Study of the method of trajectory and sequential deformations simultaneous planning for a tensegrity drone

Modern aerial robots, in particular the drones, are developing at a rapid pace. Drones appear to be a promising area in robotics performing dangerous tasks during search and rescue operations, as well as in practical applications such as photography and cinematography. An urgent task is to ensure the drone safety against their mechanical damage when interacting with the external environment, as well as the safety of people in case of contact with the drones. To solve this problem, it is advisable to use tensegrity drones with the deformable structure and the ability to adapt to the changing environment parameters taking into account the obstacles encountered in the flight. These drones are able to ensure the controlled deformation of their fuselage in flight making them more mobile in difficult environments. A method was previously proposed to plan such trajectories based on solving the optimization problem with the linear matrix inequalities. However, numerical properties of the method remained unexplored. The problem of planning the tensegrity drone flight was considered. Numerical experiments were carried out. It was established that the surrounding space geometry had insignificant effect on the task implementation, but very significantly affected computational complexity and elapsed processor time.

References

[1] Savin S., Klimchik A. Morphing-enabled path planning for flying tensegrity robots as a semidefinite program. Frontiers in Robotics and AI, 2022, vol. 9, art. 812849, doi: https://doi.org/10.3389/frobt.2022.812849

[2] Nitta K., Higuchi K., Rekimoto J. HoverBall: augmented sports with a flying ball. 5th Augmented Human Int. Conf., 2014, doi: https://doi.org/10.1145/2582051.2582064

[3] Yamaguchi K., Kato G., Kuroda Y. et al. A non-grounded and encountered-type haptic display using a drone. Symp. on Spatial User Interaction, 2016, pp. 43–46, doi: https://doi.org/10.1145/2983310.2985746

[4] Brescianini D., D’Andrea R. Design, modeling and control of an omni-directional aerial vehicle. IEEE ICRA, 2016, pp. 3261–3266, doi: https://doi.org/10.1109/ICRA.2016.7487497

[5] Kornatowski P.M., Bhaskaran A., Heitz G.M. et al. Last-centimeter personal drone delivery: field deployment and user interaction. IEEE Robot. Autom. Lett., 2018, vol. 3, no. 4, pp. 3813–3820, doi: https://doi.org/10.1109/LRA.2018.2856282

[6] Kornatowski P.M., Mintchev S., Floreano D. An origami-inspired cargo drone. IEEE/RSJ IROS, 2017, pp. 6855–6862, doi: https://doi.org/10.1109/IROS.2017.8206607

[7] Shu J., Chirarattananon P. A quadrotor with an origami-inspired protective mechanism. IEEE Robot. Autom. Lett., 2019, vol. 4, no. 4, pp. 3820–3827, doi: https://doi.org/10.1109/LRA.2019.2929978

[8] Bucki N., Mueller M.W. Design and control of a passively morphing quadcopter. IEEE ICRA, 2019, pp. 9116–9122, doi: https://doi.org/10.1109/ICRA.2019.8794373

[9] Falanga D., Kleber K., Mintchev S. et AL. The foldable drone: a morphing quadrotor that can squeeze and fly. IEEE Robot. Autom. Lett., 2019, vol. 4 no. 2, pp. 209–216, doi: https://doi.org/10.1109/LRA.2018.2885575

[10] Klaptocz A., Briod A., Daler L. et al. Euler spring collision protection for flying robots. IEEE/RSJ IROS, 2013, pp. 1886–1892, doi: https://doi.org/10.1109/IROS.2013.6696606

[11] Skelton R.E., de Oliveira M.C. Tensegrity systems. Springer. 2009. 216 p.

[12] Guest S.D. The stiffness of tensegrity structures. IMA J. of Applied Mathematics, 2011, vol. 76, no. 1, pp. 57–66, doi: https://doi.org/10.1093/imamat/hxq065

[13] Caluwaerts K., Despraz J., Işçen A. et al. Design and control of compliant tensegrity robots through simulation and hardware validation. J. R. Soc. Interface, 2014, vol. 11, no. 98, art. 20140520, doi: https://doi.org/10.1098/rsif.2014.0520

[14] Paul C., Valero-Cuevas F.J., Lipson H. Design and control of tensegrity robots for locomotion. IEEE Trans. Robot., 2006, vol. 22, no. 5, pp. 944–957, doi: https://doi.org/10.1109/TRO.2006.878980

[15] Sabelhaus A.P., Bruce J., Caluwaerts K. et l. System design and locomotion of SUPERball, an untethered tensegrity robot. IEEE ICRA, 2015, pp. 2867–2873, doi: https://doi.org/10.1109/ICRA.2015.7139590

[16] Bruce J., Sabelhaus A.P., Chen Y. et al. SUPERball: exploring tensegrities for planetary probes. i-SAIRAS, 2014, vol. ARC-E-DAA-TN15338.

[17] Sabelhaus A.P., Bruce J., Caluwaerts K. et al. Hardware design and testing of SUPERball, a modular tensegrity robot. WCSCM, 2014, doc. 20140011157.

[18] Park J.K., Chung T.M. Boundary-RRT* algorithm for drone collision avoidance and interleaved path re-planning. J. of Information Processing Systems, 2020, vol. 16, no. 6, pp. 1324–1342.

[19] Jia D., Vagners J. Parallel evolutionary algorithms for UAV path planning. AIAA 1st Intelligent Systems Technical Conf., 2004, art. 6230, doi: https://doi.org/10.2514/6.2004-6230

[20] Duchoň F., Babinec A., Kajan M. et al. Path planning with modified a star algorithm for a mobile robot. Procedia Eng., 2014, vol. 96, pp. 59–69, doi: https://doi.org/10.1016/j.proeng.2014.12.098

[21] Villaseñor C., Gallegos A.A., Lopez-Gonzalez G. et al. Ellipsoidal path planning for unmanned aerial vehicles. Appl. Sci., 2021, vol.11, no. 17, art. 7997, doi: https://doi.org/10.3390/app11177997

[22] Gao F., Wu W., Lin Y. et al. Online safe trajectory generation for quadrotors using fast marching method and Bernstein basis polynomial. IEEE ICRA, 2018, pp. 344–351, doi: https://doi.org/10.1109/ICRA.2018.8462878

[23] Zalyaev E., Savin S. Tensegrity morphing: machine learning-based tensegrity deformation predictor for traversing cluttered environments. APMS. Springer, 2021, pp. 473–480, doi: https://doi.org/10.1007/978-3-030-85910-7_50

[24] Deits R., Tedrake R. Computing large convex regions of obstacle-free space through semidefinite programming. In: Algorithmic foundations of robotics XI. Springer, 2015, pp. 109–124.

[25] Savin S. An algorithm for generating convex obstacle-free regions based on stereographic projection. IEEE SIBCON, 2017, doi: https://doi.org/10.1109/SIBCON.2017.7998590