The control of field robots in varying and uncertain terrain conditions presents a challenge for autonomous navigation. Online estimation of the wheel-terrain slip characteristics is essential for generating the accurate control predictions necessary for tracking trajectories in off-road environments. Receding horizon estimation (RHE) provides a powerful framework for constrained estimation, and when combined with receding horizon control (RHC), yields an adaptive optimisation-based control method. Presently, such methods assume slip to be constant over the estimation horizon, while our proposed structured blocking approach relaxes this assumption, resulting in improved state and parameter estimation. We demonstrate and compare the performance of this method in simulation, and propose an overlapping-block strategy to ameliorate some of the limitations encountered in applying noise-blocking in a receding horizon estimation and control (RHEC) context.
Achieving robust control of wheeled mobile robot (WMR) systems in the presence of modelling errors and external disturbances is a long-standing challenge in robotics. Early works in this area study control in the presence of bounded additive disturbances @cite_14 - @cite_4 , and while these techniques can be specialised for scenarios with slip, deriving the bounds for the uncertainties presents a challenge.
@cite_14 @cite_4
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This paper addresses the path following control problem of wheeled mobile robots in the presence of wheel skidding and slipping. A conditional global positioning system (GPS)-based path following controller is proposed for four generic WMRs to achieve high-precision path following performance. The proposed controller utilizes real-time kinematic-GPS and other aiding sensors to determine the robot's pose and wheel skidding and slipping perturbations to achieve accurate path following control. The reported simulation and experimental results suggest that with this control scheme, the WMRs are able to maneuver with precision. © 2009 Wiley Periodicals, Inc. This paper aims to give a general and unifying presentation on modeling of wheel mobile robots (WMRs) in the presence of wheel skidding and slipping from the perspective of control design. We present kinematic models that explicitly relate perturbations to the vehicle skidding and slipping. Four configurations of mobile robots are considered, and perturbations due to skidding and slipping are categorically classified as input-additive, input multiplicative, and or matched unmatched perturbations. Furthermore, we relate the WMR's maneuverability with the vehicle controllability that provides a measure on the WMR ability to track a trajectory in the presence of wheel skidding and slipping. These classifications and formulations lay a base for the deployments of various control design techniques to overcome the addressed perturbations. This paper focuses on the design of a tube-based Model Predictive Control law for the control of constrained mobile robots in off-road conditions with longitudinal slip while ensuring robustness and stability. A time-varying trajectory tracking error model is used, where uncertainties are assumed to be bounded and additive. The robust tube-based MPC is compared with other motion control techniques through simulation and physical experiments. These tests show the satisfactory behavior of the presented control strategy. Robotic systems need to be able to plan control actions that are robust to the inherent uncertainty in the real world. This uncertainty arises due to uncertain state estimation, disturbances, and modeling errors, as well as stochastic mode transitions such as component failures. Chance-constrained control takes into account uncertainty to ensure that the probability of failure, due to collision with obstacles, for example, is below a given threshold. In this paper, we present a novel method for chance-constrained predictive stochastic control of dynamic systems. The method approximates the distribution of the system state using a finite number of particles. By expressing these particles in terms of the control variables, we are able to approximate the original stochastic control problem as a deterministic one; furthermore, the approximation becomes exact as the number of particles tends to infinity. This method applies to arbitrary noise distributions, and for systems with linear or jump Markov linear dynamics, we show that the approximate problem can be solved using efficient mixed-integer linear-programming techniques. We also introduce an important weighting extension that enables the method to deal with low-probability mode transitions such as failures. We demonstrate in simulation that the new method is able to control an aircraft in turbulence and can control a ground vehicle while being robust to brake failures.