During the off-road path following of a wheeled mobile robot in presence of poor grip conditions, the longitudinal velocity should be limited in order to maintain safe navigation with limited tracking errors, while at the same time being high enough to minimize travel time. Thus, this paper presents a new approach of online speed fluctuation, capable of limiting the lateral error below a given threshold, while maximizing the longitudinal velocity. This is accomplished using a neural network trained with a reinforcement learning method. This speed modulation is done side-by-side with an existing model-based predictive steering control, using a state estimator and dynamic observers. Simulated and experimental results show a decrease in tracking error, while maintaining a consistent travel time when compared to a classical constant speed method and to a kinematic speed fluctuation method.

The adoption and deployment of the Internet of Things (IoT) technologies in agroecology raise a challenging research agenda. Agroecology IoT projects feature complex requirements involving: heterogeneous hardware and heterogeneous software systems; data collection architectures, stream and queuing systems as well as data management systems for real-time and batch processing with different data models. On top of that, agroecology IoT applications are characterized by complex spatio-temporal data and low quality communication networks.

In the last few years, fleets of mobile robots have received increased interest in agriculture with the development of master/slaves control approaches. This paper proposes on the contrary a planning strategy enabling to generate beforehand the trajectory of each robot. For that, the fleet is considered as a single mobile entity with its steering and speed constraints. An admissible trajectory for this virtual entity, including maneuver phases, is generated to cover the shape of a given field. This one is next used to plan the trajectories of the actual robots. A panel of actual fields with different fleets of robots enables to highlight the relevance of the strategy proposed.

This paper proposes a new approach for online control law gains adaptation, through the use of neural networks and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm, in order to optimize the behavior of the robot with respect to an objective function. The neural network considered takes as input the current observed state as well as its uncertainty, and provides as output the control law gains. It is trained, using the CMA-ES algorithm, on a simulator reproducing the vehicle dynamics. Then, it is tested in real conditions on an agricultural mobile robot at different speeds. The transferability of this method from simulation to a real system is demonstrated, as well as its robustness to environmental changes, such as GPS signal degradation or ground variation. As a result, path following errors are reduced, while ensuring tracking stability.

In this paper, we address the problem of localizing mobile robots based on range-only measurements from low cost Ultra-Wide-Band (UWB) sensors. The proposed solution considers every static or mobile object as beacons with contextual information. A beacon-to-beacon measurement is performed using UWB sensors and the position estimation is computed by the target beacon. This strategy allows to hide the cooperative localization problem behind these measurements. The fusion algorithm is based on a Split Intersection Covariance Filter which allows to correctly handle the correlation between the poses estimations of the beacons. We present the consistency of this solution using a simulation with 3 robots and 4 static beacons and a real experimentation with 1 robot and 3 static beacons.

Many agricultural tasks are known to be dangerous for human operators, the environment, and human health in general. The increasing pressure both on safety and on production levels motivates the development of new methodologies and technologies. The rising of off-road mobile robots for agricultural application appears to be a promising contribution to required innovations. It both permits to limit the exposure of people to hazardous products and to achieve difficult and repetitive tasks. Nevertheless, to be fully efficient, autonomous robots have to ensure a high level of accuracy, while carrying potentially heavy tools, possibly in harsh conditions. It is especially the case of spraying, for which accuracy is a key challenge for reducing environmental impacts. The use of huge robots for spraying might seem to be a straightforward solution, by simply automating existing machines. Nevertheless, a simple automation does not reduce directly the environmental impact of human activities (soil compaction, energy, reduction of the use of chemical products). Moreover, huge machines are not necessarily an advantage when considering safety aspects (rollover risk and maneuverability). As a result, a solution based on the cooperation of at least two mobile robots, moving from either side of a vine row, is investigated in this paper thanks to Ultra Wide Band (UWB) technology.

We propose a real time method for moving target detection from a camera embedded on a UAV. As the camera is moving, we must estimate the background motion in order to compensate it and then perform the moving target detection. This compensation is realized by an image registration method. For this, we use an hybrid method using global minimization and feature-based approaches, with a pyramidal implementation. The good results obtained for registration give us the potential moving targets. As some wrong detections still appear, due to noise, occlusions or local change of illuminations, we worked on a robust spatio-temporal tracker able to decide if potential targets are real moving targets or not. The algorithm must reach real time performances for VGA images at 30 fps with a standard PC.We have tested our method on different sequences and show the good results obtained thanks to the high precision in the image registration and the spatio-temporal tracker.

In this paper, a real time method for image registration dedicated to small moving target detection (MTD) from a moving camera is proposed. The main application is the road surveillance from UAV. The algorithm must work under strong constraints: real time performances for VGA images at 30 fps with a standard PC and shaking camera with irregular movements. Given the medium-altitude of the UAV, the estimated transformation is a similarity. Our proposed algorithm is an hybrid method using an ESM algorithm (Efficient Second-Order Minimization) and Harris corners matching, with pyramidal implementation. An M-Estimator is used at each loop of the ESM in order to be robust to moving objects, occlusions and camera noise. We have tested our method on UAV highway images and this coupling allows for robustness, low time computation and accuracy. Even if the camera may have important oscillations, we get a high precision alignment of the images and thus a reliable detection of the moving targets.