Hostname: page-component-8448b6f56d-sxzjt Total loading time: 0 Render date: 2024-04-23T22:48:45.161Z Has data issue: false hasContentIssue false
  • English
  • Français

An inchworm-inspired motion strategy for robotic swarms

Published online by Cambridge University Press:  23 April 2021

Kasra Eshaghi Open the ORCID record for Kasra Eshaghi [Opens in a new window] ,
Zendai Kashino Open the ORCID record for Zendai Kashino [Opens in a new window] ,
Hyun Joong Yoon ,
Goldie Nejat  and
Beno Benhabib
Kasra Eshaghi*
Affiliation:
Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ONM5S 3G8, Canada
Zendai Kashino
Affiliation:
Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ONM5S 3G8, Canada
Hyun Joong Yoon
Affiliation:
School of Mechanical and Automotive Engineering, Daegu Catholic University, Hayang, Gyeongsan, Gyeongbuk 712-702, Republic of Korea
Goldie Nejat
Affiliation:
Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ONM5S 3G8, Canada
Beno Benhabib
Affiliation:
Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ONM5S 3G8, Canada
*
*Corresponding author. Email: eshaghi@mie.utoronto.ca
Get access Rights & Permissions [Opens in a new window]

Abstract

Effective motion planning and localization are necessary tasks for swarm robotic systems to maintain a desired formation while maneuvering. Herein, we present an inchworm-inspired strategy that addresses both these tasks concurrently using anchor robots. The proposed strategy is novel as, by dynamically and optimally selecting the anchor robots, it allows the swarm to maximize its localization performance while also considering secondary objectives, such as the swarm’s speed. A complementary novel method for swarm localization, that fuses inter-robot proximity measurements and motion commands, is also presented. Numerous simulated and physical experiments are included to illustrate our contributions.

Keywords

Swarm roboticsMotion planningSwarm localizationMillirobotsMulti-robot systems
Type
Article
Information
Robotica , Volume 39 , Issue 12 , December 2021 , pp. 2283 - 2305
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Şahin, E., “Swarm Robotics: From Sources of Inspiration to Domains of Application,” In: Swarm Robotics (2005) pp. 10–20.Google Scholar
Brambilla, M., Ferrante, E., Birattari, M. and Dorigo, M., “Swarm robotics: A review from the swarm engineering perspective,” Swarm Intel. 7(1), 1–41 (2013).CrossRefGoogle Scholar
Nedjah, N. and Junior, L. S., “Review of methodologies and tasks in swarm robotics towards standardization,” Swarm Evol. Comp. 50(1), 100565 (2019).CrossRefGoogle Scholar
Schranz, M., Umlauft, M., Sende, M. and Elmenreich, W., “Swarm robotic behaviors and current applications,” Front. Robot. AI. 7(1), 36–56 (2020).10.3389/frobt.2020.00036CrossRefGoogle ScholarPubMed
Kira, Z. and Potter, M. A., “Exerting Human Control Over Decentralized Robot Swarms,” 4th International Conference on Autonomous Robots and Agents (2009) pp. 566–571.Google Scholar
Walker, P., Amraii, S. A., Lewis, M., Chakraborty, N. and Sycara, K., “Human Control of Leader-Based Swarms,” IEEE International Conference on Systems, Man, and Cybernetics (2013) pp. 2712–2717.Google Scholar
Shahrokhi, S., Lin, L., Ertel, C., Wan, M. and Becker, A. T., “Steering a swarm of particles using global inputs and swarm statistics,” IEEE Trans. Robot. 34(1), 207–219 (2018).10.1109/TRO.2017.2769094CrossRefGoogle Scholar
Kim, J. Y., Kashino, Z., Colaco, T., Nejat, G. and Benhabib, B., “Design and implementation of a millirobot for swarm studies – mROBerTO,” Robotica 36(11), 1591–1612 (2018).CrossRefGoogle Scholar
Kim, J. Y., Colaco, T., Kashino, Z., Nejat, G. and Benhabib, B., “mROBerTO: A Modular Millirobot for Swarm-Behavior Studies,” IEEE/RSJ International Conference on Intelligent Robots and Systems (2016) pp. 2109–2114.Google Scholar
Eshaghi, K., Li, Y., Kashino, Z., Nejat, G. and Benhabib, B., “mROBerTO 2.0 – An autonomous millirobot with enhanced locomotion for swarm robotics,” IEEE Robotic. Automat. Lett. 5(2), 962–969 (2020).10.1109/LRA.2020.2966411CrossRefGoogle Scholar
Cardona, G. A. and Calderon, J. M., “RPlease provide page range for Ref. [11].obot swarm navigation and victim detection using rendezvous consensus in search and rescue operations,” Appl. Sci. 9(8), 1702–1725 (2019).CrossRefGoogle Scholar
Macwan, A., Nejat, G. and Benhabib, B., “Optimal Deployment of Robotic Teams for Autonomous Wilderness Search and Rescue,” IEEE/RSJ International Conference on Intelligent Robots and Systems (2011) pp. 4544–4549.Google Scholar
Macwan, A., Nejat, G. and Benhabib, B., “Target-motion prediction for robotic search and rescue in wilderness environments,” IEEE Trans. Syst. Man. Cyber. Part B 41(5), 1287–1298 (2011).CrossRefGoogle ScholarPubMed
Vardy, A., “Orbital Construction: Swarms of Simple Robots Building Enclosures,” IEEE International Workshops on Foundations and Applications of Self* Systems (2018) pp. 147–153.Google Scholar
Heinrich, M. K., Soorati, M. D., Kaiser, T. K., Wahby, M. and Hamann, H., “Swarm robotics: Robustness, scalability, and self-X features in industrial applications,” Inf. Technol. 61(4), 159–167 (2019).Google Scholar
Eschke, C., Heinrich, M. K., Wahby, M. and Haman, H., “Self-Organized Adaptive Paths in Multi-Robot Manufacturing: Reconfigurable and Pattern-Independent Fibre Deployment,” IEEE/RSJ International Conference on Intelligent Robots and Systems (2019) pp. 4086–4091.Google Scholar
Neumann, P. P., Hirschberger, P., Baurzhan, Z., Tiebe, C., Hofmann, M., Hüllmann, D. and Bartholmai, M., “Indoor Air Quality Monitoring Using Flying Nanobots: Design and Experimental Study,” IEEE International Symposium on Olfaction and Electronic Nose (2019) pp. 1–3.Google Scholar
Innocente, M. S. and Grasso, P., “Self-organising swarms of firefighting drones: Harnessing the power of collective intelligence in decentralised multi-robot systems,” J. Comp. Sci. 34(1), 80–101 (2019).10.1016/j.jocs.2019.04.009CrossRefGoogle Scholar
Ren, W. and Beard, R., “A decentralized scheme for spacecraft formation flying via the virtual structure approach,” J. Guide. Control Dyn. 27(1), 1746–1751 (2004).Google Scholar
Vatamaniuk, I., Panina, G., Saveliev, A. and Ronzhin, A., “Convex Shape Generation by Robotic Swarm,” International Conference on Autonomous Robot Systems and Competitions (2016) pp. 300–304.Google Scholar
Vicmudo, M. P., Dadios, E. P. and Vicerra, R. R. P., “Path Planning of Underwater Swarm Robots Using Genetic Algorithm,” International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (2014) pp. 1–5.Google Scholar
Wei, H.-X., Mao, Q., Guan, Y. and Li, Y.-D., “A centroidal Voronoi tessellation based intelligent control algorithm for the self-assembly path planning of swarm robots,” Exp. Syst. App. 85(1), 261–269 (2017).CrossRefGoogle Scholar
Lee, Y.-T., Zeng, S.-F. and Chiu, C.-S., “Distributed Path Planning of Swarm Mobile Robots,” 12th Asian Control Conference (2019) pp. 49–54.Google Scholar
Hönig, W., Preiss, J. A., Kumar, T. K. S., Sukhatme, G. S. and Ayanian, N., “Trajectory planning for quadrotor swarms,” IEEE Trans. Robot. 34(4), 856–869 (2018).10.1109/TRO.2018.2853613CrossRefGoogle Scholar
Izzo, D. and Pettazzi, “Equilibrium Shaping: Distributed Motion Planning for Satellite Swarm,” Proceedings of the 8th International Symposium on Artificial Intelligence, Robotics and Automation in Space (2005) pp. 727–736.Google Scholar
Gazi, V., “Swarm aggregations using artificial potentials and sliding-mode control,” IEEE Trans. Robot. 21(6), 1208–1214 (2005).CrossRefGoogle Scholar
Lawton, J. R. T., Beard, R. W. and Young, B. J., “A decentralized approach to formation maneuvers,” IEEE Trans. Robot. Autom. 19(6), 933–941 (2003).10.1109/TRA.2003.819598CrossRefGoogle Scholar
Xu, D., Zhang, X., Zhu, Z., Chen, C. and Yang, P., “Behavior-based formation control of swarm robots,” Mathem. Prob. Eng. 2014(1), 1–13 (2014).Google Scholar
Bentes, C. and Saotome, O., “Dynamic Swarm Formation with Potential Fields and A* Path Planning in 3D Environment,” Brazilian Robotics Symposium and Latin American Robotics Symposium (2012) pp. 74–78.Google Scholar
Soleymani, T. and Saghafi, F., “Behavior-Based Acceleration Commanded Formation Flight Control,” International Conference on Control, Automation and Systems (2010) pp. 1340–1345.Google Scholar
Consolini, L., Morbidi, F., Prattichizzo, D. and Tosques, M., “A Geometric Characterization of Leader-Follower Formation Control,” Proceedings IEEE International Conference on Robotics and Automation (2007) pp. 2397–2402.Google Scholar
Wang, H., Guo, D., Liang, X., Chen, W., Hu, G. and Leang, K. K., “Adaptive vision-based leader–follower formation control of mobile robots,” Trans. Indust. Electron. 64(4), 2893–2902 (2017).10.1109/TIE.2016.2631514CrossRefGoogle Scholar
Lin, C.-C., Chen, K.-C. and Chuang, W.-J., “Motion planning using a memetic evolution algorithm for swarm robots,” Int. J. Adv. Rob. Syst. 9(1), 19 (2012).10.5772/45669CrossRefGoogle Scholar
Do, K. D. and Pan, J., “Nonlinear formation control of unicycle-type mobile robots,” Rob. Auton. Syst. 55(3), 191–204 (2007).10.1016/j.robot.2006.09.001CrossRefGoogle Scholar
Ahmadi, S. M., Kebriaei, H. and Moradi, H., “Constrained coverage path planning: Evolutionary and classical approaches,” Robotica 36(6), 904–924 (2018).10.1017/S0263574718000139CrossRefGoogle Scholar
Chen, Y., Ren, S., Chen, Z., Chen, M. and Wu, H., “Path planning for vehicle-borne system consisting of multi air–ground robots,” Robotica 38(3), 493–511 (2020).10.1017/S0263574719000808CrossRefGoogle Scholar
Ashraf, A., Majd, A. and Troubitsyna, E., “Online path generation and navigation for swarms of UAVs,” Sci. Program. 2020(1), 1–14 (2020).Google Scholar
Cornejo, A. and Nagpal, R., “Distributed Range-Based Relative Localization of Robot Swarms,” Selected Contributions of the Eleventh International Workshop on the Algorithmic Foundations of Robotics (2015) p. 107.Google Scholar
Zhang, S., Staudinger, E., Sand, S., Raulefs, R. and Dammann, A., “Anchor-Free Localization Using Round-Trip Delay Measurements for Martian Swarm Exploration,” IEEE/ION Position, Location and Navigation Symposium (2014) pp. 1130–1139.Google Scholar
Berger, M., Seversky, L. M. and Brown, D. S., “Classifying Swarm Behavior via Compressive Subspace Learning,” IEEE International Conference on Robotics and Automation (2016) pp. 5328–5335.Google Scholar
Wagner, G. and Choset, H., “Gaussian Reconstruction of Swarm Behavior from Partial Data,” IEEE International Conference on Robotics and Automation (2015) pp. 5864–5870.Google Scholar
Brown, D. S. and Goodrich, M. A., “Limited Bandwidth Recognition of Collective Behaviors in Bio-inspired swarms,” 13th International Conference on Autonomous Agents and Multiagent Systems (2015) pp. 405–412.Google Scholar
Freeman, R. A., Yang, P. and Lynch, K. M., “Distributed Estimation and Control of Swarm Formation Statistics,” American Control Conference (2006) p. 7.10.1109/ACC.2006.1655446CrossRefGoogle Scholar
Klingner, J., Ahmed, N. and Correll, N., “Fault-Tolerant Covariance Intersection for Localizing Robot Swarms,” Dist. Auton. Robot. Syst. 122(1), 1–10 (2019).Google Scholar
Mayya, S., Pierpaoli, P., Nair, G. and Egerstedt, M., “Localization in densely packed swarms using interrobot collisions as a sensing modality,” IEEE Trans. Robotic. 35(1), 21–34 (2019).CrossRefGoogle Scholar
Ma, D., Er, M. J., Wang, B. and Lim, H. B., “Range-free wireless sensor networks localization based on hop-count quantization,” Telecomm. Syst. 50(3), 199–213 (2012).10.1007/s11235-010-9395-yCrossRefGoogle Scholar
Fukui, S. and Naruse, K., “Swarm EKF Localization for a Multiple Robot System with Range-Only Measurements,” Proceedings of the IEEE/SICE International Symposium on System Integration (2013) pp. 796–801.Google Scholar
Inoue, D., Murai, D., Ikuta, Y. and Yoshida, H., “Distributed Range-Based Localization for Swarm Robot Systems using Sensor-Fusion Technique,” Proceedings of the 8th International Conference on Sensor Networks (2019) pp. 13–22.Google Scholar
Kim, J. Y., Kashino, Z., Pineros, L. M., Bayat, S., Colaco, T., Nejat, G. and Benhabib, B., “A high-performance millirobot for swarm-behaviour studies: Swarm-topology estimation,” Int. J. Adv. Rob. Syst. 16(6), 1–8 (2019).Google Scholar
Bezouska, W. and Barnhart, D., “Decentralized Cooperative Localization with Relative Pose Estimation for a Spacecraft Swarm,” IEEE Aerospace Conference (2019) pp. 1–13.Google Scholar
Pires, A. G., Macharet, D. G. and Chaimowicz, L., “Exploring Heterogeneity for Cooperative Localization in Swarm Robotics,” International Conference on Advanced Robotics (2015) pp. 407–414.Google Scholar
Wu, H., Qu, S., Xu, D. and Chen, C., “Precise localization and formation control of swarm robots via wireless sensor networks,” Math. Prob. Eng. 2014(1), 1–12 (2014).Google Scholar
Gutmann, J., Fong, P. and Munich, M. E., “Localization in a Vector Field Map,” IEEE/RSJ International Conference on Intelligent Robots and Systems (2012) pp. 3144–3151.Google Scholar
Olson, E., “AprilTag: A Robust and Flexible Visual Fiducial System,” IEEE International Conference on Robotics and Automation (2011) pp. 3400–3407.Google Scholar
Krajník, T., Nitsche, M., Faigl, J., Duckett, T., Mejail, M. and Pøeuèil, L., “External Localization System for Mobile Robotics,” International Conference on Advanced Robotics (2013) pp. 1–6.Google Scholar
“Motion Capture Systems,” OptiTrack. http://optitrack.com (accessed Jan. 07, 2021).Google Scholar
Navarro-Serment, L. E., Paredis, C. J. J. and Khosla, P. K., “A Beacon System for the Localization of Distributed Robotic Teams,” Proceedings of the International Conference on Field and Service Robotics (1999) pp. 232–237.Google Scholar
Kurazume, R., Nagata, S. and Hirose, S., “Cooperative Positioning with Multiple Robots,” Proceedings of the IEEE International Conference on Robotics and Automation (1994) pp.1250–1257.Google Scholar
Tully, S., Kantor, G. and Choset, H., “Leap-Frog Path Design for Multi-Robot Cooperative Localization,” In: Field and Service Robotics (2010) pp. 307–317.Google Scholar
Xu, S., Ji, Z., Pham, D. T. and Yu, F., “Simultaneous localization and mapping: Swarm robot mutual localization and sonar arc bidirectional carving mapping,” Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 225(3), 733–744 (2011).Google Scholar
Pires, A. G., Macharet, D. G. and Chaimowicz, L., “Towards Cooperative Localization in Robotic Swarms,” In: Distributed Autonomous Robotic Systems (2016) pp. 105–118.Google Scholar
Zak, G., Benhabib, B., Fenton, R. G. and Saban, I., “Application of the weighted least squares parameter estimation method to the robot calibration,” J. Mech. Des. 116(3), 890–893 (1994).10.1115/1.2919465CrossRefGoogle Scholar
Thrun, S., Burgard, W., Fox, D. and Arkin, R. C., “The GraphSLAM Algorithm,” In: Probabilistic Robotics (MIT Press, 2005) pp. 337–384.Google Scholar