Abstract
Human-Robot Interaction is an increasingly important topic in both research and industry fields. Since human safety must be always guaranteed and accidental contact with the operator avoided, it is necessary to investigate real-time obstacle avoidance strategies. The transfer from simulation environments, where algorithms are tested, to the real world is challenging from different points of view, e.g., the continuous tracking of the obstacle and the configuration of different manipulators. In this paper, the authors describe the implementation of a collision avoidance strategy based on the potential field method for off-line trajectory planning and on-line motion control, paired with the Motion Capture system Optitrack PrimeX 22 for obstacle tracking. Several experiments show the performance of the proposed strategy in the case of a fixed and dynamic obstacle, disturbing the robot’s trajectory from multiple directions. Two different avoidance modalities are adapted and tested for both standard and redundant robot manipulators. The results show the possibility of safely implementing the proposed avoidance strategy on real systems.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
Availability of data and materials
No publicly archived dataset has been used or generated during the research.
Code availability
Not applicable.
References
ISO 10218-2:2011-07 Robots and robotic devices – Safety requirements for industrial robots – Part 2: Robot systems and integration, Geneva, Switzerland (2011)
ISO/TS 15066:2016-02 Robots and robotic devices – Collaborative robots, Geneva, Switzerland (2016)
Palmieri, G., Scoccia, C.: Motion planning and control of redundant manipulators for dynamical obstacle avoidance. Machines 9(6), 121 (2021)
Chiriatti, Giorgia, Palmieri, Giacomo, Scoccia, Cecilia, Palpacelli, Matteo Claudio, Callegari, Massimo: Adaptive obstacle avoidance for a class of collaborative robots. Machines 9(6), 113 (2021)
Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: Autonomous robot vehicles, pp. 396–404. Springer, ??? (1986)
Lozano-Perez, Tomas: A simple motion-planning algorithm for general robot manipulators. IEEE J. Robot. Autom. 3(3), 224–238 (1987)
Scalera, L., Giusti, A., Vidoni, R., Gasparetto, A.: Enhancing fluency and productivity in human-robot collaboration through online scaling of dynamic safety zones. Int. J. Adv. Manuf. Tech. 121(9), 6783–6798 (2022)
Scalera, L., Vidoni, R., Giusti, A.: Optimal scaling of dynamic safety zones for collaborative robotics. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 3822–3828 (2021). IEEE
Lingelbach, F.: Path planning using probabilistic cell decomposition. In: IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA’04. 2004, vol. 1, pp. 467–472 (2004). IEEE
Gonzalez, R., Kloetzer, M., Mahulea, C.: Comparative study of trajectories resulted from cell decomposition path planning approaches. In: 2017 21st International Conference on System Theory, Control and Computing (ICSTCC), pp. 49–54 (2017). IEEE
Wang, J., Liu, S., Zhang, B., Yu, C.: Manipulation planning with soft constraints by randomized exploration of the composite configuration space. Int. J. Control Autom. Syst. 19(3), 1340–1351 (2021)
Xu, P., Wang, N., Dai, S.L., Zuo, L.: Motion planning for mobile robot with modified bit* and mpc. Appl. Sci. 11(1), 426 (2021)
Shitsukane, A.S.: Fuzzy Logic Model for Obstacles Avoidance Mobile Robot in Static Unknown Environment. PhD thesis, JKUAT-COETEC (2022)
Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Mach. Stud. 7(1), 1–13 (1975). https://doi.org/10.1016/S0020-7373(75)80002-2
Wenzel, P., Schön, T., Leal-Taixé, L., Cremers, D.: Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 14360–14366 (2021) https://doi.org/10.1109/ICRA48506.2021.9560787
Zindler, F., Lucchi, M., Wohlhart, L., Pichler, H., Hofbaur, M.: Towards Dynamic Obstacle Avoidance for Robot Manipulators with Deep Reinforcement Learning. In: Müller, A., Brandstötter, M. (eds.) Advances in Service and Industrial Robotics, pp. 89–96. Springer, Cham (2022)
Gharbi, A.: A dynamic reward-enhanced Q-learning approach for efficient path planning and obstacle avoidance in mobile robotics. Appl. Comput. Inform. (2024)
Liu, A., Fu, J., Zhan, S., Jin, Z., Zhang, W.: A Policy Searched-Based Optimization Algorithm for Obstacle Avoidance in Robot Manipulators. IEEE Trans. Ind. Electron. 1–10 (2024) https://doi.org/10.1109/TIE.2023.3344831
Farag, K.K.A., Shehata, H.H., El-Batsh, H.M.: Mobile robot obstacle avoidance based on neural network with a standardization technique. J. Robot. 2021, (2021)
Zohaib, M., Pasha, M., Riaz, R.A., Javaid, N., Ilahi, M., Khan, R.: Control Strategies for Mobile Robot With Obstacle Avoidance 3, 1027–1036 (2013)
Sani, M., Robu, B., Hably, A.: Dynamic Obstacles Avoidance Using Nonlinear Model Predictive Control. In: IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society, 1–6 (2021) https://doi.org/10.1109/IECON48115.2021.9589658
Adamu, P.I., Okagbue, H.I., Oguntunde, P.E.: Fast and optimal path planning algorithm (FAOPPA) for a mobile robot. Wirel. Pers. Commun. 106, 577–592 (2019)
Kumar, S., Dadas, S.S., Parhi, D.R.: Path planning of mobile robot using modified DAYKUN-BIP virtual target displacement method in static environments. Wirel. Pers. Commun. 128(3), 2287–2305 (2023)
Gasparetto, A., Zanotto, V.: A new method for smooth trajectory planning of robot manipulators. Mech. Mach. Theory 42(4), 455–471 (2007)
Gasparetto, A., Boscariol, P., Lanzutti, A., Vidoni, R.: Path planning and trajectory planning algorithms: A general overview. Motion and operation planning of robotic systems 3–27 (2015)
Du, Y., Zhang, X., Nie, Z.: A Real-Time Collision Avoidance Strategy in Dynamic Airspace Based on Dynamic Artificial Potential Field Algorithm. IEEE Access 7, 169469–169479 (2019). https://doi.org/10.1109/ACCESS.2019.2953946
Puriyanto, R.D., Wahyunggoro, O., Cahyadi, A.I.: Implementation of Improved Artificial Potential Field Path Planning Algorithm in Differential Drive Mobile Robot. In: 2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 18–23 (2022) https://doi.org/10.1109/ICITEE56407.2022.9954079
Feng, S., Qian, Y., Wang, Y.: Collision avoidance method of autonomous vehicle based on improved artificial potential field algorithm. Proc. Inst. Mech. Eng. Pt. D J. Automobile Eng. 235(14), 3416–3430 (2021)
Xu, X., Hu, Y., Zhai, J., Li, L., Guo, P.: A novel non-collision trajectory planning algorithm based on velocity potential field for robotic manipulator. Int. J. Adv. Robot. Syst. 15(4), 1729881418787075 (2018)
Halme, R.J., Lanz, M., Kämäräinen, J., Pieters, R., Latokartano, J., Hietanen, A.: Review of vision-based safety systems for human-robot collaboration. Procedia CIRP 72, 111–116 (2018)
Schmidt, B., Wang, L.: Depth camera based collision avoidance via active robot control. J. Manuf. Syst. 33(4), 711–718 (2014)
Fabrizio, F., De Luca, A.: Real-time computation of distance to dynamic obstacles with multiple depth sensors. IEEE Robot. Autom. Lett. 2(1), 56–63 (2016)
Flacco, F., Kröger, T., De Luca, A., Khatib, O.: A depth space approach to human-robot collision avoidance. In: 2012 IEEE international conference on robotics and automation, pp. 338–345. (2012). IEEE
Rybski, P., Anderson-Sprecher, P., Huber, D., Niessl, C., Simmons, R.: Sensor fusion for human safety in industrial workcells. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3612–3619 (2012) https://doi.org/10.1109/IROS.2012.6386034
Morato, C., Kaipa, K.N., Zhao, B., Gupta, S.K.: Toward safe human robot collaboration by using multiple kinects based real-time human tracking. J. Comput. Inf. Sci. Eng. 14(1) (2014)
Wang, L., Schmidt, B., Nee, A.Y.: Vision-guided active collision avoidance for human-robot collaborations. Manuf. Lett. 1(1), 5–8 (2013)
Jiang, D., Li, G., Sun, Y., Hu, J., Yun, J., Liu, Y.: Manipulator grabbing position detection with information fusion of color image and depth image using deep learning. J. Ambient Intell. Humaniz. Comput. 12, 10809–10822 (2021)
Thangaraj, M., Monikavasagom, S.: A competent frame work for efficient object detection, tracking and classification. Wirel. Pers. Commun. 107, 939–957 (2019)
Li, X.: Robot target localization and interactive multi-mode motion trajectory tracking based on adaptive iterative learning. J. Ambient Intell. Humaniz. Comput. 11, 6271–6282 (2020)
Kim, J., Jung, H., Kang, M., Chung, K.: 3D human-gesture interface for fighting games using motion recognition sensor. Wirel. Pers. Commun. 89, 927–940 (2016)
Amorim, A., Guimares, D., Mendona, T., Neto, P., Costa, P., Moreira, A.P.: Robust human position estimation in cooperative robotic cells. Robot. Comput.-Integr. Manuf. 67, 102035 (2021)
Heredia, J., Cabrera, M.A., Tirado, J., Panov, V., Tsetserukou, D.: Cobotgear: Interaction with collaborative robots using wearable optical motion capturing systems. In: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), pp. 1584–1589 (2020). IEEE
Scoccia, C., Palmieri, G., Palpacelli, M.C., Callegari, M.: A collision avoidance strategy for redundant manipulators in dynamically variable environments: on-line perturbations of off-line generated trajectories. Machines 9(2), 30 (2021)
Ahmadi, K., Asadi, D., Merheb, A., Nabavi-Chashmi, S.Y., Tutsoy, O.: Active fault-tolerant control of quadrotor UAVs with nonlinear observer-based sliding mode control validated through hardware in the loop experiments. Control Eng. Pract. 137, 105557 (2023). https://doi.org/10.1016/j.conengprac.2023.105557
Moe, S., Antonelli, G., Teel, A.R., Pettersen, K.Y., Schrimpf, J.: Set-based tasks within the singularity-robust multiple task-priority inverse kinematics framework: General formulation, stability analysis, and experimental results. Front. Robot. AI. 3, 16 (2016)
Chiaverini, S., Siciliano, B., Egeland, O.: Review of the damped least-squares inverse kinematics with experiments on an industrial robot manipulator. IEEE Trans. Control Syst. Technol. 2(2), 123–134 (1994)
Melchiorre, M., Scimmi, L.S., Pastorelli, S.P., Mauro, S.: Collison avoidance using point cloud data fusion from multiple depth sensors: a practical approach. In: 2019 23rd International Conference on Mechatronics Technology (ICMT), pp. 1–6 (2019). IEEE
Lee, K.K., Buss, M.: Obstacle avoidance for redundant robots using Jacobian transpose method. In: 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3509–3514 (2007). IEEE
Zlajpah, L., Nemec, B.: Kinematic control algorithms for on-line obstacle avoidance for redundant manipulators. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol.2, pp. 1898–1903 (2002) https://doi.org/10.1109/IRDS.2002.1044033
Swarup, A., Gopal, M.: Control strategies for robot manipulators-a review. IETE J. Res. 35(4), 198–207 (1989)
Funding
Open access funding provided by Universitá Politecnica delle Marche within the CRUI-CARE Agreement. This research has received funding from the “Karntner Wirtschaftsf orderung Fonds” (KWF) and the “European Regional Development Fund” (EFRE) within the PATTERN-Skin project 26616/34294/49769.
Author information
Authors and Affiliations
Contributions
Conceptualization, C.S and B.U.; methodology, C.S., B.U. and G.P.; software, C.S and B.U.; validation, C.S. and B.U.; formal analysis, C.S.; investigation, G.P; resources, M.H.; data curation, C.S.; writing-original draft preparation, C.S. and B.U.; writing-review and editing, G.P.; visualization, M.R.; supervision, G.P. and H.R.; project administration, M.H. All authors have read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
The authors declare their consent for publication.
Conflict of interest/Competing interests
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Scoccia, C., Ubezio, B., Palmieri, G. et al. Experimental Assessment of a Vision-Based Obstacle Avoidance Strategy for Robot Manipulators: Off-line Trajectory Planning and On-line Motion Control. J Intell Robot Syst 110, 107 (2024). https://doi.org/10.1007/s10846-024-02146-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10846-024-02146-8