Abstract
The field of Very-Large-Scale Robotics (VLSR) has garnered significant attention due to its ability to tackle complex and coordinated tasks. However, current motion planning methods for VLSR face challenges related to scalability and ensuring safety. To address these limitations, we propose a novel risk-aware motion planning framework for VLSR. Our approach formulates a finite-time optimal control (FTOC) problem based on the macroscopic state of VLSR and incorporates conditional value-at-risk (CVaR) to avoid collision. We present a systematic approach that leverages the linearized Signed Distance Function to efficiently compute the CVaR of the distance between VLSR and obstacles. Subsequently, we develop an approximation approach to reformulate the nonlinear FTOC as a linear programming problem using the discretized workspace, resulting in computationally efficient online motion planning. Simulations on VLSR consisting of five hundred robots demonstrate the effectiveness of the proposed approach in both computational efficiency and risk mitigation.
This work is sponsored by Beijing Nova Program (20220484056) and the National Natural Science Foundation of China (62203018). It is also partially supported by the NASA Established Program to Stimulate Competitive Research, Grant # 80NSSC22M0027. All correspondences should be sent to Chang Liu.
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References
Bevacqua, G., Cacace, J., Finzi, A., Lippiello, V.: Mixed-initiative planning and execution for multiple drones in search and rescue missions. Proc. Int. Conf. Autom. Planning Sched. 25, 315–323 (2015)
Cortes, J., Martinez, S., Karatas, T., Bullo, F.: Coverage control for mobile sensing networks. IEEE Trans. Robot. Autom. 20(2), 243–255 (2004)
Prorok, A., Correll, N., Martinoli, A.: Multi-level spatial modeling for stochastic distributed robotic systems. Int. J. Rob. Res. (IJRR) 30, 574–589 (2011)
Elamvazhuthi, K., Berman, S.: Mean-field models in swarm robotics: a survey. Bioinspiration Biomimetics 15(1), 015001 (2019)
Crespi, V., Galstyan, A., Lerman, K.: Top-down vs bottom-up methodologies in multi-agent system design. Auton. Robot. 24, 303–313 (2008)
Foderaro, G., Zhu, P., Wei, H., Wettergren, T.A., Ferrari, S.: Distributed optimal control of sensor networks for dynamic target tracking. IEEE Trans. Control Netw. Syst. 5(1), 142–153 (2016)
Rudd, K., Foderaro, G., Zhu, P., Ferrari, S.: A generalized reduced gradient method for the optimal control of very-large-scale robotic systems. IEEE Trans. Rob. 33(5), 1226–1232 (2017)
Zhu, P., Liu, C., Ferrari, S.: Adaptive online distributed optimal control of very-large-scale robotic systems. IEEE Trans. Control Netw. Syst. 8(2), 678–689 (2021)
Ono, M., Pavone, M., Kuwata, Y., Balaram, J.: Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Auton. Robot. 39, 555–571 (2015)
Wang, A., Jasour, A., Williams, B.C.: Non-gaussian chance-constrained trajectory planning for autonomous vehicles under agent uncertainty. IEEE Robot. Autom. Lett. 5(4), 6041–6048 (2020)
Fan, D.D., Otsu, K., Kubo, Y., Dixit, A., Burdick, J., Agha-Mohammadi, AA.: Step: stochastic traversability evaluation and planning for risk-aware off-road navigation. Robot.: Sci. Syst., 1–21, RSS Foundation (2021)
Hakobyan, A., Kim, G.C., Yang, I.: Risk-aware motion planning and control using cVaR-constrained optimization. IEEE Robot. Autom. Lett. 4(4), 3924–3931 (2019)
Villani, C.: Topics in optimal transportation. Am. Math. Soc. 58 (2021)
Chen, Y., Georgiou, T.T., Tannenbaum, A.: Optimal transport for gaussian mixture models. IEEE Access 7, 6269–6278 (2018)
Norton, M., Khokhlov, V., Uryasev, S.: Calculating CVaR and bPOE for common probability distributions with application to portfolio optimization and density estimation. Ann. Oper. Res. 299, 1281–1315 (2021)
Gilbert, E.G., Johnson, D.W., Keerthi, S.S.: A fast procedure for computing the distance between complex objects in three-dimensional space. IEEE J. Robot. Autom. 4(2), 193–203 (1988)
Van Den Bergen, G.: Proximity queries and penetration depth computation on 3D game objects. In: Game Developers Conference, vol. 170 (2001)
Gao, H., et al.: Probabilistic visibility-aware trajectory planning for target tracking in cluttered environments. ArXiv Preprint ArXiv:2306.06363 (2023)
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Yang, X., Gao, H., Zhu, P., Liu, C. (2023). Risk-Aware Motion Planning for Very-Large-Scale Robotics Systems Using Conditional Value-at-Risk. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14273. Springer, Singapore. https://doi.org/10.1007/978-981-99-6498-7_44
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DOI: https://doi.org/10.1007/978-981-99-6498-7_44
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