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Summary _ Multi-agent robotic system (MARS) for UAV-UGV path planning and automatic sensory data collection in cluttered environments .pdf

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Summary _ Multi-agent robotic system (MARS) for UAV-UGV path planning and automatic sensory data collection in cluttered environments .pdf

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Summary _ Multi-agent robotic system (MARS) for UAV-UGV path planning and automatic sensory data collection in cluttered environments .pdf

Summary _ Multi-agent robotic system (MARS) for UAV-UGV path planning and automatic sensory data collection in cluttered environments .pdf

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Summary _ Multi-agent robotic system (MARS) for UAV-UGV path planning and automatic sensory data collection in cluttered environments .pdf

  1. 1. Multi-Agent Robotic System (MARS) for UAV-UGV Path Planning and Automatic Sensory Data Collection in Cluttered Environments Difeng Hu, Vincent J.L. Gan, Tao Wang, Ling Ma Building and Environment _ Published: 2022 Presenter: Ricardo Hogan 顔永裕 Advisor: Jacob J. Lin , PhD, Assistant Professor 1
  2. 2. Contents 1. Introduction 2. Literature Review 3. Methodology 4. Experiment 5. Conclusion 6. Future Work 2
  3. 3. Introduction ● There has been growing interest in increasing the application of robotic and automation technologies for building inspection. ● Automation and robotic technologies have potential to reduce the workforce and time required to complete inspection tasks. ● Current challenges for real-life adaptation: ○ Indoor applications usually take place in cluttered environments containing various building components and obstacles, which are different from outdoor environments ○ Robotic constraints: UAVs are more agile and have better views than Unmanned Vehicle (UGV) but suffer from smaller payloads and shorter operational times ● Responding to the difficulties mentioned above, the paper presents a multi-agent robotic system (MARS) for automatic UAV-UGV path planning and indoor navigation. 3
  4. 4. Literature Review ● UGV for Indoor Applications ○ Automated inspection using fiducial markers navigated UGV, which is more effective and economical but it cannot actively avoid obstacles. (Mantha et al., 2018) ○ Automated inspection using hybrid tracks and legs robot, teleoperated by an inspector. (Rea and Ottaviano,2018) ● UAV for Data Collection ○ Bridges defect inspection using UAV equipped with LiDAR and flight path optimization using genetic algorithm & A* algorithm (Bolourian and Hammad, 2020) ○ Construction inspections using UAV and BIM information as a map (Freimuth and Konig, 2018) 4
  5. 5. Literature Review ● UAV-UGV Collaborative Data Collection ○ Unified UAV-UGV framework for collecting data in the disaster-rescue scenario. UAV took ground images and created a map while, the UGV navigated within the indoor environment based on the map information. (Lakas et al. 2018) ○ UAV-UGV system for geometric data collection and 3D visualization, in which UAV collected images of a construction site and created a map while UGV navigating to collects data (Kim et al., 2019) 5
  6. 6. Methodology 1. System Architecture & hardware Connection 2. Automated Path Planning 3. Indoor Navigation Coordination Control Algorithm 6 Figure 1 MARS Framework
  7. 7. Methodology - System Architecture 7 Figure 2 Hardware Connection
  8. 8. Methodology - System Architecture 8 Figure 3 System Architecture
  9. 9. Methodology - System Architecture The UAV pipeline contains: ● Built-in Camera → Micro Controller → Sent to Mediating Agent The UGV use Robot Operating System (ROS) and contains 2 pipelines: ● UGV leverages a 2D LiDAR to scan 2D layout and room geometry information —> Mapping Module → 2D occupancy map → Sent to Mediating Agent ● RGB-D images & Camera Poses → RGB-D synchronization module → Sent to Mediating Agent Both UAV and UGV movement are coordinated by Mediating Agent 9
  10. 10. Methodology - System Architecture The mediating agent contains 3 modules: ● RTABMAP : reconstruct the 3D point clouds using the RGB-D information collected by UGV ● Display modules : displays the image data and video streams from UAV. ● Coordinating algorithm : interoperate multiple UAV and UGV devices for data collection to improve inspection efficiency 10
  11. 11. Methodology - Automated Path Planning ● The path then is optimized using enhanced Shunting Short-Term Memory (SSTM) model ● The enhanced SSTM model is built on the construction of neural network architecture 11 Figure 4 SSTM Schematic Diagram Figure 5 Original VS Enhanced SSTM Schematic Diagram
  12. 12. Methodology - Indoor Navigation and Control ● The 3D Occupancy Map is converted to 2D Grid Map, where each grid is considered as a neuron in the SSTM Model 12 Figure 6 Indoor Navigation Procedure for UAV Figure 7 Indoor Navigation Rules for UAV
  13. 13. Methodology - Multi-robot Coordination 13 Figure 8 Coordination Algorithm Mechanism
  14. 14. Experiment ● Experiments are conducted in a Construction Technology Laboratory ● Separately test 3 scenarios of application: ○ Single UAV data collection ○ Dual UAVs data collection ○ Combined UAV-UGV data collection 14 Figure 9 Experiment Location Details
  15. 15. Experiment - Single UAV 15 To test optimized path for single robot Figure 10 Single UAV Flight Path in Grid Map
  16. 16. Experiment - Single UAV 16 Figure 11 Single UAV Flight Path in Real Experiment
  17. 17. Experiment - Dual UAVs ● 17 To test multi robot communication for navigation problem Figure 12 Dual UAVs Flight Path in Grid Map
  18. 18. Experiment - Dual UAVs 18 Figure 13 Dual UAVs Flight Path in Real Experiment
  19. 19. Experiment - Combined UGV-UAV 19 To test multi-robot communication for data collection data problem Figure 14 3D Point Cloud Model Collected by UGV
  20. 20. Experiment - Combined UGV-UAV 20 Figure 15 Combined UGV-UAV Path in Real Experiment
  21. 21. Conclusion ● The proposed MARS in the study is able to combine : ○ Environment mapping and localization → UGV (Using Lidar) ○ Optimized path planning while dodging obstacles and avoid collision → UGV, UAV (Using Enhanced SSTM) ○ Multi robot device communication → Coordination Algorithm ○ Data Collection → UGV (RGBD data), UAV (Photos & Videos) ● The study is one of the early attempts to introduce MARS into indoor navigation for automated data collection, and it shows the potential for revolutionizing data collection and indoor inspection. 21
  22. 22. Conclusion ● The study provides new insights : ○ It is possible to construct a UAV-UGV system for automatic data collection in a cluttered indoor environment. ○ Multiple types of sensory data can be collected using a UAV-UGV system, which is beneficial for facility management. ○ The UAV-UGV system can process the collected data in real-time using a low computational platform, which makes possible real-time facility inspection. ● The study is one of the early attempts to introduce MARS into indoor navigation for automated data collection, and it shows the potential for revolutionizing data collection and indoor inspection. 22
  23. 23. Future Work ● Develop dynamic obstacles detection algorithm ● Installing different kinds of robotic devices and sensors ○ Integrating visual SLAM, into the MARS for more accurate indoor localization ○ Installing Lidar on UAV ● Developing algorithms dealing with 3D cooperative navigation for different robots for indoor inspection. 23

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