Online decision making and path planning framework for safe operation of unmanned aerial vehicles in urban scenarios
As the potential for deploying low-flying unmanned aerial vehicles (UAVs) in urban spaces increases, ensuring their safe operations is becoming a major concern. Given the uncertainties in their operational environments caused by wind gusts, degraded state of health, and probability of collision with static and dynamic objects, it becomes imperative to develop online decision-making schemes to ensure safe flights. In this paper, we propose an online decision-making framework that takes into account the state of health of the UAV, the environmental conditions, and the obstacle map to assess the probability of mission failure and re-plan accordingly. The online re-planning strategy considers two situations: (1) updating the current trajectory to reduce the probability of collision; and (2) defining a new trajectory to find a new safe landing spot, if continued flight would result in risk values above a pre-specified threshold. The re-planning routine uses the differential evolution optimization method and takes into account the dynamics of the UAV and its components as well as the environmental wind conditions. The new trajectory generation routine combines probabilistic road-maps with B-spline smoothing to ensure a dynamically feasible trajectory. We demonstrate the effectiveness of our approach by running UAV flight simulation experiments in urban scenarios.
Online decision making, Unmanned aerial vehicles, Urban scenarios
Aggarwal, S., & Kumar, N. (2020). Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges. Computer Communications, 149(July 2019), 270–299.
Al-mashhadani, M. A. (2019). Optimal control and state estimation for unmanned aerial vehicle under random vibration and uncertainty. Measurement and Control, 1–8. doi: 10.1177/0020294019866860
Ancel, E., Capristan, F. M., Foster, J. V. (2017). Real-time Risk Assessment Framework for Unmanned Aircraft System (UAS) Traffic Management (UTM). In 17th aiaa aviation technology, integration, and operations conference (p. 3273). doi: 10.2514/6.2017-3273
Ancel, E., Capristan, F. M., Foster, J. V. (2019). In-Time Non-Participant Casualty Risk Assessment to Support Onboard Decision Making for Autonomous Unmanned Aircraft. In Aiaa aviation 2019 forum (p. 3063). doi: 10.2514/6.2019-3053
Benders, S., Schopferer, S., Nawrath, A. (2020). In-flight Kinematic Model Parameter Estimation and Adaptive Path Planning for Unmanned Aircraft. In Aiaa scitech 2020 forum (p. 0.135). doi: 10.2514/6.2020-0135
Brown, A., Rogers, J. (2016). A sampling-based probabilistic path planner for multirotor air vehicles in cluttered environments. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 231. doi: 10.1177/0954410016636912
Burri, M., Manuel, D., Achtelik, M. W., Siegwart, R. (2015). Robust State Estimation for Micro Aerial Vehicles Based on System Dynamics. In Ieee international conference on robotics and automation (icra) (pp. 5278–5283). Washington, D.C.
Chatterjee, A., Reza, H. (2019). Path planning algorithm to enable low altitude delivery drones at the city scale. In 2019 international conference on computational science and computational intelligence (csci) (pp. 750–753).
Clothier, R. A., Walker, R. A. (2006). Determination and evaluation of uav safety objectives.
Conte, G., Doherty, P. (2008). An integrated uav navigation system based on aerial image matching. In 2008 ieee aerospace conference (pp. 1–10).
Corke, P., Lobo, J., Dias, J. (2007). An Introduction to Inertial and Visual Sensing. The International Journal of Robotics Research, 26, 519–535. doi: 10.1177/0278364907079279
Coutinho, W., Battarra, M., Fliege, J. (2018, 04). The unmanned aerial vehicle routing and trajectory optimisation problem, a taxonomic review. Computers & Industrial Engineering, 120. doi: 10.1016/j.cie.2018.04.037
Daigle, M., Kulkarni, C. (2016). End-of-discharge and end-of-life prediction in lithium-ion batteries with electrochemistry-based aging models. AIAA Scitech Forum.
Das, S., Mullick, S., Suganthan, P. (2016). Recent advances in differential evolution an updated survey. Swarm and Evolutionary Computation, 27. doi: https://doi.org/10.1016/j.swevo.2016.01.004
De Filippis, L., Guglieri, G., Quagliotti, F. (2011). A minimum risk approach for path planning of uavs. Journal of Intelligent and Robotic Systems, 61, 203-219. doi: 10.1007/s10846-010-9493-9
Diebel, J. (2006). Representing attitude: Euler angles, unit quaternions, and rotation vectors. Matrix, 15–16(58), 1–35.
D’Souza, S., Ishihara, A., Nikaido, B., & Hasseeb, H. (2016). Feasibility of varying geo-fence around an unmanned aircraft operation based on vehicle performance and wind. In 2016 ieee/aiaa 35th digital avionics systems conference (dasc) (pp. 1–10).
Farin, G. (2014). Curves and Surfaces for Computer Aided Geometric Design: A Practical Guide. Elsevier.
Gougeon, O., Nguyen, D.-t., & E, D. S. (2018). Modeling and Control of a Quadcopter Flying in a Wind Field: A Comparison Between LQR and Structured H Control Techniques. 2018 International Conference on Unmanned Aircraft Systems (ICUAS)(1), 1408–1417. doi: 10.1109/ICUAS.2018.8453402
Gross, J., Gu, Y., Gururajan, S., Seanor, B., Napolitano, M. R. (2010). A Comparison of Extended Kalman Filter, Sigma-Point Kalman Filter, and Particle Filter in GPS/INS Sensor Fusion. In Aiaa guidance, navigation, and control conference (p. 8332).
Hsu, D., Latombe, J.-C., Kurniawati, H. (2006). On the probabilistic foundations of probabilistic roadmap planning. The International Journal of Robotics Research, 25. doi: 10.1177/0278364906067174
Hu, J., Erzberger, H., Goebel, K., Liu, Y. (2020). Probabilistic Risk-Based Operational Safety Bound for Rotary-Wing Unmanned Aircraft Systems Traffic Management. Journal of Aerospace Information Systems, 17(3), 171–181.
Kavraki, L. E., Svestka, P., Latombe, J. ., Overmars, M. H. (1996). Probabilistic roadmaps for path planning in highdimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12.
Kehlenbeck, A. G. (2014). Quaternion-based control for aggressive trajectory tracking with a micro-quadrotor uav (Unpublished doctoral dissertation).
Khorasgani, H., Biswas, G., Sankararaman, S. (2016). Methodologies for system-level remaining useful life prediction. Reliability Engineering & System Safety, 154, 8–18.
Krishnan, P. S., Manimala, K. (2020). Implementation of optimized dynamic trajectory modification algorithm to avoid obstacles for secure navigation of UAV. Applied Soft Computing, 90, 106168.
Kuchar, J. K. (2005). Safety analysis methodology for unmanned aerial vehicle (uav) collision avoidance systems. In Usa/europe air traffic management r&d seminars (Vol. 12).
Kulkarni, C., Corbetta, M. (2019). Health management and prognostics for electric aircraft powertrain. AIAA.
Lin, C., Shao, P.-C. (2020). Failure analysis for an unmanned aerial vehicle using safe path planning. Journal of Aerospace Information Systems, 1-12. doi: 10.2514/1.I010795
Lusk, P. C., Glaab, P. C., Glaab, L. J., & Beard, R. W. (2019). Safe2Ditch: Emergency Landing for Small Unmanned Aircraft Systems. Journal of Aerospace Information Systems, 19(8), 327–329.
Mahony, R., Kumar, V., Corke, P. (2012). Modeling, Estimation, and Control of Quadrotor. (August).
Merwe, R. V. D., Wan, E. A., Julier, S. I. (2004). Sigmapoint Kalman filters for nonlinear estimation and sensorfusion: Applications to integrated navigation. In Aiaa guidance, navigation, and control conference and exhibit (p. 5120). Washington, D.C.
Moir, I., & Seabridge, A. (2012). Design and development of aircraft systems (Vol. 67). John Wiley & Sons.
Plett, G. L. (2006). Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 2: Simultaneous state and parameter estimation. Journal of Power Resources, 161, 1369–1384.
Plett, G. L. (2015). Battery Management Systems Volume 2: Equivalent-Circuit Methods. Artech House.
Powers, C., Mellinger, D., Kumkar, V. (2015). Quadcopter kinematics and dynamics. Handbook of Unmanned Aerial Vehicles.
Primatesta, S., Guglieri, G., Rizzo, A. (2019). A risk-aware path planning strategy for UAVs in urban environments. Journal of Intelligent & Robotic Systems, 95(2), 629–643.
Rubio-Hervas, J., Gupta, A., Ong, Y.-S. (2018). Data-driven risk assessment and multicriteria optimization of uav operations. Aerospace Science and Technology, 77, 510 - 523. doi: 10.1016/j.ast.2018.04.001
Schacht-Rodríguez, R., Ponsart, J. C., García-Beltrán, C. D., Astorga-Zaragoza, C. M., Theilliol, D., Zhang, Y. (2018). Path Planning Generation Algorithm for a Class of UAV Multirotor Based on State of Health of Lithium Polymer Battery. Journal of Intelligent and Robotic Systems: Theory and Applications, 91(1), 115–131.
Sierra, G., Orchard, M., Goebel, K., Kulkarni, C. (2019). Battery health management for small-size rotary-wing electric unmanned aerial vehicles: An efficient approach for constrained computing platforms. Reliability Engineering & System Safety, 182, 166–178.
Storn, R., Price, K. (1995). Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11. doi: https://doi.org/10.1023/A:1008202821328
Yang, H., Lee, Y., Dongjun, S.-y. J. (2017). Multi-rotor drone tutorial: systems , mechanics , control and state estimation. Intelligent Service Robotics, 10(2), 79–93. doi: 10.1007/s11370-017-0224-y
Zammit, C., & Van Kampen, E.-J. (2020). Comparison of a* and rrt in real–time 3d path planning of uavs. In Aiaa scitech 2020 forum (p. 0861).
Zhu, G., Wei, P. (2016). Low-altitude UAS traffic coordination with dynamic geofencing. In 16th aiaa aviation technology, integration, and operations conference (p. 3453).