Fuzzy Kinodynamic RRT: a Dynamic Path Planning and Obstacle Avoidance Method

Path planning is the essential capability for autonomous navigation of UAV (Unmanned Aerial Vehicle) in unknown environments. In this paper, a Fuzzy logic inferencing system has been designed to achieve obstacle avoidance in a dynamic environment. We introduce Fuzzy-Kinodynamic RRT, method which generates dynamic path based on the traditional rapidly exploring random tree (RRT) algorithm. A set of simple Fuzzy rules are proposed for simple 2D and 3D path planning cases. It is an optimized path planning method which uses Kinodynamic RRT algorithm [1] [2] to do global path planning and utilizes Fuzzy logic to avoid obstacles. A set of heuristics Fuzzy rules are proposed to lead the UAV away from un-modeled ground-based obstacles and to guide the UAV towards the goal. In addition, the designed Fuzzy rules can augment traditional RRT for dealing with new obstacles in the environment. Various simulations are conducted in 2D and 3D environment and the results illustrate the effectiveness of the algorithm in simple dynamic environment.

[1]  Shawn Keshmiri,et al.  Flight Test Validation of Collision and Obstacle Avoidance in Fixed-Wing UASs with High Speeds Using Morphing Potential Field , 2018, 2018 International Conference on Unmanned Aircraft Systems (ICUAS).

[2]  Kostas E. Bekris,et al.  Sampling-based roadmap of trees for parallel motion planning , 2005, IEEE Transactions on Robotics.

[3]  Abraham Sánchez López,et al.  Sensor-based probabilistic roadmaps for car-like robots , 2004, Proceedings of the Fifth Mexican International Conference in Computer Science, 2004. ENC 2004..

[4]  QING XUE,et al.  Determining the path search graph and finding a collision-free path by the modified A* algorithm for a 5-link closed chain , 1995, Appl. Artif. Intell..

[5]  Subodh Bhandari,et al.  Potential flow field navigation with virtual force field for UAS collision avoidance , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

[6]  Tong He,et al.  Integrated Collision Avoidance for Unmanned Aircraft Systems with Harmonic Potential Field and Haptic Input , 2020, 2020 IEEE/SICE International Symposium on System Integration (SII).

[7]  Robert Penicka,et al.  Sampling-based motion planning of 3D solid objects guided by multiple approximate solutions , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[8]  Lotfi A. Zadeh Toward a restructuring of the foundations of fuzzy logic (FL) , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[9]  E. H. Mandami Application of Fuzzy Logic to Approximate Reasoning using Linguistic Synthesis , 1977 .

[10]  Nanning Zheng,et al.  A fast RRT algorithm for motion planning of autonomous road vehicles , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[11]  Rosli Omar,et al.  A Review on Energy Efficient Path Planning Algorithms for Unmanned Air Vehicles , 2018, Lecture Notes in Electrical Engineering.

[12]  J. How,et al.  Improving the Efficiency of Rapidly-exploring Random Trees Using a Potential Function Planner , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[13]  Tharindu Fernando,et al.  Fuzzy logic based mobile robot target tracking in dynamic hostile environment , 2015, 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA).

[14]  Jur P. van den Berg,et al.  Kinodynamic RRT*: Asymptotically optimal motion planning for robots with linear dynamics , 2013, 2013 IEEE International Conference on Robotics and Automation.

[15]  Zhongyu Zhao,et al.  A Cascaded Fuzzy Model of Friction over Large Temperature Variation , 2006, NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society.

[16]  Lydia E. Kavraki,et al.  Analysis of probabilistic roadmaps for path planning , 1998, IEEE Trans. Robotics Autom..

[17]  Amin Mahdizadeh,et al.  Density Avoided Sampling: An Intelligent Sampling Technique for Rapidly-Exploring Random Trees , 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

[18]  Miguel Hernando,et al.  Expert-Guided Kinodynamic RRT Path Planner for Non-Holonomic Robots , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[19]  Jizhong Xiao,et al.  3D PRM based real-time path planning for UAV in complex environment , 2012, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[20]  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).

[21]  Hyungpil Moon,et al.  An RRT* path planning for kinematically constrained hyper-redundant inpipe robot , 2015, 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[22]  A. Sarkar,et al.  Development of a fuzzy logic based mobile robot for dynamic obstacle avoidance and goal acquisition in an unstructured environment , 2005, Proceedings, 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics..

[23]  Sarah Eichmann,et al.  Fuzzy Logic Intelligence Control And Information , 2016 .

[24]  Max Q.-H. Meng,et al.  Variant step size RRT: An efficient path planner for UAV in complex environments , 2016, 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR).

[25]  Wang Xinyu,et al.  Bidirectional Potential Guided RRT* for Motion Planning , 2019, IEEE Access.

[26]  Long Chen,et al.  A Fast and Efficient Double-Tree RRT$^*$-Like Sampling-Based Planner Applying on Mobile Robotic Systems , 2018, IEEE/ASME Transactions on Mechatronics.

[27]  Yong Xu,et al.  An Improved Path Planning Algorithm for Unmanned Aerial Vehicle Based on RRT-Connect , 2018, 2018 37th Chinese Control Conference (CCC).

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

[29]  Kevin Warwick,et al.  Planning of multiple autonomous vehicles using RRT , 2011, 2011 IEEE 10th International Conference on Cybernetic Intelligent Systems (CIS).

[30]  Xiong Chen,et al.  Path planning approach based on probabilistic roadmap for sensor based car-like robot in unknown environments , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

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

[32]  Ninad Pradhan,et al.  Robot crowd navigation using predictive position fields in the potential function framework , 2011, Proceedings of the 2011 American Control Conference.

[33]  Katie Byl,et al.  Smooth RRT-connect: An extension of RRT-connect for practical use in robots , 2015, 2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA).

[34]  Lydia E. Kavraki,et al.  Analysis of probabilistic roadmaps for path planning , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[35]  Guangbing Zhou,et al.  Research on the Fuzzy Algorithm of Path Planning of Mobile Robot , 2017, 2017 International Conference on Computer Systems, Electronics and Control (ICCSEC).

[36]  Michio Sugeno,et al.  Fuzzy Control of Model Car , 1985 .

[37]  Lin Wang,et al.  A study on obstacle avoidance for mobile robot based on fuzzy logic control and adaptive rotation , 2012, Proceedings of the 10th World Congress on Intelligent Control and Automation.

[38]  Xiuxia Sun,et al.  A Real-Time UAV Route Planning Algorithm Based on Fuzzy Logic Techniques , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[39]  Luca Bascetta,et al.  Poli-RRT*: Optimal RRT-based planning for constrained and feedback linearisable vehicle dynamics , 2015, 2015 European Control Conference (ECC).

[40]  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).

[41]  Kenneth Y. Goldberg,et al.  Motion planning for steerable needles in 3D environments with obstacles using rapidly-exploring Random Trees and backchaining , 2008, 2008 IEEE International Conference on Automation Science and Engineering.

[42]  Lotfi A. Zadeh,et al.  Fuzzy Logic , 2009, Encyclopedia of Complexity and Systems Science.

[43]  Woojin Chung,et al.  Kinodynamic Planner Dual-Tree RRT (DT-RRT) for Two-Wheeled Mobile Robots Using the Rapidly Exploring Random Tree , 2015, IEEE Transactions on Industrial Electronics.

[44]  Xhevahir Bajrami,et al.  Application of Fuzzy Logic Controller for obstacle detection and avoidance on real autonomous mobile robot , 2016, 2016 5th Mediterranean Conference on Embedded Computing (MECO).

[45]  David Brandt Comparison of A and RRT-Connect Motion Planning Techniques for Self-Reconfiguration Planning , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[46]  Yimin Zhou,et al.  An Improved Probabilistic Roadmap Algorithm with Potential Field Function for Path Planning of Quadrotor , 2019, 2019 Chinese Control Conference (CCC).