Simultaneous area partitioning and allocation for complete coverage by multiple autonomous industrial robots
- 1.1k Downloads
For tasks that require complete coverage of surfaces by multiple autonomous industrial robots, it is important that the robots collaborate to appropriately partition and allocate the surface areas amongst themselves such that the robot team’s objectives are optimized. An approach to this problem is presented, which takes into account unstructured and complex 3D environments, and robots with different capabilities. The proposed area partitioning and allocation approach utilizes Voronoi partitioning to partition objects’ surfaces, and multi-objective optimization to allocate the partitioned areas to the robots whilst optimizing robot team’s objectives. In addition to minimizing the overall completion time and achieving complete coverage, which are objectives particularly useful for applications such as surface cleaning, manipulability measure and joint’s torque are also optimized so as to help autonomous industrial robots to operate better in applications such as spray painting and grit-blasting. The approach is validated using six case studies that consist of comparative studies, complex simulated scenarios as well as real scenarios using data obtained from real objects and applications.
KeywordsMulti-robot collaboration Multiple autonomous industrial robots Area partitioning and allocation Complete coverage
This research is supported by SABRE Autonomous Solutions Pty Ltd and the Centre for Autonomous Systems (CAS) at the University of Technology Sydney (UTS), Australia. Authors thank Prof. Gamini Dissanayake, Assoc. Prof. Shoudong Huang, Dr. Gavin Paul, Dr. Andrew To, Mr. Gregory Peters, and Mr. Teng Zhang for their valuable suggestions and discussions.
- Batsaikhan, D., Janchiv, A., & Lee, S.-G. (2013). Sensor-based incremental boustrophedon decomposition for coverage path planning of a mobile robot. In S. Lee, H. Cho, K.-J. Yoon, & J. Lee (Eds.), Intelligent autonomous systems, advances in intelligent systems and computing (Vol. 193, pp. 621–628). Berlin: Springer.Google Scholar
- Danner, T., & Kavraki, L. E. (2000). Randomized planning for short inspection paths. In IEEE International Conference on Robotics and Automation, vol 2, (pp. 971–976).Google Scholar
- Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. Merelo, & H.-P. Schwefel (Eds.), Parallel problem solving from nature PPSN VI (vol. 1917, pp. 849–858)., Lecture Notes in Computer Science, Berlin: Springer.Google Scholar
- Englot, B., & Hover, F. S. (2012). Sampling-based coverage path planning for inspection of complex structures. In Proceedings of the 22nd International Conference on Automated Planning and Scheduling (ICAPS). Atibaia, Sao Paulo Brazil. http://hdl.handle.net/1721.1/87729.
- Guanghui, L., Yamashita, A., Asama, H., & Tamura, Y. (2012). An efficient improved artificial potential field based regression search method for robot path planning. In 2012 International Conference on Mechatronics and Automation (ICMA), (pp. 1227–1232).Google Scholar
- Hassan, M., Liu, D., Huang, S., & Dissanayake, G. (2014). Task oriented area partitioning and allocation for optimal operation of multiple industrial robots in unstructured environments. In 13th International Conference on Control, Automation, Robotics and Vision (ICARCV), (pp. 1184–1189).Google Scholar
- Hassan, M., Liu, D., Paul, G., & Huang, S. (2015). An approach to base placement for effective collaboration of multiple autonomous industrial robots. In IEEE International Conference on Robotics and Automation (ICRA), (pp. 3286–3291).Google Scholar
- Hassan, M., Liu, D., & Paul, G. (2016 - in press) Modeling and stochastic optimization of complete coverage under uncertainties in multi-robot base placements. In IEEE International Conference on Intelligent Robots and Systems (IROS).Google Scholar
- Latombe, J.-C. (2012). Robot motion planning (Vol. 124). London: Springer.Google Scholar
- Niku, S. B. (2011). Introduction to robotics: Analysis, control, applications (2nd ed.). Hoboken, NJ: Wiley.Google Scholar
- Patel, S., & Sobh, T. (2015). Manipulator performance measures—a comprehensive literature survey. Journal of Intelligent and Robotic Systems, 77(3–4), 547–570.Google Scholar
- Ranjbar-Sahraei, B., Weiss, G., & Nakisaee, A. (2012). A multi-robot coverage approach based on stigmergic communication. In I. Timm & C. Guttmann (Eds.), Multiagent system technologies (Vol. 7598, pp. 126–138)., Lecture Notes in Computer Science Berlin: Springer.Google Scholar
- Ren, Z., Yuan, J., & Liu, W. (2013). Minimum near-convex shape decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(10), 2546–2552.Google Scholar
- Xu, J., Liu, D., & Fang, G. (2007). An efficient method for collision detection and distance queries in a robotic bridge maintenance system. In T.-J. Tarn, S.-B. Chen, & C. Zhou (Eds.), Robotic welding, intelligence and automation (vol. 362, pp. 71–82)., Lecture notes in control and information sciences. Berlin: Springer.Google Scholar