Adaptive and predictive path tracking control for off-road mobile robots

International audienceA major problem in the design of control laws dedicated to mobile robots appears when the classical hypothesis of rolling without sliding wheels is violated. It is generally the case for off-road vehicles as adherence conditions are often not satisfactory and sliding can then cease to be negligible. Consequently theoretical performance is impaired and the vehicle is no longer accurately controlled. It is particularly harmful with respect to path tracking tasks, where a loss of accuracy in rough terrain can generate a hazardous situation. Previous work based on the assumption of rolling without sliding has shown very satisfactory results with respect to that task when sliding is not preponderant. It has also made it possible to pinpoint and study the effects of sliding when it appears to be non-negligible. To preserve path tracking accuracy with respect to this phenomenon, a new control law based on an extended kinematic model (updated on-line via an adaptive method) is proposed and discussed. Such control is very efficient when adherence conditions are constant, but overshoots can appear when an abrupt variation is recorded (which is especially the case at the beginning/end of curves due to low level delays and inertial effects). A Model Predictive Control approach is then added to limit such transient phases in cases where a curved path is followed. The paper is organized as follows: the extended kinematic model is presented as well as the observation of unmeasured parameters required to feed it. A non-linear control law can then be designed and the results obtained are discussed. Finally, the Model Predictive Control approach is integrated and the overall control scheme is presented. The capabilities of the approach described in this paper are then discussed through full scale experiments

Multiple-Model Robust Adaptive Vehicle Motion Control

An improvement in active safety control systems has become necessary to assist drivers in unfavorable driving conditions. In these conditions, the dynamic of the vehicle shows rather different respond to driver command. Since available sensor technologies and estimation methods are insufficient, uncertain nonlinear tire characteristics and road condition may not be correctly figured out. Thus, the controller cannot provide the appropriate feedback input to vehicle, which may result in deterioration of controller performance and even in loss of vehicle control. These problems have led many researchers to new active vehicle stability controllers which make vehicle robust against critical driving conditions like harsh maneuvers in which tires show uncertain nonlinear behaviour and/or the tire-road friction coefficient is uncertain and low. In this research, the studied vehicle has active front steering system for driver steer correction and in-wheel electric motors in all wheels to generate torque vector at vehicle center of gravity. To address robustness against uncertain nonlinear characteristics of tire and road condition, new blending based multiple-model adaptive schemes utilizing gradient and recursive least squares (RLS) methods are proposed for a faster system identification. To this end, the uncertain nonlinear dynamics of vehicle motion is addressed as a multiple-input multiple-output (MIMO) linear system with polytopic parameter uncertainties. These polytopic uncertainties denote uncertain variation in tire longitudinal and lateral force capacity due to nonlinear tire characteristics and road condition. In the proposed multiple-model approach, a set of fixed linear parametric identifi cation models are designed in advance, based on the known bounds of polytopic parameter set. The proposed adaptive schemes continuously generates a weighting vector for blending the identifi cation model to achieve the true model (operation condition) of the vehicle. Furthermore, the proposed adaptive schemes are generalized for MIMO systems with polytopic parameter uncertainties. The asymptotic stability of the proposed adaptive identifi cation schemes for linear MIMO systems is studied in detail. Later, the proposed blending based adaptive identi fication schemes are used to develop Linear Quadratic (LQ) based multiple-model adaptive control (MMAC) scheme for MIMO systems with polytopic parameter uncertainties. To this end, for each identi fication model, an optimal LQ controller is computed on-line for the corresponding model in advance, which saves computation power during operation. The generated control inputs from the set of LQ controllers is being blended on-line using weighting vector continuously updated by the proposed adaptive identifi cation schemes. The stability analysis of the proposed LQ based optimal MMAC scheme is provided. The developed LQ based optimal MMAC scheme has been applied to motion control of the vehicle. The simulation application to uncertain lateral single-track vehicle dynamics is presented in Simulink environment. The performances of the proposed LQ based MMAC utilizing RLS and gradient based methods have been compared to each other and an LQ controller which is designed using the same performance matrices and fixed nominal values of the uncertain parameters. The results validated the stability and effectiveness of the proposed LQ based MMAC algorithm and demonstrate that the proposed adaptive LQ control schemes outperform over the LQ control scheme for tracking tasks. In the next step, we addressed the constraints on actuation systems for a model predictive control (MPC) based MMAC design. To determine the constraints on torque vectoring at vehicle center of gravity (CG), we have used the min/max values of torque and torque rate at each corner, and the vehicle kinematic structure information. The MPC problem has been redefi ned as a constrained quadratic programming (QP) problem which is solved in real-time via interior-point algorithm by an embedded QP solver using MATLAB each time step. The solution of the designed MPC based MMAC provides total steering angle and desired torque vector at vehicle CG which is optimally distributed to each corner based on holistic corner control (HCC) principle. For validation of the designed MPC based MMAC scheme, several critical driving scenarios has been simulated using a high- fidelity vehicle simulation environment CarSim/Simulink. The performance of the proposed MPC based MMAC has been compared to an MPC controller which is designed for a wet road condition using the same tuning parameters in objective function design. The results validated the stability and effectiveness of the proposed MPC based MMAC algorithm and demonstrate that the proposed adaptive control scheme outperform over an MPC controller with fixed parameter values for tracking tasks

Discussion on: "Adaptive and Predictive Path Tracking Control for Off-road Mobile Robots".

International audienc

Discussion on: "Adaptive and Predictive Path Tracking Control for Off-road Mobile Robots".

International audienc

Autonomous Tracked Robots. History, Modelling, Localization, and Motion Control

[ES] Uno de los campos de aplicación más significativos de la robótica móvil consiste en robots capaces de operar en condiciones exteriores sobre terrenos no preparados (robots planetarios, robots en agricultura, robot en operaciones de búsqueda y rescate, robots militares, etc.). Sin embargo, conseguir que los robots se muevan de forma eficiente y precisa en este tipo de entornos no es una tarea sencilla. Un primer aspecto crítico es el sistema de locomoción. En este caso, las orugas constituyen una alternativa sólida a otro tipo de sistemas y desde principios del siglo XX han demostrado sus bondades en vehículos tripulados. En este artículo se motiva y se demuestra mediante pruebas reales la idoneidad de este tipo de locomoción para robots móviles en terrenos no preparados. Es importante remarcar que este artículo pretende ser un resumen extendido del libro recientemente publicado por los autores “Autonomous Tracked Robots in Planar Off-Road Conditions” (González et al., 2014), y, por lo tanto, no pretende ser una contribución original. Inicialmente se presenta una perspectiva histórica de los vehículos y los robots con orugas. Posteriormente se discuten los aspectos de modelado con especial mención al fenomeno del deslizamiento. A continuación, se analizan varias estrategias de localización, en particular, la odometria visual. También se analiza el aspecto del control de navegación, para ello se analizan varias estrategias con compensación del deslizamiento. Finalmente se expresan las conclusiones del trabajo en base a la experiencia de los autores en este campo.[EN] One of the most significant research field in mobile robotics deals with robots operating in off-road conditions (planetary rovers, agriculture robots, search and rescue operations, military robots, etc.). However, obtaining a successful result is not an easy task. One primary point is the locomotion system. In this case, tracks constitute a well-known approach and since the beginning of the 20th century this locomotion system has demonstrated remarkable results in manned vehicles. This article motivates and shows through physical experiments the goodness of tracked mobile robots in off-road conditions. Firstly, a historical perspective of tracked vehicles and tracked robots is addressed. Then, the main modelling aspects are introduced, in particular, the slip phenomenon. After that, several localization techniques are discussed with especial mention to visual odometry. 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