Abstract
To save time and money while designing new products, industry needs tools to design, test and validate the product using virtual prototypes. These virtual prototypes must enable to test the product at all Product Life-cycle Management (PLM) stages. Many operations in PLM involve human manipulation of product components in cluttered environment (product assembly, disassembly or maintenance). Virtual Reality (VR) enables real operators to perform these tests with virtual prototypes. This work introduces a novel path planning architecture allowing collaboration between a VR user and an automatic path planning system. It is based on an original environment model including semantic, topological and geometric information, and an automatic path planning process split in two phases: coarse (semantic and topological information) and fine (semantic and geometric information) planning. The collaboration between VR user and automatic path planner is made of 3 main aspects. First, the VR user is guided along a pre-computed path through a haptic device whereas he VR user can go away from the proposed path to explore possible better ways. Second the authority of automatic planning system is balanced to let the user free to explore alternatives (geometric layer). Third the intents of VR user are predicted (on topological layer) to be integrated in the re-planning process. Experiments are provided to illustrate the multi-layer representation of the environment, the path planning process, the control sharing and the intent prediction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fillatreau, P., Fourquet, J.Y., Le Bolloch, R., Cailhol, S., Datas, A., Puel, B.: Using virtual reality and 3d industrial numerical models for immersive interactive checklists. Comput. Ind. (2013)
Ahmadi-Pajouh, M.A., Towhidkhah, F., Gharibzadeh, S., Mashhadimalek, M.: Path planning in the hippocampo-prefrontal cortex pathway: an adaptive model based receding horizon planner. Med. Hypotheses 68, 1411–1415 (2007)
Lozano-Perez, T.: Spatial planning: a configuration space approach. Trans. Comput. 100, 108–120 (1980)
LaValle, S.M.: Planning algorithms. Cambridge University Press, Cambridge (2006)
Marayong, P., Li, M., Okamura, A.M., Hager, G.D.: Spatial motion constraints: theory and demonstrations for robot guidance using virtual fixtures. In: International Conference on Robotics and Automation, vol. 2, pp. 1954–1959. IEEE, New York (2003)
Abbink, D.A., Mulder, M.: Neuromuscular analysis as a guideline in designing shared control. Adv. Haptics 109, 499–516 (2010)
Weber, C., Nitsch, V., Unterhinninghofen, U., Farber, B., Buss, M.: Position and force augmentation in a telepresence system and their effects on perceived realism. In: EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp. 226–231. World Haptics, IEEE (2009)
You, E., Hauser, K.: Assisted teleoperation strategies for aggressively controlling a robot arm with 2d input. Robot. Sci. Syst. VII, 354 (2012)
Flemisch, F.O., Heesen, M., Hesse, T., Kelsch, J., Schieben, A., Beller, J.: Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations. Cogn. Technol. Work 14, 3–18 (2012)
Aarno, D., Ekvall, S., Kragic, D.: Adaptive virtual fixtures for machine-assisted teleoperation tasks. In: International Conference on Robotics and Automation, pp. 1139–1144. IEEE (2005)
Fagg, A.H., Rosenstein, M., Platt, R., Grupen, R.A.: Extracting user intent in mixed initiative teleoperator control. In: American Institute of Aeronautics and Astronautics Intelligent Systems Technical Conference (2004)
Li, M., Okamura, A.M.: Recognition of operator motions for real-time assistance using virtual fixtures. In: Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. HAPTICS, pp. 125–131. IEEE (2003)
Yu, W., Alqasemi, R., Dubey, R., Pernalete, N.: Telemanipulation assistance based on motion intention recognition. In: International Conference on Robotics and Automation, pp. 1121–1126. IEEE (2005)
Loizou, S.G., Kumar, V.: Mixed initiative control of autonomous vehicles. In: International Conference on Robotics and Automation, pp. 1431–1436. IEEE (2007)
Anderson, S.J., Peters, S.C., Iagnemma, K., Overholt, J.: Semi-autonomous stability control and hazard avoidance for manned and unmanned ground vehicles. Technical report, DTIC Document (2010)
Dragan, A.D., Srinivasa, S.S.: A policy blending formalism for shared control. Int. J. Robot. Res. (2013)
Ladevèze, N., Fourquet, J.Y., Puel, B.: Interactive path planning for haptic assistance in assembly tasks. Comput. Graph. 34, 17–25 (2010)
Taïx, M., Flavigné, D., Ferré, E.: Human interaction with motion planning algorithm. J. Intel. Robot. Syst. 67, 285–306 (2012)
Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische mathematik 1, 269–271 (1959)
CGAL: CGAL, Computational Geometry Algorithms Library (2014). http://www.cgal.org
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Cailhol, S., Fillatreau, P., Zhao, Y., Fourquet, JY. (2016). Hierarchic Interactive Path Planning in Virtual Reality. In: Filipe, J., Gusikhin, O., Madani, K., Sasiadek, J. (eds) Informatics in Control, Automation and Robotics. Lecture Notes in Electrical Engineering, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-319-26453-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-26453-0_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26451-6
Online ISBN: 978-3-319-26453-0
eBook Packages: EngineeringEngineering (R0)