A Risk-Aware Path Planning Strategy for UAVs in Urban Environments

This paper presents a risk-aware path planning strategy for Unmanned Aerial Vehicles in urban environments. The aim is to compute an effective path that minimizes the risk to the population, thus enforcing safety of flight operations over inhabited areas. To quantify the risk, the proposed approach uses a risk-map that associates discretized locations of the space with a suitable risk-cost. Path planning is performed in two phases: first, a tentative path is computed off-line on the basis on the information related to static risk factors; then, using a dynamic risk-map, an on-line path planning adjusts and adapts the off-line path to dynamically arising conditions. Off-line path planning is performed using riskA*, an ad-hoc variant of the A* algorithm, which aims at minimizing the risk. While off-line path planning has no stringent time constraints for its execution, this is not the case for the on-line phase, where a fast response constitutes a critical design parameter. We propose a novel algorithm called Borderland, which uses the check and repair approach to rapidly identify and adjust only the portion of path involved by the inception of relevant dynamical changes in the risk factor. After the path planning, a smoothing process is performed using Dubins curves. Simulation results confirm the suitability of the proposed approach.

[1]  Anders la Cour-Harbo,et al.  Waypoint planning with Dubins curves using genetic algorithms , 2016, 2016 European Control Conference (ECC).

[2]  Kimon P. Valavanis,et al.  Unmanned Aircraft Systems Regulation , 2012 .

[3]  Reece A. Clothier,et al.  A Casualty Risk Analysis For Unmanned Aerial System (UAS) Operations Over Inhabited Areas , 2007 .

[4]  Andrey V. Savkin,et al.  The problem of minimum risk path planning for flying robots in dangerous environments , 2016, 2016 35th Chinese Control Conference (CCC).

[5]  Robin R. Murphy,et al.  Introduction to AI Robotics , 2000 .

[6]  Sebastian Thrun,et al.  ARA*: Anytime A* with Provable Bounds on Sub-Optimality , 2003, NIPS.

[7]  D. Rathbun,et al.  An evolution based path planning algorithm for autonomous motion of a UAV through uncertain environments , 2002, Proceedings. The 21st Digital Avionics Systems Conference.

[8]  Maxim Likhachev,et al.  Dynamic Multi-Heuristic A* , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Seyedshams Feyzabadi,et al.  Risk-aware path planning using hirerachical constrained Markov Decision Processes , 2014, 2014 IEEE International Conference on Automation Science and Engineering (CASE).

[10]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[11]  Anders la Cour-Harbo.  Quantifying Risk of Ground Impact Fatalities for Small Unmanned Aircraft , 2019, J. Intell. Robotic Syst..

[12]  Geoffrey A. Hollinger,et al.  Risk‐aware Path Planning for Autonomous Underwater Vehicles using Predictive Ocean Models , 2013, J. Field Robotics.

[13]  Emilio Frazzoli,et al.  Incremental Sampling-based Algorithms for Optimal Motion Planning , 2010, Robotics: Science and Systems.

[14]  Ole B. Jensen.  Drone City: power, design and aerial mobility in the age of 'smart cities' , 2016 .

[15]  Imad Jawhar,et al.  UAVs for smart cities: Opportunities and challenges , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).

[16]  Maxim Likhachev,et al.  A*-Connect: Bounded suboptimal bidirectional heuristic search , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[17]  S. Saripalli,et al.  Path planning using 3D Dubins Curve for Unmanned Aerial Vehicles , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).

[18]  Patrick Doherty,et al.  Reconfigurable Path Planning for an Autonomous Unmanned Aerial Vehicle , 2006, 2006 International Conference on Hybrid Information Technology.

[19]  Srikanth Saripalli,et al.  Sampling-Based Path Planning for UAV Collision Avoidance , 2017, IEEE Transactions on Intelligent Transportation Systems.

[20]  Wanquan Liu,et al.  Geometric Reinforcement Learning for Path Planning of UAVs , 2015, J. Intell. Robotic Syst..

[21]  L. Dubins.  On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents , 1957 .

[22]  Kimon P. Valavanis,et al.  On Integrating Unmanned Aircraft Systems into the National Airspace System: Issues, Challenges, Operational Restrictions, Certification, and Recommendations , 2011 .

[23]  Nidhi Kalra,et al.  Replanning with RRTs , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[24]  Giorgio Guglieri,et al.  Operation Oriented Path Planning Strategies for Rpas , 2015 .

[25]  P. B. Sujit,et al.  Unmanned Aerial Vehicle Path Following: A Survey and Analysis of Algorithms for Fixed-Wing Unmanned Aerial Vehicless , 2014, IEEE Control Systems.

[26]  Giorgio Guglieri,et al.  A Minimum Risk Approach for Path Planning of UAVs , 2011, J. Intell. Robotic Syst..

[27]  Anders la Cour-Harbo.  Mass threshold for ‘harmless’ drones: , 2017 .

[28]  Claudio Fabiano Motta Toledo,et al.  Heuristic and Genetic Algorithm Approaches for UAV Path Planning under Critical Situation , 2017, Int. J. Artif. Intell. Tools.

[29]  Guowei Cai,et al.  A Survey of Small-Scale Unmanned Aerial Vehicles: Recent Advances and Future Development Trends , 2014 .

[30]  S. LaValle.  Rapidly-exploring random trees : a new tool for path planning , 1998 .

[31]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[32]  Zhaodan Kong,et al.  A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance , 2010, J. Intell. Robotic Syst..

[33]  Jeffrey W. Herrmann,et al.  Risk-Based Path Planning Optimization Methods for Unmanned Aerial Vehicles Over Inhabited Areas1 , 2016, J. Comput. Inf. Sci. Eng..

[34]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[35]  Xiaohong Su,et al.  Online UAV path planning in uncertain and hostile environments , 2015, International Journal of Machine Learning and Cybernetics.

[36]  Emilio Frazzoli,et al.  Optimal kinodynamic motion planning using incremental sampling-based methods , 2010, 49th IEEE Conference on Decision and Control (CDC).

[37]  Lydia E. Kavraki,et al.  Probabilistic roadmaps for path planning in high-dimensional configuration spaces , 1996, IEEE Trans. Robotics Autom..

[38]  Elisa Capello,et al.  A cloud-based framework for risk-aware intelligent navigation in urban environments , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[39]  Anthony Stentz.  Optimal and Efficient Path Planning for Unknown and Dynamic Environments , 1993 .

[40]  Giorgio Guglieri,et al.  Path Planning Strategies for UAVS in 3D Environments , 2011, Journal of Intelligent & Robotic Systems.

[41]  Giorgio Guglieri,et al.  Safety Assessment for Light Remotely Piloted Aircraft Systems , 2016 .

[42]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[43]  Luigi del Re,et al.  Global real-time path planning for UAVs in uncertain environment , 2006, 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control.

[44]  Vladimir J. Lumelsky,et al.  Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape , 1987, Algorithmica.

[45]  Jizhong Xiao,et al.  A literature review of UAV 3D path planning , 2014, Proceeding of the 11th World Congress on Intelligent Control and Automation.