Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-22T00:46:49.991Z Has data issue: false hasContentIssue false

Application of bidirectional rapidly exploring random trees (BiRRT) algorithm for collision-free trajectory planning of free-floating space manipulator

Published online by Cambridge University Press:  20 July 2022

Tomasz Rybus*
Affiliation:
Centrum Badań Kosmicznych Polskiej Akademii Nauk (CBK PAN), Warsaw, Poland
Jacek Prokopczuk
Affiliation:
Centrum Badań Kosmicznych Polskiej Akademii Nauk (CBK PAN), Warsaw, Poland
Mateusz Wojtunik
Affiliation:
Centrum Badań Kosmicznych Polskiej Akademii Nauk (CBK PAN), Warsaw, Poland
Konrad Aleksiejuk
Affiliation:
Centrum Badań Kosmicznych Polskiej Akademii Nauk (CBK PAN), Warsaw, Poland
Jacek Musiał
Affiliation:
Centrum Badań Kosmicznych Polskiej Akademii Nauk (CBK PAN), Warsaw, Poland
*
*Corresponding author. E-mail: [email protected]

Abstract

On-orbit servicing and active debris removal missions will rely on the use of unmanned satellite equipped with a manipulator. Capture of the target object will be the most challenging phase of these missions. During the capture manoeuvre, the manipulator must avoid collisions with elements of the target object (e.g., solar panels). The dynamic equations of the satellite-manipulator system must be used during the trajectory planning because the motion of the manipulator influences the position and orientation of the satellite. In this paper, we propose application of the bidirectional rapidly exploring random trees (BiRRT) algorithm for planning a collision-free trajectory of a manipulator mounted on a free-floating satellite. A new approach based on pseudo-velocities method (PVM) is used for construction of nodes of the trajectory tree. Initial nodes of the second tree are selected from the set of potential final configurations of the system. The proposed method is validated in numerical simulations performed for a planar case (3-DoF manipulator). The obtained results are compared with the results obtained with two other trajectory planning methods based on the RRT algorithm. It is shown that in a simple test scenario, the proposed BiRRT PVM algorithm results in a lower manipulator tip position error. In a more difficult test scenario, only the proposed method was able to find a solution. Practical applicability of the BiRRT PVM method is demonstrated in experiments performed on a planar air-bearing microgravity simulator where the trajectory is realised by a manipulator mounted on a mock-up of the free-floating servicing satellite.

Type
Research Article
Copyright
© The Author(s), 2022. 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

Sąsiadek, J., “Space Robotics and Its Challenges,” In: Aerospace Robotics. GeoPlanet: Earth and Planetary Sciences (Sąsiadek, J., ed.) (Springer, Berlin/Heidelberg, 2013) pp. 18.CrossRefGoogle Scholar
Ticker, R. L. and Callen, P. S., “Robotics on the International Space Station: Systems and Technology for Space Operations, Commerce and Exploration,” In: Proceedings of the AIAA SPACE, 2012 Conference and Exposition, Pasadena, CA, USA (2012).Google Scholar
Gibbs, G. and Sachdev, S., “Canada and the international space station program: Overview and status,” Acta Astronaut. 51(1-9), 591600 (2002).Google ScholarPubMed
Rembala, R. and Ower, C., “Robotic assembly and maintenance of future space stations based on the ISS mission operations experience,” Acta Astronaut. 65(7–8), 912920 (2009).CrossRefGoogle Scholar
Xue, Z., Liu, J., Wu, C. and Tong, Y., “Review of in-space assembly technologies,” Chin. J. Aeronaut. 34(11), 2147 (2020).CrossRefGoogle Scholar
Belonozhko, P. P., “Modern Space Robotics: History, Trends, Prospects,” In: Robotics: Industry 4.0 Issues and New Intelligent Control Paradigms, Studies in Systems, Decision and Control (Kravets, A. G., ed.), vol. 272 (Springer, Cham, 2020) pp. 161170.Google Scholar
Li, W.-J., Cheng, D.-Y., Liu, X.-G., Wang, Y.-B., Shi, W.-H., Tang, Z.-X., Gao, F., Zeng, F.-M., Chai, H.-Y., Luo, W.-B., Cong, Q., Gao, Z.-L., “On-orbit service (OOS) of spacecraft: A review of engineering developments,” Prog. Aerosp. Sci. 108(2), 32120 (2019).CrossRefGoogle Scholar
Hudson, J. S. and Kolosa, D., “Versatile on-orbit servicing mission design in geosynchronous earth orbit,” J. Spacecraft Rockets 57(4), 844850 (2020).CrossRefGoogle Scholar
Shan, M., Guo, J. and Gill, E., “Review and comparison of active space debris capturing and removal methods,” Prog. Aerosp. Sci. 80, 1832 (2016).CrossRefGoogle Scholar
Mark, C. P. and Kamath, S., “Review of active space debris removal methods,” J. Space Policy 47, 194206 (2019).CrossRefGoogle Scholar
Murtaza, A., Pirzada, S. J. H., Xu, T. and Jianwei, L., “Orbital debris threat for space sustainability and way forward,” IEEE Access 8, 6100061019 (2020).CrossRefGoogle Scholar
Estable, S., Pruvost, C., Ferreira, E., Telaar, J., Fruhnert, M., Imhof, Ch., Rybus, T., Peckover, H., Lucas, R., Ahmed, R., Oki, T., Wygachiewicz, M., Kicman, P., Lukasik, A., Santos, N., Milhano, T., Arroz, P., Biesbroek, R., Wolahan, A., “Capturing and deorbiting Envisat with an Airbus Spacetug. Results from the ESA e.deorbit Consolidation Phase study,” J. Space Saf. Eng. 7(1), 5266 (2020).CrossRefGoogle Scholar
Seweryn, K. and Sąsiadek, J. Z., “Satellite angular motion classification for active on-orbit debris removal using robots,” Aircr. Eng. Aerosp. Technol. 91(2), 317332 (2019).CrossRefGoogle Scholar
Dubowsky, S. and Papadopoulos, E., “The kinematics, dynamics, and control of free-flying and free-floating space robotic systems,” IEEE Trans. Robot. Autom. 9(5), 531543 (1993).CrossRefGoogle Scholar
Umetani, Y. and Yoshida, K., “Theoretical and Experimental Study on In-Orbit Capture Operation with Satellite Mounted Manipulator,” In: Automatic Control in Aerospace 1989: Selected Papers from the IFAC Symposium, Tsukuba, Japan, 17-21 July 1989 (T. Nishimura, ed.) (Pergamon, Oxford, 1990) pp. 121-126.CrossRefGoogle Scholar
Papadopoulos, E., Aghili, F., Ma, O. and Lampariello, R., “Robotic manipulation and capture in space: A survey,” Front. Robot. AI 8, 686723 (2021).CrossRefGoogle ScholarPubMed
Wang, M., Luo, J., Fang, J. and Yuan, J., “Optimal trajectory planning of free-floating space manipulator using differential evolution algorithm,” Adv. Space Res. 61(6), 15251536 (2018).CrossRefGoogle Scholar
Jin, R., Rocco, P. and Geng, Y., “Cartesian trajectory planning of space robots using a multi-objective optimization,” Aerosp. Sci. Technol. 108, 106360 (2021).CrossRefGoogle Scholar
Zhang, L., Jia, Q., Chen, G. and Sun, H., “Pre-impact trajectory planning for minimizing base attitude disturbance in space manipulator systems for a capture task,” Chin. J. Aeronaut. 28(4), 11991208 (2015).CrossRefGoogle Scholar
Trigatti, G., Boscariol, P., Scalera, L., Pillan, D. and Gasparetto, A., “A Look-Ahead Trajectory Planning Algorithm for Spray Painting Robots with Non-spherical Wrists,” In: Mechanism Design for Robotics, Mechanisms and Machine Science (Gasparetto, A. and Ceccarelli, M., eds.), vol. 66 (Springer, Cham, 2018) pp. 235242.Google Scholar
Rybus, T., Seweryn, K. and Sąsiadek, J. Z., “Trajectory Optimization of Space Manipulator with Non-zero Angular Momentum During Orbital Capture Maneuver,” In: Proceedings of the AIAA Guidance, Navigation, and Control Conference (AIAA-GNC 2016), San Diego, CA, USA (2016).Google Scholar
Masehian, E. and Sedighizadeh, D., “Classic and heuristic approaches in robot motion planning – a chronological review,” Int. J. Mech. Aerosp. Ind. Mechatron. Manuf. Eng. 1(5), 228233 (2007).Google Scholar
Gasparetto, A., Boscariol, P., Lanzutti, A. and Vidoni, R., “Trajectory planning in robotics,” Math. Comput. Sci. 6(3), 269279 (2012).CrossRefGoogle Scholar
Rybus, T., “Obstacle avoidance in space robotics: Review of major challenges and proposed solutions,” Prog. Aerosp. Sci. 101(5), 3148 (2018).CrossRefGoogle Scholar
Yanoshita, Y. and Tsuda, S., “Space Robot Path Planning for Collision Avoidance,” In: Proceedings of the International MultiConference of Engineers and Computer Scientists (IMECS’2009), vol. 2, Hong Kong (2009).Google Scholar
Lampariello, R., “Motion Planning for the On-Orbit Grasping of a Non-cooperative Target Satellite with Collision Avoidance,” In: Proceedings of the 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS’2010), Sapporo, Japan (2010).Google Scholar
Gao, X., Jia, Q., Sun, H. and Chen, G., “Research on path planning for 7-DOF space manipulator to avoid obstacle based on A* algorithm,” Sens. Lett. 9(4), 15151519 (2011).CrossRefGoogle Scholar
Benevides, J. R. S., Grassi, V. Jr, “Path Planning with Collision Avoidance for Free-Floating Manipulators: A RRT-Based Approach,” In: Robotics. SBR 2016, LARS 2016, Communications in Computer and Information Science (Osório, F. S. and Gonçalves, R. S., eds.), vol. 619 (Springer, Cham, 2016) pp. 103119.Google Scholar
LaValle, S. M. and Kuffner, J. J., “Randomized kinodynamic planning,” Int. J. Robot. Res. 20(5), 378400 (2001).CrossRefGoogle Scholar
Zhang, H., Wang, Y., Zheng, J. and Yu, J., “Path planning of industrial robot based on improved RRT algorithm in complex environments,” IEEE Access 6, 5329653306 (2018).CrossRefGoogle Scholar
Ch. Yuan, W. Z., Liu, G., Pan, X. and Liu, X., “A heuristic rapidly-exploring random trees method for manipulator motion planning,” IEEE Access 8, 900910 (2018).CrossRefGoogle Scholar
Han, B. and Liu, S., “RRT Based Obstacle Avoidance Path Planning for 6-DOF Manipulator,” In: Proceedings of the 9th IEEE Data Driven Control and Learning Systems Conference (DDCLS’20), Liuzhou, China (2020).Google Scholar
Benevides, J. R. and Grassi, V. Jr, “Autonomous Path Planning of Free-Floating Manipulators Using RRT-Based Algorithms,” In: Proceedings of the 12th Latin American Robotics Symposium and 3rd Brazilian Symposium on Robotics (LARS-SBR), Uberlandia, Minas Gerais, Brasil (2015).Google Scholar
Rybus, T. and Seweryn, K., “Application of Rapidly-Exploring Random Trees (RRT) Algorithm for Trajectory Planning of Free-Floating Space Manipulator,” In: Proceedings of the 10th International Workshop on Robot Motion and Control (RoMoCo’2015), Poznań, Poland (2015).Google Scholar
Xie, Y., Zhang, Z., Wu, X., Shi, Z., Chen, Y., Wu, B. and Mantey, K. A., “Obstacle avoidance and path planning for multi-joint manipulator in a space robot,” IEEE Access 8, 35113526 (2019).CrossRefGoogle Scholar
Zhang, X., Liu, J. and Li, Y., “An obstacle avoidance algorithm for space hyper-redundant manipulators using combination of RRT and shape control method,” Robotica 39(9), 10361069 (2021).Google Scholar
Zong, L., Emami, M. R. and Luo, J., “Reactionless control of free-floating space manipulators,” IEEE Trans. Aerosp. Electron. Syst. 56(2), 14901503 (2019).CrossRefGoogle Scholar
Zhang, H. and Zhu, Z., “Sampling-based motion planning for free-floating space robot without inverse kinematics,” Appl. Sci. 10(24), 9137 (2020).CrossRefGoogle Scholar
Rybus, T., “Point-to-point motion planning of a free-floating space manipulator using the Rapidly-Exploring Random Trees (RRT) method,” Robotica 38(6), 957982 (2020).CrossRefGoogle Scholar
Serrantola, W. G., Bueno, J. N. and Grassi, V. Jr, “Planejamento de rota de um manipulador espacial planar de base livre flutuante com dois bracos utilizando RRT* ,” In: Proceedings of the XIII Simposio Brasileiro de Automacao Inteligente, Porto Alegre, Brasil (2017).Google Scholar
Serrantola, W. G. and Grassi, V. Jr, “Trajectory Planning for a Dual-Arm Planar Free-Floating Manipulator Using RRTControl,” In: Proceedings of the 19th International Conference on Advanced Robotics (ICAR’2019), Belo Horizonte, Brasil (2019).Google Scholar
Basmadji, F. L., Seweryn, K. and Sasiadek, J. Z., “Space robot motion planning in the presence of nonconserved linear and angular momenta,” Multibody Syst. Dyn. 50(1), 7196 (2020).CrossRefGoogle Scholar
Bhargava, R., Mithun, P., Anurag, V. V., Hafez, A. H. A. and Shah, S. V., “Image Space Based Path Planning for Reactionless Manipulation of Redundant Space Robot,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’2016), Daejeon, Korea (2016).Google Scholar
James, F., Shah, S. V., Singh, A. K., Krishna, K. M. and Misra, A. K., “Reactionless maneuvering of a space robot in precapture phase,” J. Guid. Control. Dyn. 39(10), 24172422 (2016).CrossRefGoogle Scholar
Pareekutty, N., James, F., Ravindran, B. and Shah, S. V., “qRRT: Quality-Biased Incremental RRT for Optimal Motion Planning in Non-holonomic Systems,” arXiv preprint, arXiv: 2101.02635v1 (2021).Google Scholar
Wan, W., Sun, C. and Yuan, J., “Adaptive caging configuration design algorithm of hyper-redundant manipulator for dysfunctional satellite pre-capture,” IEEE Access 8, 2254622559 (2020).CrossRefGoogle Scholar
Doerr, B. and Linares, R., “Motion Planning and Control for On-Orbit Assembly Using LQR-RRT* and Nonlinear MPC,” arXiv preprint, arXiv: 2008.02846v1 (2020).Google Scholar
Zappulla, R., Virgili-Llop, J. and Romano, M., “Near-Optimal Real-Time Spacecraft Guidance and Control Using Harmonic Potential Functions and a Modified RRT,” In: Proceedings of 27th AAS/AIAA Space Flight Mechanics Meeting, San Antonio, TX, USA (2017).Google Scholar
Basmadji, F. L., Chmaj, G., Rybus, T. and Seweryn, K., “Microgravity Testbed for the Development of Space Robot Control Systems and the Demonstration of Orbital Maneuvers,” In: Proceedings of SPIE: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments, 111763V, Wilga, Poland (2019).Google Scholar
Junkins, J. L. and Schaub, H., “An instantaneous eigenstructure quasivelocity formulation for nonlinear multibody dynamics,” J. Astronaut. Sci. 45(3), 279295 (1997).CrossRefGoogle Scholar
Yoshida, K., Wilcox, B., Hirzinger, G. and Lampariello, R., “Space Robotics,” In: Springer Handbook of Robotics (Siciliano, B. and Khatib, O., eds.) (Springer, New York, 2016) pp. 14231462.CrossRefGoogle Scholar
Vafa, Z. and Dubowsky, S., “The kinematics and dynamics of space manipulators: The virtual manipulator approach,” Int. J. Robot. Res. 9(4), 321 (1990).CrossRefGoogle Scholar
Liang, B., Xu, Y. and Bergerman, M., “Dynamically Equivalent Manipulator for Space Manipulator System,” In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 1997), Albuquerque, NM, USA (1997).Google Scholar
Umetani, Y. and Yoshida, K., “Resolved motion rate control of space manipulators with generalized Jacobian matrix,” IEEE Trans. Robot. Autom. 5(3), 303314 (1989).CrossRefGoogle Scholar
Seweryn, K. and Banaszkiewicz, M., “Optimization of the Trajectory of a General Free-Flying Manipulator During the Rendezvous Maneuver,” In: Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit (AIAA-GNC 2008), Honolulu, Hawaii, USA (2008).Google Scholar
Rybus, T., Seweryn, K. and Sasiadek, J. Z., “Control system for free-floating space manipulator based on nonlinear model predictive control (NMPC),” J. Intell. Robot. Syst. 85(3), 491509 (2017).Google Scholar
Schaub, H. and Junkins, J.. Analytical Mechanics of Space Systems (American Institute of Aeronautics and Astronautics, Reston, 2003).CrossRefGoogle Scholar
Sabatini, M., Gasbarri, P. and Palmerini, G. B., “Coordinated control of a space manipulator tested by means of an air bearing free floating platform,” Acta Astronaut. 139(5), 296305 (2017).CrossRefGoogle Scholar
Rank, R., Mühlbauer, Q., Naumann, W. and Landzettel, K., “The DEOS Automation and Robotics Payload,” In: Proceedings of the 11th ESA Workshop on Advanced Space Technologies for Robotics and Automation (ASTRA 2011), ESTEC, Noordwijk, The Netherlands (2011).Google Scholar
Papadopoulos, E., “Nonholonomic Behavior in Free-Floating Space Manipulators and Its Utilization,” In: Motion Planning (Li, Z. and Canny, J. F., eds.) (Kluwer Academic Publishers, Boston, MA, 1993) pp. 423445.Google Scholar
Rybus, T., Wojtunik, M. and Basmadji, F. L., “Optimal collision-free path planning of a free-floating space robot using spline-based trajectories,” Acta Atronaut. 190(6), 395408 (2022).CrossRefGoogle Scholar
Rybus, T., Lisowski, J., Seweryn, K. and Barciński, T., “Numerical Simulations and Analytical Analysis of the Orbital Capture Maneouvre as a Part of the Manipulator-Equipped Servicing Satellite Design,” In: Proceedings of the 17th International Conference on Methods and Models in Automation and Control (MMAR 2012), Miedzyzdroje, Poland (2012).Google Scholar
Waldron, K. and Schmiedeler, J., “Kinematics,” In: Springer Handbook of Robotics (Siciliano, S. and Khatib, O., eds.) (Springer, Berlin/Heidelberg, 2008) pp. 2325.Google Scholar
Berenson, D., Srinivasa, S. S., Ferguson, D., Collet, A. and Kuffner, J. J., “Manipulation Planning with Workspace Goal Regions,” In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2009), Kobe, Japan (2009).Google Scholar
Hirano, Y., Kitahama, K. and Yoshizawa, S., “Image-Based Object Recognition and Dexterous Hand/Arm Motion Planning Using RRTS for Grasping in Cluttered Scene,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005), Edmonton, AB, Canada (2005).Google Scholar
Kuffner, J. J. and LaValle, S. M., “RRT-Connect: An Efficient Approach to Single-Query Path Planning,” In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2000), San Francisco, CA, USA (2000).Google Scholar
LaValle, S. and Kuffner, J., “Rapidly-Exploring Random Trees: Progress and Prospects,” In: Algorithmic and Computational Robotics: New Directions (Donald, B. R., Lynch, K. M. and Rus, D., eds.) (A. K. Peters, Wellesley, MA, 2001) pp. 293308.Google Scholar
Qureshi, A. H. and Ayaz, Y., “Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments,” Rob. Auton. Syst. 68, 111 (2015).CrossRefGoogle Scholar
Lamiraux, F., Ferré, E. and Vallée, E., “Kinodynamic Motion Planning: Connecting Exploration Trees Using Trajectory Optimization Methods,” In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2004), New Orleans, LA, USA (2004).Google Scholar
Rybus, T. and Seweryn, K., “Zastosowanie metody sztucznych pól potencjału do planowania trajektorii manipulatora satelitarnego,” In: Postepy Robotyki, Prace Naukowe Politechniki Warszawskiej: Elektronika (Tchon, K. and Zielinski, C., eds.) (Oficyna Wydawnicza Politechniki Warszawskiej, Warszawa, 2018) pp. 6174 [in polish: “Application of the artificial potential field method for trajectory planning of space manipulator”].Google Scholar
Redon, S., Kheddar, A. and Coquillart, S., “An Algebraic Solution to the Problem of Collision Detection for Rigid Polyhedral Objects,” In: International Conference on Robotics and Automation (ICRA 2000), San Francisco, CA, USA (2000).Google Scholar
Rybus, T. and Seweryn, K., “Planar air-bearing microgravity simulators: Review of applications, existing solutions and design parameters,” Acta Atronaut. 120, 239259 (2016).CrossRefGoogle Scholar