Drone On-time Obstacle Avoidance for Static and Dynamic Obstacles
Authors: Herath MPC Jayaweera, Samer Hanoun
Abstract:
Path planning for on-time obstacle avoidance is an essential and challenging task that enables drones to achieve safe operation in any application domain. The level of challenge increases significantly on the obstacle avoidance technique when the drone is following a ground mobile entity (GME). This is mainly due to the change in direction and magnitude of the GMEs velocity in dynamic and unstructured environments. Force field techniques are the most widely used obstacle avoidance methods due to their simplicity, ease of use and potential to be adopted for three-dimensional dynamic environments. However, the existing force field obstacle avoidance techniques suffer many drawbacks including their tendency to generate longer routes when the obstacles are sideways of the drones route, poor ability to find the shortest flyable path, propensity to fall into local minima, producing a non-smooth path, and high failure rate in the presence of symmetrical obstacles. To overcome these shortcomings, this paper proposes an on-time three-dimensional obstacle avoidance method for drones to effectively and efficiently avoid dynamic and static obstacles in unknown environments while pursuing a GME. This on-time obstacle avoidance technique generates velocity waypoints for its obstacle-free and efficient path based on the shape of the encountered obstacles. This method can be utilize on most types of drones that have basic distance measurement sensors and autopilot supported flight controllers. The proposed obstacle avoidance technique is validated and evaluated against existing force field methods for different simulation scenarios in Gazebo and ROS supported PX4-SITL. The simulation results show that the proposed obstacle avoidance technique outperforms the existing force field techniques and is better suited for real-world applications.
Keywords: Drones, force field methods, obstacle avoidance, path planning.
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[1] J. T. Butler, “UAVS and ISR Sensor Technology,” AIR COMMAND AND STAFF COLL MAXWELL AFB AL, AU/ACSC/033/2001-04, Apr. 2001. Accessed: Apr. 10, 2019.
[Online]. Available: https://apps.dtic.mil/docs/citations/ADA407741.
[2] F. Bangkui, Li Yun, R. Zhang, and Fu Qiqi, “Review on the technological development and application of UAV systems,” Chinese Journal of Electronics, vol. 29, no. 2, pp. 199-207, Mar. 2020.
[3] J. Y. C. Chen, “UAV-guided navigation for ground robot tele-operation in a military reconnaissance environment,” Ergonomics, vol. 53, no. 8, pp. 940–950, Aug. 2010.
[4] S. Sudhakar, V. Vijayakumar, C. Sathiya Kumar, V. Priya, L. Ravi, and V. Subramaniyaswamy, “Unmanned Aerial Vehicle (UAV) based Forest Fire Detection and monitoring for reducing false alarms in forest-fires,” Comput. Commun., vol. 149, pp. 1–16, Jan. 2020.
[5] P. Nevavuori, N. Narra, P. Linna, and T. Lipping, “Crop Yield Prediction Using Multitemporal UAV Data and Spatio-Temporal Deep Learning Models,” Remote Sens., vol. 12, no. 23, p. 4000, Dec. 2020.
[6] R. H. Kabir and K. Lee, “Wildlife Monitoring Using a Multi-UAV System with Optimal Transport Theory,” Appl. Sci., vol. 11, no. 9, p. 4070, Apr. 2021.
[7] S. Chowdhury, A. Emelogu, M. Marufuzzaman, S. G. Nurre, and L. Bian, “Drones for disaster response and relief operations: A continuous approximation model,” Int. J. Prod. Econ., vol. 188, pp. 167–184, Jun. 2017.
[8] H. M. P. C. Jayaweera and S. Hanoun, “UAV Path Planning for Reconnaissance and Look-Ahead Coverage Support for Mobile Ground Vehicles,” Sensors, vol. 21, no. 13, p. 4595, Jul. 2021.
[9] S. X. Yang and C. Luo, “A Neural Network Approach to Complete Coverage Path Planning,” IEEE Trans. Syst. Man Cybern. Part B Cybern., vol. 34, no. 1, pp. 718–724, Feb. 2004.
[10] E. Shan, B. Dai, J. Song, and Z. Sun, “A Dynamic RRT Path Planning Algorithm Based on B-Spline,” in 2009 Second International Symposium on Computational Intelligence and Design, Changsha, Hunan, China, pp. 25–29, 2009.
[11] K. H. Sedighi, K. Ashenayi, T. W. Manikas, R. L. Wainwright, and Heng-Ming Tai, “Autonomous local path planning for a mobile robot using a genetic algorithm,” in Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), Portland, OR, USA, pp. 1338–1345, 2004.
[12] C. C. Wong, H. Y. Wang, and S. A. Li, “PSO-based Motion Fuzzy Controller Design for Mobile Robots,” Int. J. Fuzzy Syst., vol. 10, no. 1, p. 9, 2008.
[13] X. Chen and J. Zhang, “The Three-Dimension Path Planning of UAV Based on Improved Artificial Potential Field in Dynamic Environment,” in 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, vol. 2, pp. 144–147, Aug. 2013.
[14] S. Back, G. Cho, J. Oh, X.-T. Tran, and H. Oh, “Autonomous UAV Trail Navigation with Obstacle Avoidance Using Deep Neural Networks,” J. Intell. Robot. Syst., vol. 100, no. 3–4, pp. 1195–1211, Dec. 2020.
[15] L. Yang, Z. Wei-guo, S. Jing-ping, and L. Guang-wen, “A path planning method based on improved RRT,” in Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference, Yantai, China, pp. 564–567, Aug. 2014.
[16] X. Wang and X. Meng, “UAV Online Path Planning Based on Improved Genetic Algorithm,” in 2019 Chinese Control Conference (CCC), Guangzhou, China, pp. 4101–4106, Jul. 2019.
[17] D. Gonz´alez, J. P´erez, V. Milan´es, and F. Nashashibi, “A Review of Motion Planning Techniques for Automated Vehicles,” IEEE Trans. Intell. Transp. Syst., vol. 17, no. 4, pp. 1135–1145, Apr. 2016.
[18] Y. K. Hwang, and N. Ahuja, “A potential field approach to path planning,” IEEE Transactions on Robotics and Automation, vol. 8, no. 1, pp. 23-32, Feb. 1992.
[19] Woods, C. Alexander, and M. La. Hung, ”Dynamic target tracking and obstacle avoidance using a drone.” In International Symposium on Visual Computing, pp. 857-866. Springer, Cham, 2015.
[20] H.M. Jayaweera, and S. Hanoun, “A Dynamic Artificial Potential Field (D-APF) UAV Path Planning Technique for Following Ground Moving Targets.” IEEE Access, 8, pp.192760-192776, 2020.
[21] “Gazebo.” http://gazebosim.org/ (accessed Apr. 15, 2019).
[22] “Gazebo Simulation PX4 v1.9.0 Developer Guide.” https://dev.px4.io/v1.9.0 noredirect/en/simulation/gazebo.html (accessed May 03, 2021).
[23] A. Koubaa, Robot operating system (ROS). New York, NY: Springer Berlin Heidelberg, vol. 1, 2018.
[24] “MAVLink Developer Guide.” https://mavlink.io/en/ (accessed Apr. 5, 2019).