Improved RRT* For Fast Path Planning in Underwater 3D Environment

Path planning for Autonomous Underwater Vehicle(AUV) needs to find a feasible path in three-dimension workspace and is a very difficult and challenging task. Traditional algorithms have failed to find the path effectively in high-dimensional space since it is proven that the complexity of the problem is NP-hard. Consequently, sample-based algorithms, such as Rapidly-exploring Random Tree star (RRT*), have been proposed to find the probabilistically complete and asymptotically optimal solution, which reduces the complexity of the algorithm. However, in underwater environment with undulating terrain and scattered floating obstacles, the global uniform random sampling strategy in RRT* costs too much memory and time, resulting in a slow convergence to optimal path. To deal with these problem, an improved RRT* is proposed in this paper. Goal-biased Gaussian distribution sampling with variable standard deviation is proposed to controls the randomness of search nodes. Furthermore, a focused optimal search strategy is introduced to improve the convergence rate. Finally, some simulations are conducted under various 3D underwater environments. The results show that the improved RRT* outperforms RRT* in search efficiency and convergence rate under water.

[1]  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).

[2]  M. Farooq,et al.  Note on the generation of random points uniformly distributed in hyper-ellipsoids , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[3]  Anthony Stentz,et al.  Optimal and efficient path planning for partially-known environments , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[4]  Wheeler Ruml,et al.  An effort bias for sampling-based motion planning , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[5]  Yasar Ayaz,et al.  Potential functions based sampling heuristic for optimal path planning , 2015, Autonomous Robots.

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

[7]  Jean-Claude Latombe,et al.  On the Probabilistic Foundations of Probabilistic Roadmap Planning , 2006, Int. J. Robotics Res..

[8]  Amna Khan,et al.  Optimal path planning in cluttered environment using RRT*-AB , 2018, Intell. Serv. Robotics.

[9]  Hiroshi Noborio,et al.  On the heuristics of A* or A algorithm in ITS and robot path-planning , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[10]  John F. Canny,et al.  New lower bound techniques for robot motion planning problems , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).

[11]  Yasar Ayaz,et al.  Potentially Guided Bidirectionalized RRT* for Fast Optimal Path Planning in Cluttered Environments , 2018, Robotics Auton. Syst..

[12]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1986 .

[13]  Emilio Frazzoli,et al.  Asymptotically-optimal path planning for manipulation using incremental sampling-based algorithms , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Sajad Saeedi,et al.  AUV Navigation and Localization: A Review , 2014, IEEE Journal of Oceanic Engineering.

[15]  Rüdiger Dillmann,et al.  RRT∗-Connect: Faster, asymptotically optimal motion planning , 2015, 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO).