Sampling-Based Path Planning in Heterogeneous Dimensionality-Reduced Spaces*

Many sampling strategies often consider the goal and obstacle population to bias/restrict the search area, and they however become less effective when the robot has many degrees of freedom. This paper explores the nonhomogeneous restriction imposed by the obstacles and presents an improved SBP approach enhanced by heterogeneous dimensionality reduction of the full configuration space. Based on the projection residual, a new Dirichlet process (DP) mixture model is proposed to capture a number of Dimensionality-Reduced Spaces (DRSs), which offer the planning spaces with fewer dimensions than its single-DRS counterpart. Then, the sampling and planning procedures are unified with a proposed transversality condition, connecting sampled nodes across DRSs. At last, a quadratic programming is formulated and quickly solved to map the found path in DRSs to an output path in the full configuration space. Numerical simulations on path planning problems of a high-dimensional Intervention Autonomous Underwater Vehicle (I-AUV) have been conducted, showing the feasibility and efficiency of the proposed method.

[1]  Christopher G. Atkeson,et al.  An optimization approach to rough terrain locomotion , 2010, 2010 IEEE International Conference on Robotics and Automation.

[2]  Wenjie Lu,et al.  A Unified Closed-Loop Motion Planning Approach For An I-AUV In Cluttered Environment With Localization Uncertainty , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[3]  Maxim Likhachev,et al.  Planning with adaptive dimensionality for mobile manipulation , 2012, 2012 IEEE International Conference on Robotics and Automation.

[4]  Jean-Paul Laumond,et al.  Control of Probabilistic Diffusion in Motion Planning , 2008, WAFR.

[5]  Wenjie Lu,et al.  Heterogeneous Dimensionality Reduction for Efficient Motion Planning in High-Dimensional Spaces , 2020, IEEE Access.

[6]  Dikai Liu,et al.  A Scalable Sampling-Based Optimal Path Planning Approach via Search Space Reduction , 2019, IEEE Access.

[7]  Marco Pavone,et al.  Bidirectional Fast Marching Trees : An Optimal Sampling-Based Algorithm for Bidirectional Motion Planning , 2014 .

[8]  Dinesh Manocha,et al.  Fast Motion Planning for High-DOF Robot Systems Using Hierarchical System Identification , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[9]  Russ Tedrake,et al.  Path planning in 1000+ dimensions using a task-space Voronoi bias , 2009, 2009 IEEE International Conference on Robotics and Automation.

[10]  V. Rovenski,et al.  Differential Geometry of Curves and Surfaces , 1952, Nature.

[11]  Marco Pavone,et al.  Learning Sampling Distributions for Robot Motion Planning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[12]  Jonathan P. How,et al.  Camera control for learning nonlinear target dynamics via Bayesian nonparametric Dirichlet-process Gaussian-process (DP-GP) models , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Daniel D. Lee,et al.  Learning and exploiting low-dimensional structure for efficient holonomic motion planning in high-dimensional spaces , 2012, Int. J. Robotics Res..

[14]  Frank Chongwoo Park,et al.  VF-RRT: Introducing optimization into randomized motion planning , 2013, 2013 9th Asian Control Conference (ASCC).

[15]  Marco Pavone,et al.  Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions , 2013, ISRR.

[16]  Wenjie Lu,et al.  An Information Value Function for Nonparametric Gaussian Processes , 2014, ArXiv.

[17]  Marco Pavone,et al.  Robot Motion Planning in Learned Latent Spaces , 2018, IEEE Robotics and Automation Letters.

[18]  Siddhartha S. Srinivasa,et al.  Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Han-Lim Choi,et al.  Approximate Inference-Based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models , 2018, IEEE Robotics and Automation Letters.

[20]  Lei Gao,et al.  PCA-subspace method — Is it good enough for network-wide anomaly detection , 2012, 2012 IEEE Network Operations and Management Symposium.

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

[22]  Dikai Liu,et al.  Sliding Mode Impedance Control for contact intervention of an I-AUV: Simulation and experimental validation , 2020 .