Informed Asymptotically Near-Optimal Planning for Field Robots with Dynamics

Recent progress in sampling-based planning has provided performance guarantees in terms of optimizing trajectory cost even in the presence of significant dynamics. The STABLE_SPARSE_RRT (SST) algorithm has these desirable path quality properties and achieves computational efficiency by maintaining a sparse set of state-space samples. The current paper focuses on field robotics, where workspace information can be used to effectively guide the search process of a planner. In particular, the computational performance of SST is improved by utilizing appropriate heuristics. The workspace information guides the exploration process of the planner and focuses it on the useful subset of the state space. The resulting Informed- SST is evaluated in scenarios involving either ground vehicles or quadrotors. This includes testing for a physically-simulated vehicle over uneven terrain, which is a computationally expensive planning problem.

[1]  David S. Wettergreen,et al.  FINDING ROUTES FOR EFFICIENT AND SUCCESSFULL SLOPE ASCENT FOR EXPLORATION ROVERS , 2022 .

[2]  Scott Kuindersma,et al.  Optimization and stabilization of trajectories for constrained dynamical systems , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[3]  Kostas E. Bekris,et al.  Informed and probabilistically complete search for motion planning under differential constraints , 2008, AAAI 2008.

[4]  D. Ferguson,et al.  Motion planning in urban environments: Part II , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[6]  ZhangYu,et al.  Robust Autonomous Flight in Constrained and Visually Degraded Shipboard Environments , 2017 .

[7]  M. A. Jaradat,et al.  Integrated simulation platform for indoor quadrotor applications , 2013, 2013 9th International Symposium on Mechatronics and its Applications (ISMA).

[8]  Geoffrey A. Hollinger,et al.  Sampling-based robotic information gathering algorithms , 2014, Int. J. Robotics Res..

[9]  James J. Kuffner,et al.  Randomized statistical path planning , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Inna Sharf,et al.  Sampling-based A* algorithm for robot path-planning , 2014, Int. J. Robotics Res..

[11]  Yu Zhang,et al.  Robust Autonomous Flight in Constrained and Visually Degraded Shipboard Environments , 2017, J. Field Robotics.

[12]  Tim D. Barfoot,et al.  Monocular Visual Teach and Repeat Aided by Local Ground Planarity , 2015, FSR.

[13]  Julius Ziegler,et al.  Spatiotemporal state lattices for fast trajectory planning in dynamic on-road driving scenarios , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Kostas E. Bekris,et al.  The Importance of a Suitable Distance Function in Belief-Space Planning , 2015, ISRR.

[15]  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.

[16]  Maxim Likhachev,et al.  Dynamic Multi-Heuristic A* , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[17]  Oliver Brock,et al.  Planning Long Dynamically-Feasible Maneuvers for Autonomous Vehicles , 2009 .

[18]  Siddhartha S. Srinivasa,et al.  CHOMP: Covariant Hamiltonian optimization for motion planning , 2013, Int. J. Robotics Res..

[19]  Anthony Stentz,et al.  R* Search , 2008, AAAI.

[20]  Kostas E. Bekris,et al.  Asymptotically optimal sampling-based kinodynamic planning , 2014, Int. J. Robotics Res..

[21]  Alonzo Kelly,et al.  Kinodynamic motion planning with state lattice motion primitives , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  Kostas E. Bekris,et al.  Sparse Methods for Efficient Asymptotically Optimal Kinodynamic Planning , 2014, WAFR.

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

[24]  Michiel van de Panne,et al.  RRT-blossom: RRT with a local flood-fill behavior , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

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

[26]  Mike Stilman,et al.  Kinodynamic RRTs with Fixed Time Step and Best-Input Extension Are Not Probabilistically Complete , 2014, WAFR.

[27]  Maxim Likhachev,et al.  Search-based planning for manipulation with motion primitives , 2010, 2010 IEEE International Conference on Robotics and Automation.

[28]  Erion Plaku,et al.  Adaptive Sampling-Based Motion Planning for Mobile Robots with Differential Constraints , 2015, TAROS.

[29]  Ross A. Knepper,et al.  Differentially constrained mobile robot motion planning in state lattices , 2009 .

[30]  Siddhartha S. Srinivasa,et al.  Regionally accelerated batch informed trees (RABIT*): A framework to integrate local information into optimal path planning , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

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

[32]  Geoffrey A. Hollinger,et al.  Risk‐aware Path Planning for Autonomous Underwater Vehicles using Predictive Ocean Models , 2013, J. Field Robotics.

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