement the o2s control approach to accommodate the information in the attached figure.
  2.  Write the Arduino code taking into account the rotation matrices
  3. Implement the open-loop torque control approach

04.10.2022 – Innovative Research Discussion

Meeting notes on the 4th of October, 2022

Location: Chair of CPS

Date & Time: 4th October, 2022, 12:15 am to 1:15 pm

Participants: Univ.-Prof. Dr. Elmar Rueckert, Linus Nwankwo, M.Sc.

 

Agenda

  1. Discussion of the research progress
  2. Discussion on the Hardware-X journal publication 
  3. Discussion of the next conference publication

Journal Publication : Hardware-X

  1.  Put the HardwareX manuscript on Arxiv to enable us to: 
  • cite it in the subsequent article
  • obtain the DOI
  • update the publication in our cloud with the Arxiv number

Conference Paper: O2S: Open Source Open Shuttle - A comparison of SLAM algorithms

  1.  Start working on the conference paper using the shared Latex template provided
  • Compare the various 2D SLAM algorithms
  •  Establish the key metrics for the evaluation
  • Evaluate their performance in real-world with the O2S
  • Evaluate visual SLAM (optional)
  • Check how 2D SLAM can be combined with RGB-D cameras with a deep neural network to improve the map quality (optional)

Future Steps

Intention signalling to improve human-robot interaction (HRI)

Next Meeting

Yet to be defined

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

Abstract

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 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).

[3] 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.

[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

29.08.2022 – Innovative Research Discussion

Meeting notes on the 29th of August, 2022

Location: Chair of CPS

Date & Time: 29th August, 2022, 11:30 am to 12.00 noon

Participants: Univ.-Prof. Dr. Elmar Rueckert, Linus Nwankwo, M.Sc.

 

Agenda

  1. Discussion of the research progress
  2. Discussion on the Hardware-X journal publication 
  3. Discussion of the next project after hardware-X submission

Topic 1: Journal Publication - Hardware-X

  1.  Re-design the robot base for the hardware-X publication
  2. Publish the following file:
    • CAD files including the URDF
    • ROS nodes and schematics
    • Show some experimental results e.g real-time control and monitoring with ROS-Mobile and Android-based devices; remote control from Arduino and joystick etc.
    • Perform SLAM and demonstrate some experiment.

Topic 2:  Conference paper for possible publication in IROS, ECMR etc:

  1.  Open source open shuttle (O2S) for SLAM and navigation applications:  – Compare the existing SLAM algorithms for efficient navigation in a cluttered and dynamic environment
  2. O2S intention signalling: – for safe human-robot interaction (HRI) using intention signalling.
  3. Mobile robot teleoperation through hand gestures approach (possible for publication at the National European conference )
  4. Deep navigation with O2S (proposal for Christoper M.Sc. thesis – subject to changes pending the outcome of the meeting on Friday)

Future Steps

Survey of open-shuttles in logistics:  Possible for A.I based journal publication

Next Meeting

Yet to be defined

B.Sc. or M.Sc. Project/Thesis: Mobile robot teleoperation based on human finger direction and vision

Theoretical difficulty: mid
Practical difficulty: mid

Naturally, humans have the ability to give directions (go front, back, right, left etc) by merely pointing fingers towards the direction in question. This can be done effortlessly without saying a word. However, mimicking or training a mobile robot to understand such gestures is still today an open problem to solve.
In the context of this thesis, we propose finger-pose based mobile robot navigation to maximize natural human-robot interaction. This could be achieved by observing the human fingers’ Cartesian  pose from an

RGB-D camera and translating it to the robot’s linear and angular velocity commands. For this, we will leverage computer vision algorithms and the ROS framework to achieve the objectives.
The prerequisite for this project are basic understanding of Python or C++ programming, OpenCV and ROS.

Tentative work plan

In the course of this thesis, the following concrete tasks will be focused on:

  • study the concept of visual navigation of mobile robots
  • develop a hand detection and tracking algorithm in Python or C++
  • apply the developed algorithm to navigate a simulated mobile robot
  • real-time experimentation
  • thesis writing

References

  1. Shuang Li, Jiaxi Jiang, Philipp Ruppel, Hongzhuo Liang, Xiaojian Ma,
    Norman Hendrich, Fuchun Sun, Jianwei Zhang,  “A Mobile Robot Hand-Arm Teleoperation System by Vision and IMU“,  IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),  October 25-29, 2020, Las Vegas, NV, USA.
  2.  

24.03.2022 – Innovative Research Discussion

Meeting notes on the 23rd of March 2022

Location: Chair of CPS

Date & Time: 23rd March. 2022, 12 pm to 1.00 pm

Participants: Univ.-Prof. Dr. Elmar Rueckert, Linus Nwankwo, M.Sc.

 

Agenda

  1. Discussion of the research progress
  2. Discussion of related literature for 2D Lidar-based SLAM and path planning for improved autonomous navigation

Topic 1: SLAM + Path Planning Algorithms

  1.  Compare Lidar-based SLAM with RGBD-based
  2. Select the appropriate algorithm to build the SLAM system
  3. Evaluate the performances of the chosen SLAM + path planning algorithm in terms of:
    • computational efficiency
    • collision avoidance in a dynamic environment.

Topic 2: Real-time Implementation

  1.  Compare our two mobile bases: 
  2. Implement the chosen SLAM algorithm to solve the problem of loop closure that we are currently facing.
  3.  Send the robot to a specific goal location within the laboratory
  4.  

Future Steps

Toward improved autonomous navigation in real-time with any of our mobile bases.

Next Meeting

Yet to be defined