Kinodynamic Motion Planning

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Overview

Kinodynamic motion planning searches for motions that are not only collision-free but also physically executable under a robot’s dynamics and control limits. Common approaches include sampling-based tree planners such as RRT variants, heuristic search and motion lattices, trajectory optimization and model predictive control, and learned models or policies that guide planning. Methods are tested on systems ranging from ground robots and manipulators to drones and multi-robot teams, including cluttered scenes, moving obstacles, and uncertain dynamics. A central challenge is balancing fast, robust planning in high-dimensional settings with guarantees such as safety, completeness, and near-optimality.

Latest papers

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46 papers5 papers in the last four weeks, down 37% on the four weeks before. 0.0% of all new papers.

  1. CMC-Opt: Constraint Manifold with Corners for Inequality-Constrained Optimization

    May 20, 2026Yetong Zhang, Frank DellaertKinodynamic Motion PlanningRiemannian Optimization

    College of Computing Georgia Institute of Technology Atlanta, USA

  2. KIO-planner: Attention-Guided Single-Stage Motion Planning with Dual Mapping for UAV Navigation

    May 19, 2026Dexing Yao, Haochen Li, Junhao Wei +11Constrained Motion PlanningKinodynamic Motion Planning

    Faculty of Applied Sciences Macao Polytechnic University Macao, China · College of Animal Science and Technology Zhongkai University of Agriculture and Engineering Guangzhou, China · School of Economics and Management South China Normal University Macao, China +1

  3. Efficient Multi-Robot Motion Planning with Precomputed Translation-Invariant Edge Bundles

    May 10, 2026Himanshu Gupta, Paul Motter, Aritra Chakrabarty +5Multi-Robot Motion PlanningKinodynamic Motion Planning

    Smead Aerospace Engineering Sciences, University of Colorado Boulder · Department of Computer Science, University of Colorado Boulder · Cherry Creek High School, Greenwood Village, CO, USA

  4. Terminal Matters: Kinodynamic Planning with a Terminal Cost and Learned Uncertainty in Belief State-Cost Space

    May 9, 2026Zhuoyun Zhong, Seyedali Golestaneh, Constantinos ChamzasBelief-Space PlanningTrajectory Optimization

    All authors are affiliated with the Department of Robotics Engineering, Worcester Polytechnic Institute (WPI), Worcester, MA 01609, USA

  5. Optimized and kinematically feasible multi-agent motion planning

    May 3, 2026Anja Hellander, Kristoffer Bergman, Daniel AxehillConstrained Motion PlanningTrajectory Optimization

    Division of Automatic Control, Linköping University, Sweden · Saab AB, Linköping, Sweden

  6. From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution

    Apr 14, 2026Lidor Erez, Shahaf S. Shperberg, Ayal TaitlerRobot Task PlanningConstrained Motion Planning

    Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Be’er Sheva, Israel · Stein Faculty of Computer and Information Science, Ben-Gurion University of the Negev, Be’er Sheva, Israel

  7. Optimal Kinodynamic Motion Planning Through Anytime Bidirectional Heuristic Search with Tight Termination Condition

    Apr 13, 2026Yi Wang, Bingxian Mu, Shahab Shokouhi +1Motion PlanningKinodynamic Motion Planning

    Department of Mechanical Engineering, University of New Hampshire, Durham, NH, 03824, USA. · Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada

  8. Primitive-based Truncated Diffusion for Efficient Trajectory Generation of Differential Drive Mobile Manipulators

    Apr 5, 2026Long Xu, Choilam Wong, Yuhang Zhong +3Diffusion-Based PlanningDiffusion Models for Motion Generation

  9. Sampling-based Task and Kinodynamic Motion Planning under Semantic Uncertainty

    Apr 1, 2026Qi Heng Ho, Zachary N. Sunberg, Morteza LahijanianSemantic Uncertainty EstimationRobot Task Planning

    Q. H. Ho is with the Kevin T. Crofton Department of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, VA, USA · Z. N. Sunberg and M. Lahijanian are with the Smead Department of Aerospace Engineering Sciences, University of Colorado Boulder, CO, USA

  10. Planning over MAPF Agent Dependencies via Multi-Dependency PIBT

    Mar 24, 2026Zixiang Jiang, Yulun Zhang, Rishi Veerapaneni +1Kinodynamic Motion PlanningMotion Planning

    University Of Melbourne · Robotics Institute, Carnegie Mellon University

  11. Ultrafast Sampling-based Kinodynamic Planning via Differential Flatness

    Mar 17, 2026Thai Duong, Clayton W. Ramsey, Zachary Kingston +2Constrained Motion PlanningKinodynamic Motion Planning

    Department of Computer Science, Rice University, Houston, TX, 77005, USA

  12. Multi-Robot Trajectory Planning via Constrained Bayesian Optimization and Local Cost Map Learning with STL-Based Conflict Resolution

    Mar 6, 2026Sourav Raxit, Abdullah Al Redwan Newaz, Jose Fuentes +3STL Motion PlanningMulti-Robot Motion Planning

    Department of Computer Science, University of New Orleans, New Orleans, LA 70148, USA · Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA · Providence College, Department of Mathematics & Computer Science, Providence, RI 02918, USA

  13. cuRoboV2: Dynamics-Aware Motion Generation with Depth-Fused Distance Fields for High-DoF Robots

    Mar 5, 2026Balakumar Sundaralingam, Adithyavairavan Murali, Stan BirchfieldRobot Motion GenerationKinodynamic Motion Planning

  14. Kino-PAX+^+: Near-Optimal Massively Parallel Kinodynamic Sampling-based Motion Planner

    Feb 2, 2026Nicolas Perrault, Qi Heng Ho, Morteza LahijanianConstrained Motion PlanningKinodynamic Motion Planning

    Department of Aerospace Engineering Sciences, University of Colorado Boulder, CO, USA · Department of Aerospace and Ocean Engineering, Virginia Tech, VA, USA

  15. From Kinematic Motion Planners to Dynamic Autonomous Navigation with Obstacle Avoidance (Extended version)

    Mar 22, 2025Mayur Sawant, Abdelhamid TayebiRobotic ControlKinodynamic Motion Planning

    Department of Electrical and Computer Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, Canada

  16. D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations

    Mar 15, 2025Yuhao Zhang, Keisuke Okumura, Heedo Woo +2Multi-Robot Motion PlanningConstrained Motion Planning

    University of Cambridge · UK · National Institute of Advanced Industrial Science and Technology (AIST) +1