Grasp Planning Based on Metrics for Collaborative Tasks Using Optimization
Source abstract
Abstract
In recent years, technological developments in the field of robotics have expanded their application spectrum to encompass tasks that involve human inclusion in the same workspace. One of the challenges of robotics collaboration is the issue of how a robot and a human can perform daily collaborative tasks, like manipulation of an object. One significant specific problem to solve is where the robot can grasp the object knowing the human grasping points. This research proposes a planning algorithm to find a robot grasping point based on geometric grasp metrics as well as a new heuristic metric focused on the intrinsic inertia in multi-directional object movement. We propose three grasping points: two points emulating each human hand, positioned anywhere on the object and one last point, referencing the robot, which will be optimized as a multi-objective (MO) function problem. The planner was tested using common objects present in human environments (a chair and a table).
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robot Manipulation and Learning · Robotic Path Planning Algorithms · Robotic Mechanisms and Dynamics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey 64849, Mexico
Funding
Tecnologico de Monterrey
References (52)
Show source-provided references
1. Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications10.1016/j.mechatronics.2018.02.009 ↗
2. Symbiotic human–robot collaborative assembly10.1016/j.cirp.2019.05.002 ↗
3. Operations management issues in design and control of hybrid human–robot collaborative manufacturing systems: A survey10.1016/j.arcontrol.2020.04.009 ↗
4. Cobot programming for collaborative industrial tasks: An overview10.1016/j.robot.2019.03.003 ↗
5. Matheson, E., Minto, R., Zampieri, E., Faccio, M., and Rosati, G. (2019). Human–robot collaboration in manufacturing applications: A review. Robotics, 8.10.3390/robotics8040100 ↗
6. The role of roles: Physical cooperation between humans and robots10.1177/0278364912455366 ↗
7. Online hybrid motion planning for dyadic collaborative manipulation via bilevel optimization10.1109/TRO.2020.2992987 ↗
8. Vision-based control architecture for human–robot hand- over applications10.1002/asjc.2480 ↗
9. Calibrating intuitive and natural human– robot interaction and performance for power-assisted heavy object manipulation using cognition-based intelligent admittance control schemes
10. Der Spaa, L., Gienger, M., Bates, T., and Kober, J. (August, January 31). Predicting and optimizing ergonomics in physical human–robot cooperation tasks. Proceedings of the International Conference on Robotics and Automation (ICRA), Paris, France.
11. Mujica, M., Benoussaad, M., and Fourquet, J. (August, January 31). Evaluation of human–robot object co-manipulation under robot impedance control. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Paris, France.10.1109/ICRA40945.2020.9197329 ↗
12. Bio-inspired robotic impedance adaptation for human–robot collaborative tasks10.1007/s11432-019-2748-x ↗
13. Tariq, U., Muthusamy, R., and Kyrki, V. (2018, January 21–25). Grasp planning for load sharing in collaborative manipulation. Proceedings of the International Conference on Robotics and Automation (ICRA), Brisbane, Australia.10.1109/ICRA.2018.8460579 ↗
14. Pose estimation and adaptable grasp configuration with point cloud registration and geometry understanding for fruit grasp planning10.1016/j.compag.2020.105818 ↗
15. New formulation of mixed-integer conic programming for globally optimal grasp planning
16. Modeling grasp type improves learning-based grasp planning10.1109/LRA.2019.2893410 ↗
17. Benchmarking protocol for grasp planning algorithms10.1109/LRA.2019.2956411 ↗
18. Tang, K., Ho, C., Mehlich, J., and Chen, S. (2020). Assessment of handover prediction models in estimation of cycle times for manual assembly tasks in a human–robot collaborative environment. Appl. Sci., 10.10.3390/app10020556 ↗
19. Collaborative manufacturing with physical human–robot interaction10.1016/j.rcim.2015.12.007 ↗
20. Precision grasp planning for multi-Finger hand to grasp unknown objects10.1017/S0263574719000031 ↗
21. Grasp analysis and optimal design of robotic fingertip for two tendon-driven fingers10.1016/j.mechmachtheory.2018.08.028 ↗
22. Xu, X., Li, Y., Du, Y., Cong, M., and Liu, D. (2018, January 19–23). Grasp planning for multi-fingered hand in blind grasping. Proceedings of the IEEE International Conference on CYBER Technology in Automation, Control and Intelligent Systems (IEEE-CYBER), Tianjin, China.10.1109/CYBER.2018.8688364 ↗
23. Grasping force optimization for multi-fingered robotic hands using projection and contraction methods10.1007/s10957-019-01540-9 ↗
24. León, B., Rubert, C., Sancho-Bru, J., and Morales, A. (June, January 31). Characterization of grasp quality measures for evaluating robotic hands prehension. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.
25. Characterisation of grasp quality metrics10.1007/s10846-017-0562-1 ↗
26. Stable grasp planning based on minimum force for dexterous hands10.1007/s11370-019-00305-7 ↗
27. Multi-fingered grasping force optimization based on generalized penalty-function concepts10.1016/j.robot.2020.103672 ↗
28. Robust form-closure grasp planning for 4-pin gripper using learning-based attractive region in environment10.1016/j.neucom.2019.12.039 ↗
29. Robot adaptation to human physical fatigue in human–robot co-manipulation10.1007/s10514-017-9678-1 ↗
30. Anvaripour, A., Khoshnam, K., Menon, C., and Saif, M. (2019, January 20–24). Safe human robot cooperation in task performed on the shared load. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.10.1109/ICRA.2019.8794176 ↗
31. Improving human–robot collaboration through Force/Torque based learning for object manipulation10.1016/j.rcim.2020.102111 ↗
32. Rahman, S.M. (2021). Machine learning-based cognitive position and force controls for power-assisted human–robot collaborative manipulation. Machines, 9.10.3390/machines9020028 ↗
33. Grasp and dexterous manipulation of multi-fingered robotic hands: A review from a control view point10.1080/01691864.2017.1365011 ↗
34. Quality measures for object grasping10.1016/S1697-7912(08)70124-9 ↗
35. Stouraitis, T., Chatzinikolaidis, I., Gienger, M., and Vijayakumar, S. (2018, January 29–31). Dyadic collaborative manipulation through hybrid trajectory optimization. Proceedings of the Conference on Robot Learning (CoRL), Zurich, Switzerland.
36. Eiben, A., and Smith, J. (2015). Introduction to Evolutionary Computing, Springer.10.1007/978-3-662-44874-8 ↗
37. A Multiobjective Particle Swarm Optimization algorithm based on competition mechanism and gaussian Variation10.1155/2020/6352021 ↗
38. Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization10.1016/j.eswa.2015.10.039 ↗
39. A simplified multi-objective particle swarm optimization algorithm10.1007/s11721-019-00170-1 ↗
40. Alvarez-Benitez, J., Everson, R., and Fieldsend, J. (2005, January 9–11). A MOPSO algorithm based exclusively on pareto dominance concepts. Proceedings of the International Conference on Evolutionary Multi-Criterion Optimization (EMO), Guanajuato, Mexico.10.1007/978-3-540-31880-4_32 ↗
41. Performance indicators in multiobjective optimization10.1016/j.ejor.2020.11.016 ↗
42. Handling multiple objectives With particle swarm optimization10.1109/TEVC.2004.826067 ↗
43. Sun, Y., and Gao, Y. (2019). A multi-objective particle swarm optimization algorithm based on gaussian mutation and an improved learning strategy. Mathematics, 7.10.3390/math7020148 ↗
44. Grey wolf optimizer10.1016/j.advengsoft.2013.12.007 ↗
45. Optimización multiobjetivo usando un algoritmo genético y un operador elitista basado en un ordenamiento no-dominado (NSGA-II)
46. A bi-objective load balancing model in a distributed simulation system using NSGA-II and MOPSO approaches10.1016/j.asoc.2017.09.012 ↗
47. Schaffer, J.D. (1985, January 24–26). Multiple objective optimization with vector evaluated genetic algorithm. Proceedings of the 1st International Conference on Genetic Algorithm and Their Applications, Pittsburg, CA, USA.
48. Kursawe, F. (1991). International Conference on Parallel Problem Solving from Nature, Springer.
49. Multiobjective optimization and multiple constraint handling with evolutionary algorithms, Part II: Application example10.1109/3468.650320 ↗
50. Comparison of multi-objective evolutionary algorithms: Empirical results10.1162/106365600568202 ↗
51. An improved grey wolf optimizer based on differential evolution and elimination mechanism10.1038/s41598-019-43546-3 ↗
52. Dasheng, L. (2008). Multiobjective Particle Swarm Optimization: Algorithms and Applications. [Ph.D. Thesis, National University of Singapore].