In this paper, we present a novel method to control a rigidly connected location on the vehicle, such as a point on the implement in case of agricultural tasks. Agricultural robots are transforming modern farming by enabling precise and efficient operations, replacing humans in arduous tasks while reducing the use of chemicals. Traditionally, path-following algorithms are designed to guide the vehicle's center along a predefined trajectory. However, since the actual agronomic task is performed by the implement, it is essential to control a specific point on the implement itself rather than the vehicle's center. As such, we present in this paper two approaches for achieving the control of an offset point on the robot. The first approach adapts existing control laws, initially intended for the rear axle's midpoint, to manage the desired lateral deviation. The second approach employs backstepping control techniques to create a control law that directly targets the implement. We conduct realworld experiments, highlighting the limitations of traditional approaches for offset point control, and demonstrating the strengths and weaknesses of the proposed methods.

In this paper, the problems associated with accurate path tracking control in off-road conditions is addressed with model-based adaptive control. In particular, the estimation of grip conditions is investigated through the derivation of a new observer and by gathering kinematic and dynamic models into a single framework. This new reference point employs a unique observer regardless of the velocity of the robots. Previous approaches necessitated the switching of models depending upon the phenomena encountered as well as robot dynamics. The observer proposed here allows an accurate and reactive estimation of sliding. This permits to feed relevantly a control law based on an extended kinematic model, enabling accurate path tracking, even in harsh conditions and when facing significant dynamic effects such as spin around.

One of the most important points in path tracking applications, is the capability of the robot to track the desired path as accurately as possible. Results presented in the literature in on-road contexts are convincing, but the case of off-road mobile robots introduces other issues, like bad grip conditions or non-flat ground. These issues can lead to a lack of accuracy of the tracking, even more when we consider high speed. The quality of the tracking depends on the modelling and the control laws used, but it depends also on the path to follow and on the soil conditions. If some small errors may be acceptable, it is sometimes mandatory to have a limited error, in particular when avoiding an obstacle or when moving in a narrow pathway. The aim of the algorithm proposed in this paper is to determine the maximum velocity of the robot during the tracking, for the lateral error to stay below a desired limit. In order to achieve this, a predictive approach taking into account for the evolution of a dynamic model, control laws and actuators properties is proposed. It permits to predict the forthcoming tracking error and consequently to estimate the maximum velocity that the robot can reach in accordance with the maximum deviation allowed.

The design and the evaluation of an autonomous interactive robot is a challenging research endeavor because there is as much to learn from the interaction between the integrated technologies as there is from the embodied human-robot interaction, in addition to observing their mutual interdependencies. This paper reports on IRL-0, a prototyping platform that we used to conduct preliminary studies on the influences of combining verbal and nonverbal modalities (facial expressions, head movement, arm gestures, and approach trajectory) for engaging interaction with people in controlled conditions and in real-world settings. IRL-0 is made of a compliant omnidirectional mobile base equipped with an expressive face and a three degrees-of-freedom (DOFs) compliant arm. By assembling this prototype and conducting these preliminary studies, our objective is to acquire insights in terms of design (e.g., technology, control) and experimental procedures that are important to take into consideration for the designing and evaluating autonomous robots engaging interaction with people.