Quicker Path planning of a collaborative dual-arm robot using Modified BP-RRT* algorithm


  • Josin Hippolitus A Department of Mechatronics Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India
  • R. Senthilnathan Department of Mechatronics Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India




computational path planning, robot control, Artificial Neural Network (ANN), Back Propagation


Path-planning of an industrial robot is an important task to reduce the overall operation time. In industrial tasks, path planning is executed with lead-through programming, where in most cases the robot faces singulated object configurations. Cluttered environments demand path-planning algorithms, which are sensor driven, rather than pre-programmed. Path-planning algorithms, like RRT, and RRT* and their variants have inherent problems like the duration of a search and the creation of several node samples which may lead to longer path lengths. Back Propagation-Rapidly exploring Random Tree* (BP-RRT*) algorithm was a leap forward when an obstacle is enveloped with a sphere. Due to the spherical envelope of the obstacle, this method evaluates the connection between the path and obstacle in space with a spherical envelope using the triangular function and identifies the non-collision path in 3D space. This predicts the best non-collision path in the 3D workspace. The current state-of-the-art of BP-RRT* is limited to single-arm robots. A collaborative dual-arm robot faces more problems in path planning than a single-arm robot like inter-collision of manipulator arms apart from avoiding obstacles. A Modified BP-RRT* algorithm is proposed for the dual-arm collaborative robot has a pre-stage partition of grids that makes the computation faster, efficient, and collision-free compared to the traditional path planning algorithms namely RRT, RRT*, Improved RRT* and BP-RRT*. The algorithm is implemented in simulation as well as in physical implementation for ABB YuMi dual-arm collaborative robot and the typical length of the path of the proposed modified BP-RRT* method has reduced by 53.8% from the traditional RRT method, 6.95% from the RRT* method, 7.77% from improved RRT* method and 6.83% from the BP-RRT* method. Also, the average time to grasp has reduced by 17.84%, the typical duration for search has decreased by 33.45%, the number of node samples created has reduced by 14.79% from BP-RRT* algorithm.


A'Campo-Neuen, Annette (2022). Lambert's Work on Geographic Map Projections, In: Mathematical Geography in the Eighteenth Century: Euler, Lagrange and Lambert, Springer, 183-202, 2022.


Barber, C.B. and Dobkin, D.P. and Huhdanpaa, H (1993). The Quickhull Algorithm for Convex Hulls, ACM Transactions on Mathematical Software, 22(4), 1993.


Brezovnik, S., Gotlih, J., Balič, J., Gotlih, K., and Brezočnik, M. (2014). Optimization of an Automated Storage and Retrieval Systems by Swarm Intelligence. 25th Daaam International Symposium on Intelligent Manufacturing and Automation, 100, 1309-1318, 2014.


Chen, Pengzhan, and Weiqing Lu. (2021). Deep Reinforcement Learning Based Moving Object Grasping, Information Sciences 565, 62-76, 2021.


Elbanhawi, Mohamed, and Milan Simic (2014). Sampling-Based Robot Motion Planning: A Review, IEEE Access 2, 56-77, 2014.


Erke, S., Bin, D., Yiming, N., Qi, Z., Liang, X., and Dawei, Z. (2020). An Improved A-Star Based Path Planning Algorithm for Autonomous Land Vehicles, International Journal of Advanced Robotic Systems 17(5), 1729881420962263, 2020.


Foumani, Mehdi, Asghar Moeini, Michael Haythorpe, and Kate Smith-Miles (2018). A Cross- Entropy Method for Optimising Robotic Automated Storage and Retrieval Systems, International Journal of Production Research 56(19), 6450-6472, 2018.


Gao, Q. and Yuan, Q. and Sun, Y. and Xu, L. (2023). Path Planning Algorithm of Robot Arm Based on Improved RRT* and BP Neural Network Algorithm, Journal of King Saud University- Computer and Information Sciences 35(8), 101650, 2023.


Ladra, S. and Paramá, J.R. and Silva-Coira, F. (2016). Compact and Queryable Representation of Raster Datasets, Proceedings of the 28th International Conference on Scientific and Statistical Database Management (SSDBM '16), ACM 1-12, 2016.


He, Zhibo, Chenguang Liu, Xiumin Chu, Rudy R. Negenborn, and Qing Wu (2022). Dynamic Anti-Collision A-Star Algorithm for Multi-Ship Encounter Situations, Applied Ocean Research 118, 102995, 2022.


Husain, Zainab, Amna Al Zaabi, Hanno Hildmann, Fabrice Saffre, Dymitr Ruta, and A. F. Isakovic. (2022). Search and Rescue in a Maze-Like Environment with Ant and Dijkstra Algorithms, Drones 6(10), 273, 2022.


Karaman, S. and Frazzoli, E. (2010). Optimal Kinodynamic Motion Planning Using Incremental Sampling-Based Methods, 49th IEEE Conference on Decision and Control (CDC) 7681-7687, 2010.


Kavraki, L.E. and Kolountzakis, M.N. and Latombe, J.-C. (1998). Analysis of Probabilistic Roadmaps for Path Planning, IEEE Transactions on Robotics and Automation 14(1), 166-171, 1998.


LaValle, Steven (1998). Rapidly-Exploring Random Trees: A New Tool for Path Planning, Research Report 9811, 1998.

Li, Jing, Ji-hang Cheng, Jing-yuan Shi, and Fei Huang. (2012). Brief Introduction of Backpropagation

(BP) Neural Network Algorithm and its Improvement, Advances in Computer Science and Information Engineering, Springer 553-558, 2021.

Vitter, Jeffrey Scott. (1984). Faster Methods for Random Sampling, Communications of the ACM 27(7),703-718,1984.


Liu, Chenguang, Qingzhou Mao, Xiumin Chu, and Shuo Xie (2019). An Improved A-Star Algorithm Considering Water Current, Traffic Separation and Berthing for Vessel Path Planning, Applied Sciences 9(6),1057,2019.


Patel, Utsav, Nithish K. Sanjeev Kumar, Adarsh Jagan Sathyamoorthy, and Dinesh Manocha.

(2021). DWA-RL: Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation Among Mobile Obstacles, 2021 IEEE International Conference on Robotics and Automation (ICRA) 6057-6063, 2021.

Qi, J. and Yang, H. and Sun, H. (2020). MOD-RRT*: A Sampling-Based Algorithm for Robot Path Planning in a Dynamic Environment, IEEE Transactions on Industrial Electronics 68(8), 7244-7251, 2020.


Salem, Israa Ezzat, Maad M. Mijwil, Alaa Wagih Abdulqader, and Marwa M. Ismaeel (2022). Flight-Schedule Using Dijkstra's Algorithm with Comparison of Routes Findings, International Journal of Electrical and Computer Engineering 12(2),1675, 2022.


Swinbank, Richard, and R. James Purser. (2006). Fibonacci Grids: A Novel Approach to Global Modelling, Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, applied meteorology and physical oceanography , 132(619), 1769-1793, 2006.


Wang, Jiankun, Wenzheng Chi, Chenming Li, Chaoqun Wang, and Max Q-H. Meng (2020). Neural RRT*: Learning-Based Optimal Path Planning, EEE Transactions on Automation Science and Engineering 17(4), 1748-1758, 2020.


Yi, Junhui, Qingni Yuan, Ruitong Sun, and Huan Bai (2020). Path Planning of a Manipulator Based on an Improved P_RRT* Algorithm, Complex & intelligent systems 8(3), 2227-2245, 2022.


Zafar, Mohd Nayab, and J. C. Mohanta. (2018). Methodology for Path Planning and Optimization of Mobile Robots: A Review, Procedia computer science 133, 141-152, 2018.


Zhou, Yulan, and Nannan Huang. (2022). Airport AGV Path Optimization Model Based on Ant Colony Algorithm to Optimize Dijkstra Algorithm in Urban Systems, Sustainable Computing: Informatics and Systems 35, 100716, 2022.


Hippolitus, A.J. and Senthilnathan, R. and Malla, O. (2021). Simulation of Grasp Localization Inferences for a Dual-Arm Collaborative Robot, IOP Conference Series: Materials Science and Engineering 1012,1, 012004, 2021.


Newbury, R., Gu, M., Chumbley, L., Mousavian, A., Eppner, C., Leitner, J., Bohg, J., Morales, A., Asfour, T., Kragic, D. and Fox, D. (2023). Deep Learning Approaches to Grasp Synthesis: A Review, IEEE Transactions on Robotics 2023.


Wan, W. and Harada, K. (2016). Developing and Comparing Single-Arm and Dual-Arm Regrasp, IEEE Robotics and Automation Letters 1(1), 243-250, 2016.


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