Modal Planning for Cooperative Non-Prehensile Manipulation by Mobile Robots
Source abstract
Abstract
If we define a mode as a set of specific configurations that hold the same constraint, and if we investigate their transitions beforehand, we can efficiently probe the configuration space by using a manipulation planner. However, when multiple mobile robots together manipulate an object by using the non-prehensile method, the candidates for the modes and their transitions become enormous because of the numerous contacts among the object, the environment, and the robots. In some cases, the constraints on the object, which include a combination of robot contacts and environmental contacts, are incapable of guaranteeing the object’s stability. Furthermore, some transitions cannot appear because of geometrical and functional restrictions of the robots. Therefore, in this paper, we propose a method to narrow down the possible modes and transitions between modes by excluding the impossible modes and transitions from the viewpoint of statics, kinematics, and geometry. We first generated modes that described an object’s contact set from the robots and the environment while ignoring their exact configurations. Each multi-contact set exerted by the robots and the environment satisfied the condition necessary for the force closure on the object along with gravity. Second, we listed every possible transition between the modes by determining whether or not the given robot could actively change the contacts with geometrical feasibility. Finally, we performed two simulations to validate our method on specific manipulation tasks. Our method can be used in various cases of non-prehensile manipulations by using mobile robots. The mode transition graph generated by our method was used to efficiently sequence the manipulation actions before deciding the detailed configuration planning.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotic Path Planning Algorithms · Robot Manipulation and Learning · Modular Robots and Swarm Intelligence
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Precision Engineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Research into Artifacts, Center for Engineering, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8568, Japan
References (27)
Show source-provided references
1. Stilwell, D.J., and Bay, J.S. (1993, January 2–6). Toward the development of a material transport system using swarms of ant-like robots. Proceedings of the IEEE International Conference on Robotics and Automation, Atlanta, GA, USA.
2. Mason, M.T. (2001). Mechanics of Robotic Manipulation, MIT Press.10.7551/mitpress/4527.001.0001 ↗
3. Nonprehensile Dynamic Manipulation: A Survey10.1109/LRA.2018.2801939 ↗
4. Arai, T., and Ota, J. (1996, January 8). Let us work together-Task planning of multiple mobile robots. Proceedings of the 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems’ 96, Osaka, Japan.
5. Latombe, J.C. (1991). Robot Motion Planning, Springer US.10.1007/978-1-4615-4022-9 ↗
6. LaValle, S.M. (1998). Rapidly-Exploring Random Trees: A New Tool for Path Planning, Computer Science Department, Iowa State University. Technical Report TR98-11.
7. Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces10.1109/70.508439 ↗
8. Multi-modal motion planning in non-expansive spaces10.1177/0278364909352098 ↗
9. Hsu, D., Kavraki, L.E., Latombe, J.C., Motwani, R., and Sorkin, S. (1998). On finding narrow passages with probabilistic roadmap planners. Proceedings of the Workshop on the Algorithmic Foundations of Robotics, Robotics: The Algorithmic Perspective, A. K. Peters, Ltd.
10. Planning of graspless manipulation by a multifingered robot hand10.1163/156855305323383776 ↗
11. Miyazawa, K., Maeda, Y., and Arai, T. (2005, January 19–21). Planning of graspless manipulation based on rapidly-exploring random trees. Proceedings of the 6th IEEE International Symposium on Assembly and Task Planning: From Nano to Macro Assembly and Manufacturing, Montreal, QC, Canada.
12. Barry, J., Kaelbling, L.P., and Lozano-Pérez, T. (2013, January 6–10). A hierarchical approach to manipulation with diverse actions. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.10.1109/ICRA.2013.6630814 ↗
13. Lee, G., Lozano-Pérez, T., and Kaelbling, L.P. (October, January 28). Hierarchical planning for multi-contact non-prehensile manipulation. Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.
14. Schmitt, P.S., Neubauer, W., Feiten, W., Wurm, K.M., Wichert, G.V., and Burgard, W. (June, January 29). Optimal, sampling-based manipulation planning. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.10.1109/ICRA.2017.7989390 ↗
15. Xiao, J. (1997, January 7–9). Goal-contact relaxation graphs for contact-based fine motion planning. Proceedings of the 1997 IEEE International Symposium on Assembly and Task Planning, Marina del Rey, CA, USA.
16. Automatic generation of high-level contact state space10.1177/02783640122067552 ↗
17. Fan, C., Shirafuji, S., and Ota, J. (2018). Least Action Sequence Determination in the Planning of Non-Prehensile Manipulation with Multiple Mobile Robots. the 15th International Conference on Intelligent Autonomous Systems (IAS–15), Springer.10.1007/978-3-030-01370-7_14 ↗
18. Xiao, J., and Zhang, L. (1993, January 2–6). A General Strategy to Determine Geometrically Valid Contact Formations from Possible Contact Primitives. Proceedings of the IEEE International Conference on Robotics And Automation, Atlanta, GA, USA.
19. Maeda, Y., Aiyama, Y., Arai, T., and Ozawa, T. (1996, January 8). Analysis of object-stability and internal force in robotic contact tasks. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Osaka, Japan.
20. Dexterous assembly manipulation of a compact array of objects10.1016/S0007-8506(07)62775-9 ↗
21. LaValle, S.M. (2006). Planning Algorithms, Cambridge University Press.10.1017/CBO9780511546877 ↗
22. Planning motions compliant to complex contact states10.1177/02783640122067480 ↗
23. On computing three-finger force-closure grasps of polygonal objects10.1109/70.478433 ↗
24. Murray, R.M., Li, Z., and Sastry, S.S. (1994). A Mathematical Introduction to Robotic Manipulation, CRC Press.
25. Shirafuji, S., Terada, Y., and Ota, J. (2016). Mechanism Allowing a Mobile Robot to Apply a Large Force to the Environment. The 14th International Conference on Intelligent Autonomous Systems (IAS–14), Springer.10.1007/978-3-319-48036-7_58 ↗
26. Mechanism allowing large-force application by a mobile robot, and development of ARODA10.1016/j.robot.2018.09.005 ↗
27. Ito, T., Shirafuji, S., and Ota, J. (2018, January 12–15). Development of a Mobile Robot Capable of Tilting Heavy Objects and its Safe Placement with Respect to Target Objects. Proceedings of the 2018 IEEE International Conference on Roboics and Biomimetics (ROBIO2018), Kuala Lumpur, Malaysia.10.1109/ROBIO.2018.8665241 ↗