Bio-Inspired Principles Applied to the Guidance, Navigation and Control of UAS
Abstract
:1. Introduction
1.1. Current Limitations of Unmanned Aerial System (UAS)
1.1.1. Navigation
1.1.2. Situational Awareness
1.1.3. Intelligent Autonomous Guidance
1.2. Vision-Based Techniques and Biological Inspiration
1.3. Objective and Paper Overview
2. Experimental Platform
2.1. Overview of Platform
2.2. Biologically Inspired Vision System
2.3. Control Architecture
3. Navigation
3.1. Optic Flow-Based Navigation
3.1.1. Visual Odometry
3.1.2. Landing
3.2. Drift-Free Hover Stabilisation and Navigation via Bio-Inspired Snapshot Matching
3.2.1. Control of UAS Hover through Image Coordinates Extrapolation (ICE)
3.2.2. Control of UAS Hover Through Snapshot Matching
3.2.3. Control of UAS Navigation Through Snapshot Matching
3.3. Vision-Only Navigation
4. Situational Awareness
4.1. Object Detection and Tracking
4.2. Target Motion Classification: Determining Whether an Object Is Moving or Stationary
4.2.1. Using the Epipolar Constraint to Classify Motion
- Compute the egomotion of the aircraft based on the pattern of optic flow in a panoramic image.
- Determine the component of this optic flow pattern that is generated by the aircraft’s translation.
- Finally, detect the moving object by evaluating whether the direction of the flow generated by the object is different from the expected direction, had the object been stationary.
4.2.2. The Triangle Closure Method (TCM)
- The translation direction of the UAS.
- The direction to the centroid of the object.
- The change in the size of the object’s image between two frames.
4.3. Situational Awareness Conclusions
5. Guidance for Pursuit and Interception
5.1. Interception: An Engineering Approach to Pursue Ground Targets
5.2. Interception: A Biological Approach
5.3. Comparison of Pursuit and Constant-Bearing Interception Strategies
5.3.1. Evaluation of Pursuit and Interception Performance as a Function of Sensorimotor Delay
5.3.2. Pursuit and Constant Bearing Interception in the Context of Robotics
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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UAS Type | Approx. Size (m) | Endurance | Primary Function |
---|---|---|---|
Global Hawk [1] | 14 (length), 40 (wingspan) | 32+ h | Surveillance |
Hummingbird [2] | 11 (length), 11 (wingspan) | 24 h | Military reconnaissance |
Predator [3] | 8.2 (length), 14.8 (wingspan) | 24 h | Military reconnaissance and air strike |
Scan Eagle [4] | 1.6 (length), 3.1 (wingspan) | 24+ h | Surveillance |
Raven [5] | 0.9 (length), 1.4 (wingspan) | 60–90 min | Communications |
Air Robot [6] | 1.0 (diameter) | 25 min | Surveillance and inspection |
Phantom 3 [7] | 0.35 (diagonal length excluding propellers) | 23 min (max) | Aerial imaging |
Method | Platform | Path Length | Number of Tests | Average Error (%) |
---|---|---|---|---|
Feature-based stereo matching [75] | Ground vehicle | 20 m | 1 | 1.3 |
Feature-based monocular/stereo matching [66] | Ground vehicle | 186 m; 266 m; 365 m | 3 | 1.4 |
OF-FTF and stereo [59] | UAS | 46 m; 50 m | 15 of each | 1.7 |
path length | ||||
Maimone [58] | Ground vehicle | 24 m; 29 m | 2 | 2.0 |
Feature-based monocular/stereo matching [67] | Ground vehicle | 186 m; 266 m; 365 m | 3 | 2.5 |
Monocular visual SLAM using SIFT features [76] | Ground vehicle | 784 m; 2434 m | 2 | 2.7 |
Optic flow [77] | UAS | 94 m; 83 m | 2 | 3.2 |
Template matching [78] | Ground vehicle | 10 m; 20 m; 50 m; 100 m | 8 of each | 3.3 |
path length | ||||
Stereo/monocular interest point matching SLAM [79] | UAS/Ground vehicle | 100 m | 2 | 3.5 |
Method Reference | Environment | Static Target | Moving Target | ||||||
---|---|---|---|---|---|---|---|---|---|
Test Height (m) | Mean Error (m) | Std. Error or RMSE (m) | Angular Error () | Test Height (m) | Mean Error (m) | Std. Error or RMSE (m) | Angular Error () | ||
Yang et al. [85] | Indoor | 1.00 | 0.03 | NA | 1.97 | NA | NA | NA | NA |
Hou et al. [134] | Indoor | 0.70 | 0.04 | NA | 2.86 | NA | NA | NA | NA |
Guenard et al. [83] | Indoor | 1.40 | 0.10 | NA | 4.09 | NA | NA | NA | NA |
Masselli et al. [86] | Indoor | 0.80 | 0.11 | 0.08 | 5.74 | NA | NA | NA | NA |
Azrad et al. [87] | Outdoor | 5.00 | 2.00 | NA | 21.80 | NA | NA | NA | NA |
Lange et al. [84] | Indoor | 0.70 | 0.29 | NA | 22.29 | NA | NA | NA | NA |
wenzel et al. [118] | Indoor | NA | NA | NA | NA | 0.25 | 0.02 | 0.07 | 14.90 |
Li et al. [135] | Indoor | NA | NA | NA | NA | 1.00 | 0.38 | NA | 20.81 |
Teuliere et al. [136] | Indoor | 2.00 | 0.10 | NA | 2.86 | 0.70 | 0.30 | NA | 23.20 |
Strydom et al. [133] | Outdoor | 2.00 | 0.01 | 0.32 | 9.09 | 2.00 | 0.14 | 0.24 | 6.84 |
Task | Technique | Section |
---|---|---|
Hover | OF-FTF, OF-SM and ICE | 3.2.1 and 3.2.2 |
Landing | OF-FTF | 3.1.2 |
Odometry | OF-FTF and Stereo | 3.1.1 |
Classifying target motion | Optic flow and expansion cues | 4.2 |
Target pursuit | Stereo | 5.1 |
Target pusuit in the presence of sensorimotor delay | Simple pursuit and constant bearing | 5.2 and 5.3 |
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Strydom, R.; Denuelle, A.; Srinivasan, M.V. Bio-Inspired Principles Applied to the Guidance, Navigation and Control of UAS. Aerospace 2016, 3, 21. https://doi.org/10.3390/aerospace3030021
Strydom R, Denuelle A, Srinivasan MV. Bio-Inspired Principles Applied to the Guidance, Navigation and Control of UAS. Aerospace. 2016; 3(3):21. https://doi.org/10.3390/aerospace3030021
Chicago/Turabian StyleStrydom, Reuben, Aymeric Denuelle, and Mandyam V. Srinivasan. 2016. "Bio-Inspired Principles Applied to the Guidance, Navigation and Control of UAS" Aerospace 3, no. 3: 21. https://doi.org/10.3390/aerospace3030021