Comparison between A* and RRT Algorithms for UAV Path Planning

Unmanned Aerial Vehicles (UAVs) are being integrated into a wide range of indoor and outdoor applications. In this light, robust and ecient path planning is paramount. An extensive literature review showed that the A* and Rapidly{Exploring Random Tree (RRT) algorithms and their variants are the most promising path planning algorithms candidates for 3D UAV scenarios. These two algorithms are tested in dierent complexity 3D scenarios consisting of a box and a combination of vertical and horizontal plane obstacles with apertures. The path length and generation time are considered as the performance measures. The A* with a spectrum of resolutions, the standard RRT with dierent step{ size constraints, RRT without step size constraints and the Multiple RRT (MRRT) with various seeds are implemented and their performance measures compared. Results conrm that all algorithms are able to generate a path in all scenarios for all resolutions, step sizes and seeds considered, respectively. Overall A*'s path length is more optimal and generation time is shorter than RRT projecting A* as a better candidate for online 3D path planning of UAVs.

[1]  Mark H. Overmars,et al.  Creating High-quality Paths for Motion Planning , 2007, Int. J. Robotics Res..

[2]  Steven M. LaValle,et al.  RRT-connect: An efficient approach to single-query path planning , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[3]  Patrick G. Xavier,et al.  Lazy Reconfiguration Forest (LRF) - An Approach for Motion Planning with Multiple Tasks in Dynamic Environments , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[4]  Anthony J. Barbera,et al.  Trajectory Generation for an On-Road Autonomous Vehicle , 2006 .

[5]  Lydia E. Kavraki,et al.  Anytime solution optimization for sampling-based motion planning , 2013, 2013 IEEE International Conference on Robotics and Automation.

[6]  Eliot Winer,et al.  Path Planning of Unmanned Aerial Vehicles using B-Splines and Particle Swarm Optimization , 2009, J. Aerosp. Comput. Inf. Commun..

[7]  Sven Koenig,et al.  Incremental A* , 2001, NIPS.

[8]  Emilio Frazzoli,et al.  Real-Time Motion Planning for Agile Autonomous Vehicles , 2000 .

[9]  Leo Dorst,et al.  Differential A* , 2002, IEEE Trans. Knowl. Data Eng..

[10]  Seokcheon Lee,et al.  Response Threshold Model Based UAV Search Planning and Task Allocation , 2014, J. Intell. Robotic Syst..

[11]  Walter Fichter,et al.  Spline and OBB-based Path-Planning for Small UAVs with the Finite Receding-Horizon Incremental-Sampling Tree Algorithm , 2013 .

[12]  Li-Der Chou,et al.  A Star Search Algorithm for Civil UAV Path Planning with 3G Communication , 2014, 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[13]  Anthony Stentz,et al.  Anytime RRTs , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Nadjib Achir,et al.  Path planning of unmanned aerial vehicles with terrestrial wireless network tracking , 2016, 2016 Wireless Days (WD).

[15]  Mihai Pomarlan,et al.  Motion planning for manipulators in dynamically changing environments using real-time mapping of free workspace , 2013, 2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI).

[16]  Wei Shang,et al.  Computational path planner for product assembly in complex environments , 2013, Chinese Journal of Mechanical Engineering.

[17]  Jee-Hwan Ryu,et al.  Development and Experiences of an Autonomous Vehicle for High-Speed Navigation and Obstacle Avoidance , 2013, Frontiers of Intelligent Autonomous Systems.

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

[19]  J. Karl Hedrick,et al.  Autonomous UAV path planning and estimation , 2009, IEEE Robotics & Automation Magazine.

[20]  Steven M. LaValle,et al.  Randomized Kinodynamic Planning , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[21]  Peter King,et al.  Odin: Team VictorTango's entry in the DARPA Urban Challenge , 2008, J. Field Robotics.

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

[23]  Anthony Stentz,et al.  Anytime, Dynamic Planning in High-dimensional Search Spaces , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[24]  Lakhmi C. Jain,et al.  Multiple UAVs path planning algorithms: a comparative study , 2008, Fuzzy Optim. Decis. Mak..

[25]  Duncan A. Campbell,et al.  Multi-Objective Four-Dimensional Vehicle Motion Planning in Large Dynamic Environments , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[26]  Sven Koenig,et al.  Generalized Adaptive A* , 2008, AAMAS.

[27]  Daniel D. Lee,et al.  Little Ben: The Ben Franklin Racing Team's entry in the 2007 DARPA Urban Challenge , 2008, J. Field Robotics.

[28]  Somayé Ghandi,et al.  Review and taxonomies of assembly and disassembly path planning problems and approaches , 2015, Comput. Aided Des..

[29]  Sean Luke,et al.  Tunably decentralized algorithms for cooperative target observation , 2005, AAMAS '05.

[30]  Didier Devaurs,et al.  Enhancing the transition-based RRT to deal with complex cost spaces , 2013, 2013 IEEE International Conference on Robotics and Automation.

[31]  Jean-Loup Farges,et al.  Cell-RRT: Decomposing the environment for better plan , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[32]  Rodrigo Benenson,et al.  Integrating Perception and Planning for Autonomous Navigation of Urban Vehicles , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[33]  Kostas E. Bekris,et al.  Greedy but Safe Replanning under Kinodynamic Constraints , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[34]  Oliver Brock,et al.  Elastic roadmaps—motion generation for autonomous mobile manipulation , 2010, Auton. Robots.

[35]  Anthony Stentz,et al.  Using interpolation to improve path planning: The Field D* algorithm , 2006, J. Field Robotics.

[36]  Stefan B. Williams,et al.  Large-scale path planning for Underwater Gliders in ocean currents , 2009 .

[37]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[38]  L. Shepp,et al.  OPTIMAL PATHS FOR A CAR THAT GOES BOTH FORWARDS AND BACKWARDS , 1990 .

[39]  Manuela M. Veloso,et al.  Real-time randomized path planning for robot navigation , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[40]  Qidan Zhu,et al.  An Improved Anytime RRTs Algorithm , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[41]  Emilio Frazzoli,et al.  Sampling-based algorithms for optimal motion planning , 2011, Int. J. Robotics Res..

[42]  Chun-Liang Lin,et al.  Development of Flight Path Planning for Multirotor Aerial Vehicles , 2015 .

[43]  Joseph S. B. Mitchell,et al.  The weighted region problem: finding shortest paths through a weighted planar subdivision , 1991, JACM.

[44]  Lydia E. Kavraki,et al.  Path planning using lazy PRM , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[45]  Alberto Broggi,et al.  The TerraMax autonomous vehicle , 2006, J. Field Robotics.

[46]  Ivan Petrovic,et al.  Real-time Approximation of Clothoids With Bounded Error for Path Planning Applications , 2014, IEEE Transactions on Robotics.

[47]  Debasish Ghose,et al.  Two-agent cooperative search using game models with endurance-time constraints , 2010 .

[48]  Anthony Stentz,et al.  The Focussed D* Algorithm for Real-Time Replanning , 1995, IJCAI.

[49]  Sebastian Thrun,et al.  Anytime search in dynamic graphs , 2008, Artif. Intell..

[50]  Emmanouil Tsardoulias,et al.  A Review of Global Path Planning Methods for Occupancy Grid Maps Regardless of Obstacle Density , 2016, J. Intell. Robotic Syst..

[51]  Reid G. Simmons,et al.  Approaches for heuristically biasing RRT growth , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[52]  Maxim Likhachev,et al.  Truncated incremental search , 2016, Artif. Intell..

[53]  Debasish Ghose,et al.  Search by UAVs with Flight Time Constraints using Game Theoretical Models , 2005 .

[54]  Chao Cai,et al.  A New Cloud Model Based Human-Machine Cooperative Path Planning Method , 2015, J. Intell. Robotic Syst..

[55]  Sven Koenig,et al.  Dynamic fringe-saving A* , 2009, AAMAS.

[56]  J.K. Hedrick,et al.  A mode-switching path planner for UAV-assisted search and rescue , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[57]  Sven Koenig,et al.  Incremental Phi*: Incremental Any-Angle Path Planning on Grids , 2009, IJCAI.

[58]  Qingquan Li,et al.  Hierarchical route planning based on taxi GPS-trajectories , 2009, 2009 17th International Conference on Geoinformatics.

[59]  Jonathan Schaeffer,et al.  Block A*: Database-Driven Search with Applications in Any-Angle Path-Planning , 2011, AAAI.

[60]  Zengxi Pan,et al.  Recent progress on sampling based dynamic motion planning algorithms , 2016, 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).

[61]  N. Kwok,et al.  Evaluating Performance of Multiple RRTs , 2008, 2008 IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications.

[62]  Didier Devaurs,et al.  Optimal Path Planning in Complex Cost Spaces With Sampling-Based Algorithms , 2016, IEEE Transactions on Automation Science and Engineering.

[63]  Ron Alterovitz,et al.  Rapidly-exploring roadmaps: Weighing exploration vs. refinement in optimal motion planning , 2011, 2011 IEEE International Conference on Robotics and Automation.

[64]  E. Feron,et al.  Real-time motion planning for agile autonomous vehicles , 2000, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[65]  Andras Sobester,et al.  An intelligent, heuristic path planner for multipleagent unmanned air systems , 2015 .

[66]  Csaba Szepesvári,et al.  Extending rapidly-exploring random trees for asymptotically optimal anytime motion planning , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[67]  Rui Rodrigues,et al.  Smooth trajectory planning for fully automated passengers vehicles - spline and clothoid based methods and its simulation , 2006, ICINCO-RA.

[68]  Yasar Ayaz,et al.  RRT*-SMART: A Rapid Convergence Implementation of RRT* , 2013 .

[69]  Han-Lim Choi,et al.  Learning covariance dynamics for path planning of UAV sensors in a large-scale dynamic environment , 2009 .

[70]  Ariel Felner,et al.  Theta*: Any-Angle Path Planning on Grids , 2007, AAAI.

[71]  Sven Koenig,et al.  Any-Angle Path Planning , 2013, AI Mag..

[72]  Sean R. Martin,et al.  Offline and Online Evolutionary Bi-Directional RRT Algorithms for Efficient Re-Planning in Dynamic Environments , 2007, 2007 IEEE International Conference on Automation Science and Engineering.

[73]  Pedro Meseguer,et al.  Tree Adaptive A , 2011, AAMAS.

[74]  Emilio Frazzoli,et al.  RRTX: Asymptotically optimal single-query sampling-based motion planning with quick replanning , 2016, Int. J. Robotics Res..

[75]  Dinesh Manocha,et al.  Reactive deformation roadmaps: motion planning of multiple robots in dynamic environments , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[76]  David González,et al.  A Review of Motion Planning Techniques for Automated Vehicles , 2016, IEEE Transactions on Intelligent Transportation Systems.

[77]  Tsai-Yen Li,et al.  An incremental learning approach to motion planning with roadmap management , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[78]  Anjan Chakrabarty,et al.  Energy Maps for Long-Range Path Planning for Small- and Micro - UAVs , 2009 .

[79]  Leo Hartman,et al.  Anytime dynamic path-planning with flexible probabilistic roadmaps , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[80]  Ran Dai,et al.  Path Planning and State Estimation for Unmanned Aerial Vehicles in Hostile Environments , 2010 .

[81]  J. D. Bo,et al.  A Fast and Efcient Approach to Path Planning for Unmanned Vehicles , 2006 .

[82]  Martin Buss,et al.  Cell-based Probabilistic Roadmaps (CPRM) for Efficient Path Planning in Large Environments , 2007 .

[83]  S. LaValle,et al.  Randomized Kinodynamic Planning , 2001 .

[84]  Jonathan P. How,et al.  Real-Time Motion Planning With Applications to Autonomous Urban Driving , 2009, IEEE Transactions on Control Systems Technology.

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

[86]  Scott Alan Hutchinson,et al.  Toward real-time path planning in changing environments , 2000 .

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

[88]  Alban Grastien,et al.  Online Graph Pruning for Pathfinding On Grid Maps , 2011, AAAI.

[89]  L. Ryan Lewis,et al.  Collision-Free Multi-UAV Optimal Path Planning and Cooperative Control for Tactical Applications , 2008 .

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

[91]  James J. Kuffner,et al.  Multipartite RRTs for Rapid Replanning in Dynamic Environments , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[92]  Rahul Kala,et al.  Multi-Level Planning for Semi-autonomous Vehicles in Traffic Scenarios Based on Separation Maximization , 2013, J. Intell. Robotic Syst..