Abstract
Noise coming from sensors or caused by external world phenomena results in measurement errors that cause uncertainties in some robotic tasks, e.g. tracking a robot displacement and tracking an observed target. Control approaches such as model predictive control (MPC) usually guarantee constraints satisfaction by way of using detailed models of prediction. Although the deterministic MPC allows certain robustness to be controlled in the system, it usually does not adequately deal with uncertainties. Therefore, we introduce in this manuscript a pure perception motion control, which consists of an approach that deals with the uncertainty problem through a stochastic nonlinear model predictive control (SNMPC) by minimizing only the covariances matrices of target observation and robot state estimation. As introduced previously, it can be used to track targets that are observed during some tasks. The SNMPC penalizes the undesired behavior, allowing the robot to converge to the optimal pose to observe the target optimally. A modification provided both in the prediction model and the cost function allows this minimization to be achieved. The proposed stochastic nonlinear controller is validated, providing a satisfactory control of the target tracking, by way of results obtained from simulation, which are presented and discussed in the paper to verify our proposal.
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References
Ahmad, A., Lima, P.: Multi-robot cooperative spherical-object tracking in 3d space based on particle filters. Robot. Auton. Syst. 61(10), 1084–1093 (2013). https://doi.org/10.1016/j.robot.2012.12.008
Barreto, S.J.C.L., Conceição, A.S., Dórea, C E T, Martinez, L., de Pieri, E.R.: Design and implementation of model-predictive control with friction compensation on an omnidirectional mobile robot. IEEE/ASME Trans. Mech. 19(2), 467–476 (2014). https://doi.org/10.1109/TMECH.2013.2243161
Bascetta, L., Ferretti, G., Matteucci, M., Bossi, M.: Lft-based mpc control of an autonomous vehicle. IFAC-PapersOnLine 49(15), 7–12 (2016). https://doi.org/10.1016/j.ifacol.2016.07.597
Basso, G.F., Amorim, T.G.S.D., Brito, A.V., Nascimento, T.P.: Kalman filter with dynamical setting of optimal process noise covariance. IEEE Access 5, 8385–8393 (2017). https://doi.org/10.1109/ACCESS.2017.2697072
Blackmore, L., Ono, M., Bektassov, A., Williams, B.C.: A probabilistic particle-control approximation of chance-constrained stochastic predictive control. IEEE Trans. Robot. 26(3), 502–517 (2010). https://doi.org/10.1109/TRO.2010.2044948
Blackmore, L., Ono, M., Williams, B.C.: Chance-constrained optimal path planning with obstacles. IEEE Trans. Robot. 27(6), 1080–1094 (2011). https://doi.org/10.1109/TRO.2011.2161160
Buehler, E.A., Paulson, J.A., Mesbah, A.: Lyapunov-based stochastic nonlinear model predictive control: Shaping the state probability distribution functions. In: 2016 American Control Conference (ACC), pp. 5389–5394 . https://doi.org/10.1109/ACC.2016.7526514 (2016)
Cannon, M., Kouvaritakis, B., Ng, D.: Probabilistic tubes in linear stochastic model predictive control. Syst. Contr. Lett 58(10), 747–753 (2009). https://doi.org/10.1016/j.sysconle.2009.08.004
Conceição, A.S., Moreira, A.P., Costa, P.J.: A nonlinear model predictive control strategy for trajectory tracking of a four-wheeled omnidirectional mobile robot. Optim. Contr. Appl. Meth. 29(5), 335–352 (2008). https://doi.org/10.1002/oca.827
Ferreira, Ja.R., Moreira, A.P.G.M.: Non-linear model predictive controller for trajectory tracking of an Omni-directional robot using a simplified model. In: 9th Portuguese Conference on Automatic Control, pp. 57–62. Coimbra, Portugal (2010)
Gray, A., Gao, Y., Lin, T., Hedrick, J.K., Borrelli, F.: Stochastic predictive control for semi-autonomous vehicles with an uncertain driver model. In: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pp. 2329–2334. https://doi.org/10.1109/ITSC.2013.6728575 (2013)
Gu, Y., Seanor, B., Campa, G., Napolitano, M.R., Rowe, L., Gururajan, S., Wan, S.: Design and flight testing evaluation of formation control laws. IEEE Trans. Control Syst. Technol. 108(6), 1105–1112 (2006)
Hafez, A.T., Givigi, S.N., Ghamry, K.A., Yousefi, S.: Multiple cooperative uavs target tracking using learning based model predictive control. In: 2015 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1017–1024. https://doi.org/10.1109/ICUAS.2015.7152391 (2015)
Lima, P.U., Ahmad, A., Dias, A., ao, A.G.C., Moreira, A.P., Silva, E., Almeida, L., Oliveira, L., Nascimento, T.P.: Formation control driven by cooperative object tracking. Robot. Auton. Syst. 63, 68–79 (2015). https://doi.org/10.1016/j.robot.2014.08.018
Liu, C., Gray, A., Lee, C., Hedrick, J.K., Pan, J.: Nonlinear stochastic predictive control with unscented transformation for semi-autonomous vehicles. In: 2014 American Control Conference, pp. 5574–5579. https://doi.org/10.1109/ACC.2014.6859347 (2014)
Mesbah, A.: Stochastic model predictive control: an overview and perspectives for future research. IEEE Control. Syst. 36(6), 30–44 (2016). https://doi.org/10.1109/MCS.2016.2602087
Mesbah, A., Streif, S., Findeisen, R., Braatz, R.D.: Stochastic nonlinear model predictive control with probabilistic constraints. In: 2014 American Control Conference, pp. 2413–2419. https://doi.org/10.1109/ACC.2014.6858851 (2014)
Mohimani, H., Babaie-Zadeh, M., Jutten, C.: A fast approach for overcomplete sparse decomposition based on smoothed L0 norm. IEEE Trans. Signal Process. 57(1), 289–301 (2009). https://doi.org/10.1109/TSP.2008.2007606
Morbidi, F., Mariottini, G.L.: Active target tracking and cooperative localization for teams of aerial vehicles. IEEE Trans. Control Syst. Technol. 21(5), 1694–1707 (2013). https://doi.org/10.1109/TCST.2012.2221092
Nascimento, T.P., Conceição, A.S., Moreira, A.P.: Multi-robot nonlinear model predictive formation control: Moving target and target absence. Robot. Auton. Syst. 61(12), 1502–1515 (2013)
Nascimento, T.P., Moreira, A.P., ao, A.G.S.C., Bonarini, A.: Intelligent state changing applied to multi-robot systems. Robot. Auton. Syst. 61(2), 115–124 (2013). https://doi.org/10.1016/j.robot.2012.10.011
Nascimento, T.P., Conceição, A.S., Moreira, A.P.: Multi-robot nonlinear model predictive formation control: the obstacle avoidance problem. Robotica 34(3), 549–567 (2014). https://doi.org/10.1017/S0263574714001696
Nascimento, T.P., Garcia Gonçalves, L.M., Basso, G., Trabuco Dórea, C.E.: Stochastic nonlinear model predictive mobile robot motion control. In: 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE), pp. 19–25. https://doi.org/10.1109/LARS/SBR/WRE.2018.00014 (2018)
Ostafew, C.J., Schoellig, A.P., Barfoot, T.D.: Robust constrained learning-based nmpc enabling reliable mobile robot path tracking. Int. J. Robot. Res. 1, 1–17 (2016). https://doi.org/10.1177/0278364916645661
Quintero, S.A.P., Copp, D.A., Hespanha, J.P.: Robust uav coordination for target tracking using output-feedback model predictive control with moving horizon estimation. In: 2015 American Control Conference (ACC), pp 3758–3764. https://doi.org/10.1109/ACC.2015.7171914 (2015)
Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the Rprop algorithm. In: IEEE International Conference on Neural Networks, pp. 586–591. San Francisco, CA, USA (1993)
Rostampour, V., Esfahani, P.M., Keviczky, T.: Stochastic nonlinear model predictive control of an uncertain batch polymerization reactor. IFAC-PapersOnLine 48(23), 540–545 (2015). https://doi.org/10.1016/j.ifacol.2015.11.334
Sarunic, P., Evans, R.: Hierarchical model predictive control of uavs performing multitarget-multisensor tracking. IEEE Trans. Aerosp. Electron. Syst. 50(3), 2253–2268 (2014). https://doi.org/10.1109/TAES.2014.120780
Stroupe, A.W., Balch, T.: Value-based action selection for observation with robot teams using probabilistic techniques. Robot. Auton. Syst. 50(2-3), 85–97 (2005)
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)
Weissel, F., Schreiter, T., Huber, M.F., Hanebeck, U.D.: Stochastic model predictive control of time-variant nonlinear systems with imperfect state information. In: 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp 40–46. https://doi.org/10.1109/MFI.2008.4648105 (2008)
Weissel, F., Huber, M.F., Hanebeck, U.D.: Stochastic Nonlinear Model Predictive Control based on Gaussian Mixture Approximations, pp 239–252. Springer, Berlin (2009). https://doi.org/10.1007/978-3-540-85640-5_18
Yang, P., Freeman, R.A., Lynch, K.M.: Distributed cooperative active sensing using consensus filters. In: Proceedings 2007 IEEE International Conference Robotics and Automation, pp. 405-410. Evanston - USA (2007)
Yao, P., Wang, H., Ji, H.: Multi-uavs tracking target in urban environment by model predictive control and improved grey wolf optimizer. Aerosp. Sci. Technol. 55(Supplement C), 131–143 (2016). https://doi.org/10.1016/j.ast.2016.05.016
Zhou, K., Roumeliotis, S.I.: Multirobot active target tracking with combinations of relative observations. IEEE Trans. Robot. 27(4), 678–695 (2011). https://doi.org/10.1109/TRO.2011.2114734
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Nascimento, T.P.d., Dórea, C.E.T. & Gonçalves, L.M.G. Pure Perception Motion Control based on Stochastic Nonlinear Model Predictive Control. J Intell Robot Syst 99, 451–466 (2020). https://doi.org/10.1007/s10846-019-01141-8
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DOI: https://doi.org/10.1007/s10846-019-01141-8