{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:34:46Z","timestamp":1777502086054,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T00:00:00Z","timestamp":1694131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Hunan Province","award":["2021JJ30211"],"award-info":[{"award-number":["2021JJ30211"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Intelligent manufacturing requires robots to adapt to increasingly complex tasks, and dual-arm cooperative operation can provide a more flexible and effective solution. Motion planning serves as a crucial foundation for dual-arm cooperative operation. The rapidly exploring random tree (RRT) algorithm based on random sampling has been widely used in high-dimensional manipulator path planning due to its probability completeness, handling of high-dimensional problems, scalability, and faster exploration speed compared with other planning methods. As a variant of RRT, the RRT*Smart algorithm introduces asymptotic optimality, improved sampling techniques, and better path optimization. However, existing research does not adequately address the cooperative motion planning requirements for dual manipulator arms in terms of sampling methods, path optimization, and dynamic adaptability. It also cannot handle dual-manipulator collaborative motion planning in dynamic scenarios. Therefore, in this paper, a novel motion planner named RRT*Smart-AD is proposed to ensure that the dual-arm robot satisfies obstacle avoidance constraints and dynamic characteristics in dynamic environments. This planner is capable of generating smooth motion trajectories that comply with differential constraints and physical collision constraints for a dual-arm robot. The proposed method includes several key components. First, a dynamic A* cost function sampling method, combined with an intelligent beacon sampling method, is introduced for sampling. A path-pruning strategy is employed to improve the computational efficiency. Strategies for dynamic region path repair and regrowth are also proposed to enhance adaptability in dynamic scenarios. Additionally, practical constraints such as maximum velocity, maximum acceleration, and collision constraints in robotic arm applications are analyzed. Particle swarm optimization (PSO) is utilized to optimize the motion trajectories by optimizing the parameters of quintic non-uniform rational B-splines (NURBSs). Static and dynamic simulation experiments verified that the RRT*Smart-AD algorithm for cooperative dynamic path planning of dual robotic arms outperformed biased RRT* and RRT*Smart. This method not only holds significant practical engineering significance for obstacle avoidance in dual-arm manipulators in intelligent factories but also provides a theoretical reference value for the path planning of other types of robots.<\/jats:p>","DOI":"10.3390\/s23187759","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T10:42:49Z","timestamp":1694428969000},"page":"7759","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Cooperative Dynamic Motion Planning for Dual Manipulator Arms Based on RRT*Smart-AD Algorithm"],"prefix":"10.3390","volume":"23","author":[{"given":"Houyun","family":"Long","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Hunan University of Technology, Zhuzhou 412001, China"}]},{"given":"Guang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hunan University of Technology, Zhuzhou 412001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4629-5989","authenticated-orcid":false,"given":"Fenglin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hunan University of Technology, Zhuzhou 412001, China"}]},{"given":"Tengfei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hunan University of Technology, Zhuzhou 412001, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107980","DOI":"10.1016\/j.ast.2022.107980","article-title":"A learning system for motion planning of free-float dual-arm space manipulator towards non-cooperative object","volume":"131","author":"Wang","year":"2022","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_2","first-page":"435","article-title":"Research on path planning of space robotic arm based on Sarsa (\u03bb) reinforcement learning","volume":"40","author":"Xu","year":"2019","journal-title":"J. Astronaut."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kim, H., Ohmura, Y., and Kuniyoshi, Y. (October, January 27). Transformer-based deep imitation learning for dual-arm robot manipulation. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636301"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nonoyama, K., Liu, Z., Fujiwara, T., Alam, M., and Nishi, T. (2022). Energy-efficient robot configuration and motion planning using genetic algorithm and particle swarm optimization. Energies, 15.","DOI":"10.3390\/en15062074"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"106099","DOI":"10.1016\/j.engappai.2023.106099","article-title":"Trajectory planning for a 6-axis robotic arm with particle swarm optimization algorithm","volume":"122","author":"Ekrem","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Huadong, Z., Chaofan, L., and Nan, J. (2019, January 12\u201314). A path planning method of robot arm obstacle avoidance based on dynamic recursive ant colony algorithm. Proceedings of the 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), Shenyang, China.","DOI":"10.1109\/ICPICS47731.2019.8942495"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, F., Huang, Z., and Xu, L. (2019, January 6\u20138). Path planning of 6-DOF venipuncture robot arm based on improved a-star and collision detection algorithms. Proceedings of the 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), Dali, China.","DOI":"10.1109\/ROBIO49542.2019.8961668"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1177\/0142331214532002","article-title":"Improved APF strategies for dual-arm local motion planning","volume":"37","author":"Byrne","year":"2015","journal-title":"Trans. Inst. Meas. Control."},{"key":"ref_9","unstructured":"Kim, D.H., Lim, S.J., Lee, D.H., Lee, J.Y., and Han, C.S. (2013, January 24\u201326). A RRT-based motion planning of dual-arm robot for (Dis) assembly tasks. Proceedings of the 2013 44th International Symposium on Robotics (ISR), Seoul, Republic of Korea."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wei, K., and Ren, B. (2018). A method of dynamic path planning for robotic manipulator autonomous obstacle avoidance based on an improved RRT algorithm. Sensors, 18.","DOI":"10.3390\/s18020571"},{"key":"ref_11","unstructured":"Li, Q., Li, N., Miao, Z., Sun, T., and He, C. (2021, January 22\u201324). Path Planning of Manipulator Based on Improved Informed-RRT* Algorithm. Proceedings of the Intelligent Equipment, Robots, and Vehicles: 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, Hangzhou, China. Proceedings, Part III 7."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6830","DOI":"10.1109\/LRA.2022.3174257","article-title":"RRT*-based path planning for continuum arms","volume":"7","author":"Meng","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Qi, J., Yuan, Q., Wang, C., Du, X., Du, F., and Ren, A. (2023). Path planning and collision avoidance based on the RRT* FN framework for a robotic manipulator in various scenarios. Complex Intell. Syst., 1\u201320.","DOI":"10.1007\/s40747-023-01131-2"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2227","DOI":"10.1007\/s40747-021-00628-y","article-title":"Path planning of a manipulator based on an improved P_RRT* algorithm","volume":"8","author":"Yi","year":"2022","journal-title":"Complex Intell. Systems"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Naderi, K., Rajam\u00e4ki, J., and H\u00e4m\u00e4l\u00e4inen, P. (2015, January 16\u201318). RT-RRT*: A real-time path planning algorithm based on RRT. Proceedings of the ACM SIGGRAPH Conference on Motion in Games, Paris, France.","DOI":"10.1145\/2822013.2822036"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Adiyatov, O., and Varol, H.A. (2017, January 6\u20139). A novel RRT-based algorithm for motion planning in Dynamic environments. Proceedings of the 2017 IEEE International Conference on Mechatronics and Automation (ICMA), Takamatsu, Japan.","DOI":"10.1109\/ICMA.2017.8016024"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shi, W., Wang, K., Zhao, C., and Tian, M. (2022). Obstacle avoidance path planning for the dual-arm robot based on an improved RRT algorithm. Appl. Sci., 12.","DOI":"10.3390\/app12084087"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, Z., Ma, H., Zhang, X., and Fei, Q. (2019, January 27\u201330). Path planning of the dual-arm robot based on VT-RRT algorithm. Proceedings of the 2019 Chinese Control Conference (CCC), Guangzhou, China.","DOI":"10.23919\/ChiCC.2019.8866388"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9820","DOI":"10.1016\/j.ifacol.2020.12.2685","article-title":"Spline-RRT\u204e: Coordinated motion planning of dual-arm space robot","volume":"53","author":"Yu","year":"2020","journal-title":"IFAC Pap. OnLine"},{"key":"ref_20","unstructured":"Zhang, J., Wang, H., Guo, Y., and Zhao, S. Research on Dual-Arm Robot Assembly Path Planning Based on Improved RRT* Algorithm. Proceedings of the Chinese Intelligent Systems Conference."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shao, J., Gan, Y., and Dai, X. (2023, January 8\u201310). Autonomous Path Planning and Realization for Dual Robot Cooperation Based on ROS Framework. Proceedings of the 2023 International Conference on Advanced Robotics and Mechatronics (ICARM), Sanya, China.","DOI":"10.1109\/ICARM58088.2023.10218950"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, X., You, X., Jiang, J., Ye, J., and Wu, H. (2022). Trajectory planning of dual-robot cooperative assembly. Machines, 10.","DOI":"10.3390\/machines10080689"},{"key":"ref_23","first-page":"47","article-title":"Research on trajectory planning algorithm for six-degree-of-freedom industrial robots","volume":"4","author":"Wang","year":"2017","journal-title":"Precis. Manuf. Autom."},{"key":"ref_24","first-page":"114","article-title":"Time-optimal and pulsation-optimal trajectory planning for slurry spraying manipulator","volume":"44","author":"Cao","year":"2013","journal-title":"J. Cent. South Univ."},{"key":"ref_25","first-page":"8","article-title":"Polynomial interpolation method for motion planning of free-floating space robots","volume":"34","author":"Cui","year":"2019","journal-title":"J. Beijing Inf. Sci. Technol. Univ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"253","DOI":"10.4028\/www.scientific.net\/AMM.376.253","article-title":"Application of optimal algorithm on trajectory planning of mechanical arm based on B-Spline curve","volume":"376","author":"Guo","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"104423","DOI":"10.1016\/j.mechmachtheory.2021.104423","article-title":"A multi-objective approach for the trajectory planning of a 7-DOF serial-parallel hybrid humanoid arm","volume":"165","author":"Wang","year":"2021","journal-title":"Mech. Mach. Theory"},{"key":"ref_28","first-page":"20","article-title":"A comparison of RRT, RRT*, and RRT*-smart path planning algorithms","volume":"16","author":"Noreen","year":"2016","journal-title":"Int. J. Comput. Sci. Netw. Secur. IJCSNS"},{"key":"ref_29","unstructured":"Zeng, C. (2013). Research on Space Robotic Arm Motion and Mission Planning Methods for On-Orbit Services. [Ph.D. Thesis, Dalian University of Technology]."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pan, J., Chitta, S., and Manocha, D. (2012, January 14\u201318). FCL: A general purpose library for collision and proximity queries. Proceedings of the 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA.","DOI":"10.1109\/ICRA.2012.6225337"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Adiyatov, O., and Varol, H.A. Rapidly-exploring random tree based memory efficient motion planning. Proceedings of the 2013 IEEE International Conference on Mechatronics and Automation.","DOI":"10.1109\/ICMA.2013.6617944"},{"key":"ref_32","unstructured":"Bruce, J., and Veloso, M. (October, January 30). Real-time randomized path planning for robot navigation. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4382","DOI":"10.1177\/0954406220969734","article-title":"Optimum time-energy-jerk trajectory planning for serial robotic manipulators by reparameterized quintic NURBS curves","volume":"235","author":"Wu","year":"2021","journal-title":"Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jiang, M., Yang, Z., Li, Y., Sun, Z., and Zi, B. (2021, January 22\u201325). Smooth Trajectory Planning for a Cable-Driven Waist Rehabilitation Robot Using Quintic NURBS. Proceedings of the Intelligent Robotics and Applications: 14th International Conference (ICIRA 2021), Yantai, China. Proceedings, Part I 14.","DOI":"10.1007\/978-3-030-89095-7_53"},{"key":"ref_35","unstructured":"Piegl, L., and Tiller, W. (2012). The NURBS Book, Springer Science & Business Media."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7759\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:47:35Z","timestamp":1760129255000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7759"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,8]]},"references-count":35,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23187759"],"URL":"https:\/\/doi.org\/10.3390\/s23187759","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,8]]}}}