4/18/2023 0 Comments Dobri gochev xscopeIn: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. Jaillet, L., Cortés, J., Siméon, T.: Transition-based RRT for Path Planning in Continuous Cost Spaces. In: Fourth Annual Symposium on Combinatorial Search (2011) Gochev, K., Cohen, B., Butzke, J., Safonova, A., Likhachev, M.: Path Planning with Adaptive Dimensionality. In: IEEE International Conference on Robotics and Automation, pp. E.: Resolution Independent Density Estimation for Motion Planning in High-Dimensional Spaces. In: 2015 IEEE International Conference On Robotics and Automation (ICRA), pp. D.: Batch Informed Trees (Bit*): Sampling-Based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs. D.: Informed rrt*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic. M.: Conditional density growth (cdg) model: a simplified model of rrt coverage for kinematic systems. 834–841 (2002)Ĭhamzas, C., Shrivastava, A., Kavraki, L.E.: Using Local Experiences for Global Motion Planning. Italian Association for Artificial Intelligence, pp. MIT Press, Cambridge (1988)Ĭarpin, S., Pagello, E.: On Parallel RRTs for Multi-Robot Systems. 1874–1879 (2006)Ĭanny, J.: The complexity of robot motion planning. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 1187–1192 (2008)īertram, D., Kuffner, J., Dillmann, R., Asfour, T.: An Integrated Approach to Inverse Kinematics and Path Planning for Redundant Manipulators. 3433–3440 (2005)īerenson, D., Kuffner, J., Choset, H.: An Optimization Approach to Planning for Mobile Manipulation. M.: Iterative Relaxation of Constraints: a Framework for Improving Automated Motion Planning. We also explore important implementation issues in the sampling process and discuss the limitations of this method.īayazit, O. Extensive experiments comparing the enhanced and traditional version of RRT, RRT-Connect, and Bidirectional T-RRT on both a planar hyper-redundant manipulator and the Baxter humanoid robot show significant acceleration, up to two orders of magnitude, on computing a solution. The proposed enhancement to popular sampling-based planning algorithms is aimed at circumventing the exponential dependence on dimensionality, by progressively exploring lower dimensional volumes of the configuration space. Despite the performance advantages of modern sampling-based motion planners, solving high dimensional planning problems in near real-time remains a considerable challenge. This paper introduces an enhancement to traditional sampling-based planners, resulting in efficiency increases for high-dimensional holonomic systems such as hyper-redundant manipulators, snake-like robots, and humanoids.
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