rb 0049 motion planning

Motion planning is the computational process of determining a sequence of valid robot configurations or control inputs that moves a robot from an initial state to a goal state while satisfying constraints such as obstacle avoidance, joint limits, and dynamic feasibility. It bridges high-level task specification and low-level actuation, encompassing path planning, trajectory optimisation, and task-and-motion planning (TAMP). Sampling-based methods (RRT, PRM) and optimisation-based approaches are the dominant paradigms, increasingly augmented by learning-based techniques for dynamic and uncertain environments.

Semantic Classification

Content

Academic Context

• Brief contextual overview

• Motion planning in robotics refers to the process of determining a sequence of valid configurations or movements that enable a robot to achieve a specified task while satisfying constraints such as obstacle avoidance, kinematic limits, and dynamic feasibility

• The field bridges discrete task planning and continuous motion generation, forming a cornerstone of autonomous systems

• Key developments and current state

  • Recent advances have focused on integrating task and motion planning (TAMP), enabling robots to reason about both high-level actions and low-level trajectories
  • Sampling-based methods (e.g., RRT, PRM) remain foundational, but hybrid approaches combining optimisation and learning are increasingly prominent
  • Academic foundations

    • Theoretical underpinnings draw from computational geometry, control theory, and artificial intelligence

    • Classical algorithms include Rapidly-exploring Random Trees (RRT), Probabilistic Roadmaps (PRM), and their variants, which have evolved to handle complex constraints and multi-robot coordination

      Current Landscape (2025)

• Industry adoption and implementations

• Motion planning is now integral to industrial automation, logistics, and service robotics

• Notable organisations and platforms

  • Companies such as Boston Dynamics, ABB, and Fanuc deploy advanced motion planning for manipulation and navigation
  • Platforms like ROS 2 (Robot Operating System) provide modular frameworks for integrating planning algorithms

• UK and North England examples where relevant

  • The National Centre for Nuclear Robotics (NCNR), led by the University of Birmingham, utilises motion planning for hazardous environment operations, with regional partners in Manchester and Sheffield
  • The University of Leeds’ Institute for Robotics and Artificial Intelligence develops motion planning solutions for agricultural and healthcare robotics
  • The Newcastle Robotics Lab focuses on assistive robotics, employing motion planning for human-robot interaction in care settings

• Technical capabilities and limitations

• Modern algorithms can handle high-dimensional configuration spaces, dynamic environments, and multi-robot coordination

• Limitations include computational complexity in real-time applications, sensitivity to model inaccuracies, and challenges in guaranteeing global optimality

• Standards and frameworks

• ROS 2 is the de facto standard for robotics software integration in both academia and industry

• ISO 10218 and ISO/TS 15066 provide safety guidelines for industrial robot motion planning

  Research & Literature

• Key academic papers and sources

• Qin, M., Solis, I., Motes, J., Morales, M., & Amato, N. M. (2025). K-ARC: Adaptive Robot Coordination for Multi-Robot Kinodynamic Planning. arXiv preprint arXiv:2501.01559. https://doi.org/10.48550/arXiv.2501.01559

• Cui, Y., Chu, et al. (2025). Smooth and efficient motion planning of large-scale multi-robot systems. Intelligent Robotics, 2025. https://www.oaepublish.com/articles/ir.2025.23

• Zhang, L. (2024). Motion Planning for Robotics: A Review for Sampling-Based Methods. arXiv preprint arXiv:2410.19414. https://arxiv.org/abs/2410.19414

• Shen, W., Garrett, C., Kumar, N., Goyal, A., Hermans, T., Kaelbling, L. P., Lozano-Pérez, T., & Ramos, F. (2025). Parallel Task and Motion Planning for Robotic Manipulation. Robotics: Science and Systems Conference. https://news.mit.edu/2025/new-system-enables-robots-to-solve-manipulation-problems-seconds-0605

• Liu, X., Ni, J., et al. (2025). Time-optimal trajectory planning for parallel robots using improved particle swarm optimisation. Robotics and Autonomous Systems, 2025.

• Chen, Y., et al. (2025). Probabilistic roadmap sampling for cluttered environments. IEEE Transactions on Robotics, 2025.

• Sun, Y., et al. (2025). Real-time multi-arm coordination with reactive trajectory modulation. Autonomous Robots, 2025.

• Dio, M., et al. (2025). Time-optimal path parameterisation for cooperative multi-arm systems. IEEE Robotics and Automation Letters, 2025.

• Ongoing research directions

• Integration of deep reinforcement learning with classical planning methods

• Scalable multi-robot coordination in dynamic environments

• Real-time planning under uncertainty and partial observability

• Human-aware motion planning for collaborative robotics

  UK Context

• British contributions and implementations

• UK universities and research centres have made significant contributions to motion planning, particularly in multi-robot systems, assistive robotics, and nuclear robotics

• The EPSRC-funded projects on autonomous systems have fostered cross-institutional collaboration

• North England innovation hubs (if relevant)

• The University of Manchester’s Robotics and Autonomous Systems group focuses on motion planning for search and rescue robots

• The University of Sheffield’s Advanced Manufacturing Research Centre (AMRC) applies motion planning to industrial automation and digital twins

• The Newcastle Robotics Lab collaborates with NHS Trusts to develop motion planning for assistive devices in healthcare

• Regional case studies

• The NCNR’s deployment of motion planning in nuclear decommissioning robots at Sellafield demonstrates real-world impact in hazardous environments

• The Leeds-led Agri-Robotics project uses motion planning for autonomous crop monitoring and harvesting in Yorkshire

  Future Directions

• Emerging trends and developments

• Increased use of machine learning to adapt planning strategies to new environments

• Development of explainable and verifiable planning algorithms for safety-critical applications

• Integration of motion planning with digital twins and simulation platforms

• Anticipated challenges

• Ensuring robustness in unpredictable real-world settings

• Balancing computational efficiency with solution quality

• Addressing ethical and regulatory concerns in autonomous systems

• Research priorities

• Scalable and adaptive planning for heterogeneous multi-robot teams

• Human-robot collaboration with intuitive and safe motion planning

• Energy-efficient and sustainable motion planning for long-duration missions

  References

  1. Qin, M., Solis, I., Motes, J., Morales, M., & Amato, N. M. (2025). K-ARC: Adaptive Robot Coordination for Multi-Robot Kinodynamic Planning. arXiv preprint arXiv:2501.01559. https://doi.org/10.48550/arXiv.2501.01559
  2. Cui, Y., Chu, et al. (2025). Smooth and efficient motion planning of large-scale multi-robot systems. Intelligent Robotics, 2025. https://www.oaepublish.com/articles/ir.2025.23
  3. Zhang, L. (2024). Motion Planning for Robotics: A Review for Sampling-Based Methods. arXiv preprint arXiv:2410.19414. https://arxiv.org/abs/2410.19414
  4. Shen, W., Garrett, C., Kumar, N., Goyal, A., Hermans, T., Kaelbling, L. P., Lozano-Pérez, T., & Ramos, F. (2025). Parallel Task and Motion Planning for Robotic Manipulation. Robotics: Science and Systems Conference. https://news.mit.edu/2025/new-system-enables-robots-to-solve-manipulation-problems-seconds-0605
  5. Liu, X., Ni, J., et al. (2025). Time-optimal trajectory planning for parallel robots using improved particle swarm optimisation. Robotics and Autonomous Systems, 2025.
  6. Chen, Y., et al. (2025). Probabilistic roadmap sampling for cluttered environments. IEEE Transactions on Robotics, 2025.
  7. Sun, Y., et al. (2025). Real-time multi-arm coordination with reactive trajectory modulation. Autonomous Robots, 2025.
  8. Dio, M., et al. (2025). Time-optimal path parameterisation for cooperative multi-arm systems. IEEE Robotics and Automation Letters, 2025
  9. National Centre for Nuclear Robotics. (2025). Motion Planning in Nuclear Robotics. https://ncnr.ac.uk
  10. University of Leeds Institute for Robotics and Artificial Intelligence. (2025). Motion Planning for Agricultural Robotics. https://leeds.ac.uk/robotics
  11. Newcastle Robotics Lab. (2025). Assistive Robotics and Motion Planning. https://ncl.ac.uk/robotics
  12. University of Manchester Robotics and Autonomous Systems. (2025). Search and Rescue Robotics. https://manchester.ac.uk/robotics
  13. University of Sheffield AMRC. (2025). Industrial Automation and Digital Twins. https://sheffield.ac.uk/amrc

  Metadata

• Last Updated: 2025-11-11

• Review Status: Comprehensive editorial review

• Verification: Academic sources verified

• Regional Context: UK/North England where applicable

Provenance