Low-Altitude Navigation for Multi-Rotor Drones in Urban Areas

Multi-rotor drones have witnessed a drastic usage increase in several smart city applications due to their 3D mobility, flexibility, and low cost. Collectively, they can be used to accomplish different short- and long-term missions that require low-altitude motion in urban areas. Therefore, it is important to efficiently manage the operation of the fleet to leverage its use and maximize its application performances. In this paper, we propose to investigate the path routing problem for the multiple drones in urban areas, where obstacles with different heights exist. The objective is to find the best trajectories in this 3D environment while ensuring collision-free navigation. The collision is prevented by three possible alternatives: forcing the drone to statically hover, so its peer can pass first, making it fly at a different altitude, or completely changing its path. Multiple charging stations are made available to allow the drones to recharge their batteries when needed. A mixed integer linear program is first developed to model the problem and achieve optimal navigation of the fleet. Afterward, two heuristic algorithms with different conceptual constructions are designed to solve the trajectory planning problem with faster convergence speed. The selected simulation results illustrate the performance of our framework in realistic 3D maps and show that the designed heuristic approaches provide close performances to the optimal ones.

[1]  Zhiyong Cui,et al.  Real-Time Bidirectional Traffic Flow Parameter Estimation From Aerial Videos , 2017, IEEE Transactions on Intelligent Transportation Systems.

[2]  José Antonio Lozano,et al.  Path Planning for Single Unmanned Aerial Vehicle by Separately Evolving Waypoints , 2015, IEEE Transactions on Robotics.

[3]  Wei Li,et al.  A 3D path planning approach for quadrotor UAV navigation , 2015, 2015 IEEE International Conference on Information and Automation.

[4]  Sang-Jo Yoo,et al.  Optimal UAV Path Planning: Sensing Data Acquisition Over IoT Sensor Networks Using Multi-Objective Bio-Inspired Algorithms , 2018, IEEE Access.

[5]  Hakim Ghazzai,et al.  On the Placement of UAV Docking Stations for Future Intelligent Transportation Systems , 2017, 2017 IEEE 85th Vehicular Technology Conference (VTC Spring).

[6]  M. Sniedovich Dijkstra's algorithm revisited: the dynamic programming connexion , 2006 .

[7]  Hakim Ghazzai,et al.  Optimal Collision-Free Navigation for Multi-Rotor UAV Swarms in Urban Areas , 2019, 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring).

[8]  Mingyue Ding,et al.  3D multipath planning for UAV based on network graph , 2011 .

[9]  Sang-Jo Yoo,et al.  Flying path optimization in UAV-assisted IoT sensor networks , 2016, ICT Express.

[10]  Marco Di Felice,et al.  When UAVs Ride A Bus: Towards Energy-efficient City-scale Video Surveillance , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[11]  Sebastian Magierowski,et al.  Vehicle Routing Problems for Drone Delivery , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[12]  Maxim A. Dulebenets Application of Evolutionary Computation for Berth Scheduling at Marine Container Terminals: Parameter Tuning Versus Parameter Control , 2018, IEEE Transactions on Intelligent Transportation Systems.

[13]  Donald B. Johnson,et al.  Efficient Algorithms for Shortest Paths in Sparse Networks , 1977, J. ACM.

[14]  Khaled A. Harras,et al.  Energy efficient path planning techniques for UAV-based systems with space discretization , 2016, 2016 IEEE Wireless Communications and Networking Conference.

[15]  Han-Lim Choi,et al.  Optimal control-based UAV path planning with dynamically-constrained TSP with neighborhoods , 2016, 2017 17th International Conference on Control, Automation and Systems (ICCAS).

[16]  Michele Garraffa,et al.  Drones path planning for WSN data gathering: A column generation heuristic approach , 2018, 2018 IEEE Wireless Communications and Networking Conference (WCNC).

[17]  Halil Yetgin,et al.  Analysis and Optimization of Unmanned Aerial Vehicle Swarms in Logistics: An Intelligent Delivery Platform , 2019, IEEE Access.

[18]  James R. Morrison,et al.  On the Scheduling of Systems of UAVs and Fuel Service Stations for Long-Term Mission Fulfillment , 2013, J. Intell. Robotic Syst..

[19]  Yi Zhou,et al.  Multi-UAV-Aided Networks: Aerial-Ground Cooperative Vehicular Networking Architecture , 2015, IEEE Vehicular Technology Magazine.

[20]  Hakim Ghazzai,et al.  An exploratory search strategy for data routing in flying ad hoc networks , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[21]  Ismail Güvenç,et al.  UAV-Enabled Intelligent Transportation Systems for the Smart City: Applications and Challenges , 2017, IEEE Communications Magazine.

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

[23]  Donghyun Kim,et al.  On Theoretical Trajectory Planning of Multiple Drones To Minimize Latency in Search-and-Reconnaissance Operations , 2017, IEEE Transactions on Mobile Computing.

[24]  Fuchun Sun,et al.  Evolutionary route planner for unmanned air vehicles , 2005, IEEE Transactions on Robotics.

[25]  Anupam Shukla,et al.  Vehicle Routing Problem with Time Windows Using Meta-Heuristic Algorithms: A Survey , 2018, Harmony Search and Nature Inspired Optimization Algorithms.

[26]  Kaarthik Sundar,et al.  Algorithms for Routing an Unmanned Aerial Vehicle in the Presence of Refueling Depots , 2013, IEEE Transactions on Automation Science and Engineering.

[27]  Xiangjie Kong,et al.  Collision Avoidance in Fixed-Wing UAV Formation Flight Based on a Consensus Control Algorithm , 2018, IEEE Access.

[28]  Hakim Ghazzai,et al.  A Generic Spatiotemporal UAV Scheduling Framework for Multi-Event Applications , 2019, IEEE Access.

[29]  Jie Xu,et al.  Energy Minimization for Wireless Communication With Rotary-Wing UAV , 2018, IEEE Transactions on Wireless Communications.

[30]  Masoud Daneshtalab,et al.  Using Optimization, Learning, and Drone Reflexes to Maximize Safety of Swarms of Drones , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[31]  Mani Shankar Prasad,et al.  Three dimensional D* algorithm for incremental path planning in uncooperative environment , 2016, 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN).

[32]  Sang-Jo Yoo,et al.  Robust and Reliable Predictive Routing Strategy for Flying Ad-Hoc Networks , 2017, IEEE Access.

[33]  David M. Lane,et al.  An Evolutionary Algorithm for Online, Resource-Constrained, Multivehicle Sensing Mission Planning , 2018, IEEE Robotics and Automation Letters.

[34]  David Eppstein,et al.  Randomized Speedup of the Bellman-Ford Algorithm , 2011, ANALCO.

[35]  Jizhong Xiao,et al.  Guiding attraction based random tree path planning under uncertainty: Dedicate for UAV , 2014, 2014 IEEE International Conference on Mechatronics and Automation.

[36]  Randal W. Beard,et al.  Cooperative Path Planning for Target Tracking in Urban Environments Using Unmanned Air and Ground Vehicles , 2015, IEEE/ASME Transactions on Mechatronics.