In this paper, the problem of tracking desired longitudinal and lateral motions for a vehicle is addressed here. Let us point out that a “good” modeling is often quite difficult or even impossible to obtain. It is due for example to parametric uncertainties, for the vehicle mass, inertia or for the interaction forces between the wheels and the road pavement. To overcome this type of difficulties, we consider a model-free control approach leading to “intelligent” controllers. The longitudinal and the lateral motions, on one hand, and the driving/braking torques and the steering wheel angle, on the other hand, are respectively the output and the input variables. An important part of this paper is dedicated to present simulation results with actual data. Actual data, used in MATLAB as reference trajectories, have been previously recorded with an instrumented Peugeot 406 experimental car. The simulation results show the efficiency of our approach. Some comparisons with a nonlinear flatness-based control in one hand, and with a classical PID control in another hand confirm this analysis. Other virtual data have been generated through the interconnected platform SiVIC/RTMaps, which is a virtual simulation platform for prototyping and validation of advanced driving assistance systems.

Most autonomous driving architectures separate planning and control phases in different layers, even though both problems are intrinsically related. Due to limitations on the available computational power, their levels of abstraction and modeling differ; in particular, vehicle dynamics are often highly simplified at the planning phase, which may lead to inconsistency between the two layers. In this paper, we study the kinematic bicycle model, which is often used for trajectory planning, and compare its results to a 9 degrees of freedom model. Modeling errors and limitations of the kinematic bicycle model are highlighted. Lastly, this paper proposes a simple and efficient consistency criterion in order to validate the use of this model for planning purposes.

Nowadays a great interest focuses on UAV and particularly on quadrotor platforms. Its mechanical simplicity and stability in flight has contributed to its commercial success. However the energy consumption remains a major technological limitation and its usual flight autonomy is only 15 to 20 minutes. More and more platforms deriving from that concept come out. For example terrestrial quadrotors have already appeared on the market. They can move on the ground to save energy and fly to avoid obstacles. It is proposed in this paper to study control laws for one particular design of terrestrial quadrotor. A terrestrial dynamic model of the system is first presented and then validated by an identification process relying on results recorded by an experimental platform also described in the present document. In addition, some first experimental results of xy-plane trajectory tracking control laws based on a flatness approach are given and commented.

Over the past decade, a lot of researches have been done on the development of advanced driver assistance systems (ADAS). Most of these ADAS are now active and need to be tested and evaluated before large deployment. In these ADAS, the prototyping and the implementation of the control stages are risky stages and not so easy to carry out. Indeed, the prototyping and the test of such reactive algorithms need heavy hardware and software supports (dedicated vehicle, actuators, hardware architecture, software architecture, sensors). To achieve such active devices, additional developments and implementation of numerous expensive embedded devices are required. Therefore, in order to reduce both time and risk, in early design stage, it becomes necessary to have a very realistic simulation environment dedicated to the development and to the evaluation of these ADAS. For such virtual platform, it is mandatory to provide physics-driven road environments, virtual embedded sensors, and physics-based vehicle models. In this publication, we present a dedicated couple of platforms with their efficient interconnection for the prototyping of such ADAS. Initially, the SiVIC simulation platform has been developed to generate the virtual world (environments, sensors, actuators, vehicles). In order to improve the real time prototyping capabilities of SiVIC, an efficient interconnection of this first platform has been done with RTMaps platform. This second one is mainly dedicated to the multi-sensors data processing (data management, fusion, flow recording and replaying). In this paper we will show the interest of such bi-directionnal interconnected platforms to prototype complex and real time embedded ADAS. This interconnection can be done not only on one computer but also on a distributed and distant computers architecture. The relevance of this approach will be illustrated with an automatic parking application.

It is obvious to say that perception is necessary to drive. Futhermore we can say that a good visibility is a guaranty for passengers security. The driver will adapt the vehicle speed to the offered visibility. Strong visibility reductions (dense fog for instance) are conditions of risk of accident. We therefore propose here a method to perform an onboard estimation of the visibility distance. Once this estimate is obtained, assistance could be offered to the driver (eg if it runs at a speed not adapted to the current visibility) or to the road infrastructure management so that it can inform other users of risk on his road network. This method uses images acquired by an onboard camera filming the scene and the estimation of vehicle motion. Thus, from this information we will explain how we can achieve a spatial partial structure reconstruction to estimate the visibility distance.

The presence of an area with low visibility conditions is a relevant information for autonomous vehicle as far as environment sensing is important regarding safety. In this aim, we develop a generic sensor of visibility using an onboard camera in a vehicle. Our approach consists in estimating the range to the most distant object belonging to the plane of the road having at least 5% of contrast. The originality of this approach lies in the fact that the depth map of the vehicle environment is obtained by aligning the road plane in successive images. This algorithm exploits the dynamics of the vehicle which is given or observed from proprioceptive sensors classically available on public vehicles. In this paper, we present the principle of our approach in terms of image processing and explain how the vehicle dynamics takes part in it with a sensitivity study.

This paper concerns the joystick control of the the high performance econo vehicle developed by Mitsubishi Heavy Industries featuring 2 independent fully actuated steering wheels. For this purpose, we propose a velocity control law based on the assumption of pure rolling without slipping wheel/road contact and the techniques of dynamic feedback linearization. It allows the robot's joystick control along 3 independent degrees of freedom: the longitudinal and lateral directions in the horizontal plane and the angular orientation about the vertical axis. Experiments on an asphalt road were performed at small speed which proved to be successful.

The structure of the kinematic and dynamic models of wheeled mobile robots is analyzed. It is shown that, for a large class of possible configurations, they can be classified into five types, characterized by generic structures of the model equations. For each type of model the following questions are addressed: (ir)reducibility and (non)holonomy, mobility and controllability, configuration of the motorization, and feedback equivalence.

This paper deals with state feedback control of nonholonomic systems whose configuration space presents some singular points. The whole study is driven on the example of mobile robots equipped with several steering wheels. Linearizing dynamic feedback and time-varying feedback are successively considered to handle the tracking of a moving reference trajectory and the stabilization of the robots to a rest configuration, respectively. Both laws are designed in such a way that the systems singular configurations cannot definitely be met. Finally, a commutation procedure between the two laws is presented, in order to handle the very natural problem of tracking a reference trajectory ended by a rest configuration.

The authors propose an indirect adaptive scheme for incompletely controlled mechanical systems such as overhead cranes or, more generally, classical rigid manipulators ended by a simple pendulum. In the case of known parameters a dynamic state feedback produces full linearization. An adaptive version is obtained by a simple estimation method together with a certainty equivalence law, taking advantage of the particular structure of the equations. Global stability is discussed, and simulation results for the overhead crane are reported.>

Smooth time-varying laws can solve the stabilization problem of nonholonomic mechanical systems. The authors show that by means of dynamic state feedback, it is possible for three-wheeled mobile robots to track arbitrary fast trajectories not reduced to equilibrium points. Dynamical modeling of nonholonomic mechanical systems for the case of three-wheeled mobile robots is considered. Dynamic feedback allows solution of the tracking problem for an omnidirectional mobile robot with less motors than degrees of freedom. This is possible by choosing output functions depending on the mass repartition of the robot.>

A general dynamical model is derived for three-wheel mobile robots with nonholonomic constraints by using a Lagrange formulation and differential geometry. It is shown that a static state feedback allows one to reduce the dynamics of the system to a form in which stabilizing input-output linearizing control is possible.>