International audience

Industry 4.0, involving technological advances such as the Internet of Things, artificial intelligence, and autonomous robotics, promises to bring great benefits and outstanding innovations to traditional manufacturing. To make this vision a reality, one key challenge consists in harmonizing and adapting these new technologies to the industrial environment. However, there are two major obstacles. First, the technologies, divided into information technologies (IT) and operational technologies (OT), are not interoperable by default, making the IT and OT convergence a complex and uneasy problem. Second, deploying an Industry 4.0 system requires the participation of experts from different domains who have different technical vocabularies and knowledge. Therefore, communication and collaboration between these experts can be complicated, and every misunderstanding may cause the failure of a part or the whole system. Regarding the mentioned obstacles, this paper shares our successful experiences and approaches in an industrial waste management case study of the project OTPaaS. In detail, we have applied standardized modeling approaches, more specifically, the asset administration shell (AAS) digital twins models, for the interoperability between IT and OT involved in the system and used model-driven engineering (MDE) techniques to automate the translation of data models between the different stakeholders in the nroiect.

Motion planning for mobile robots involves defining set-points for their locomotion to reachable destinations, while taking into account the robot's dynamics and its interaction with the environment, which may be cluttered with rough terrain. Although the literature offers a wide range of strategies for this task, it remains difficult to get an overview of all available techniques and to choose the right approach for a given use-case. First, this article focuses on identification and classification of main families of motion planning techniques. Then, based on a vast selection of surveys, reviews, case-studies and articles, and through specifically selected criteria, a comparative study of these methods is proposed for the specific use-case of wheeled robots navigating in complex and uneven terrains.

Robust and accurate path tracking for autonomous vehicle navigation is a complex task, especially when it comes to managing system uncertainties such as inertia, slippage, and action delays. Although model-based controllers are efficient, their performance can be limited by such uncertainties and by the complexity of the gain tuning process. To address this, our study evaluates the effectiveness of four strategies using reinforcement learning (RL) with a controller, to provide either - steering correction, full gain tuning, gain correction, or end-to-end learning without any controller - to improve trajectory tracking. These methods are trained on geometric controllers (Pure Pursuit, Stanley) and model predictive controllers (Romea, EBSF). Our results show that all RL methods improve tracking at high speeds, with steering correction proving the most consistently effective in all cases.

Most navigation approaches treat obstacles as static objects and choose to bypass them. However, the detour could be costly or could lead to failures in indoor environments. The recently developed navigation among movable obstacles (NAMO) methods prefer to remove all the movable obstacles blocking the way, which might be not the best choice when planning and moving obstacles takes a long time. We propose a pipeline where the robot solves the NAMO problems by optimizing the total time to reach the goal. This is achieved by a supervised learning approach that can predict the time of planning and performing obstacle motion before actually doing it if this leads to faster goal reaching. Besides, a pose generator based on reinforcement learning is proposed to decide where the robot can move the obstacle. The method is evaluated in two kinds of simulation environments and the results demonstrate its advantages compared to the classical bypass and obstacle removal strategies.

International audience

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.

International audience

Ensuring a safe online motion planning despite a large number of moving agents is the problem addressed in this paper. Collision avoidance is achieved without communication between the agents and without global localization system. The proposed solution is a modification of the Hybrid Reciprocal Velocity Obstacles (HRVO) combined with a tracking error estimation, in order to adapt the Velocity Obstacle paradigm to agents with kinodynamic constraints and unreliable velocity estimates. This solution, evaluated in simulation and in real test scenario with three dynamic unicycle type robots, shows an improvement over HRVO.

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.

International audience

This paper addresses the problem of reducing the elastic deformations and the residual vibrations of flexible loads when they are handled by a robot manipulator. During the manipulation of the low-stiffness load, such as bumper or exhaust system in automotive industry, large motion-induced deformations and vibrations may be induced. These deformations will have detrimental effects on the settling time, on the accuracy and on the integrity of the operational process in a constrained environment. The trajectory shaping approaches, i.e smoothing filter or input shaping method are well-known solutions for the suppression of the residual vibrations at the end of a rest-to-rest motion. However, using trajectory shaping technique alone may not be sufficient to suppress the static elastic deformations during the transfer phase of the object. Thus, the main contribution of this paper is to propose a two stages feedforward based approach that combines trajectory shaping technique for vibrations reduction, with a deformation compensation trajectory. The latter exploits the rotation space of the manipulator to mitigate the flexural motion of the flexible load. The effectiveness of the proposed method is demonstrated by experimental validations on an industrial robot Kuka iiwa.

This paper addresses the problem of motion planning for a multirobot system in a partially known environment where conditions such as uncertainty about robots' positions and communication delays are real. In particular, we detail the use of a Distributed Receding Horizon Approach that guarantees collision avoidance with static obstacles and between robots communicating with each other. Underlying optimization problems are solved by using a Sequential Least Squares Programming algorithm. Experiments with real nonholonomic mobile platforms are performed. The proposed framework is compared with the Dynamic Window approach to motion planning in a single robot setup. A second experiment shows results for a multirobot case using two robots where collision is avoided even in presence of significant localization uncertainties.

This paper proposes an improvement of a motion planning approach and a modified model predictive control (MPC) for solving the navigation problem of a team of dynamical wheeled mobile robots in the presence of obstacles in a realistic environment. Planning is performed by a distributed receding horizon algorithm where constrained optimization problems are numerically solved for each prediction time-horizon. This approach allows distributed motion planning for a multi-robot system with asynchronous communication while avoiding collisions and minimizing the travel time of each robot. However, the robots dynamics prevents the planned motion to be applied directly to the robots. Using unicycle-like vehicles in a dynamic simulation, we show that deviations from the planned motion caused by the robots dynamics can be overcome by modifying the optimization problem underlying the planning algorithm and by adding an MPC for trajectory tracking. Results also indicate that this approach can be used in systems subjected to real-time constraint.

This paper is focused on the problem of accurate and reliable path tracking control of a 4-wheels car-like mobile robot moving off-road at high speed. Dynamic and extended kinematic models that take into account the effects of wheel skidding are presented. Based on the extended kinematic model, an adaptive and predictive controller for path tracking is derived. This control law is combined to a stabilization algorithm of yaw motion, based on the dynamic model and the modulation of driven wheel forces. The overall control architecture is experimentally evaluated on a slipping terrain. Results demonstrate enhanced performances as the robot succeed in following the path at high speed, accurately and without loss of control.

A robust dynamic feedback controller is designed and implemented, based on the dynamic model of the six-wheel skid-steering RobuROC6 robot, performing high speed turns. The control inputs are respectively the linear velocity and the yaw angle. The main object of this paper is to elaborate a sliding mode controller, proved to be robust enough to ignore the knowledge of the forces within the wheel-soil interaction, in the presence of sliding phenomena and ground level fluctuations. Finally, a 3D simulation is performed with an accurate physical engine to evaluate the efficiency of this designed control law.

This paper deals with design and implementation of a stabilization algorithm for a car like robot performing high speed turns. The control of such a kind of system is rather difficult because of the complexity of the physical wheel- soil interaction model. In this paper, it is planned to analyze the complex dynamic model of this process to elaborate a stabilization algorithm only based on the measurement of the system yaw rate. Finally, a 3D simulation is performed to evaluate the efficiency of this designed stabilization algorithm.