This work addresses the integration of simultaneous obstacle avoidance for an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) operating cooperatively to rendezvous at a predefined location. A distributed consensus-based architecture is proposed to guide the vehicles toward their designated rendezvous point. Additionally, a virtual force-based obstacle avoidance method is employed for both vehicles. A comparison is conducted with an existing control approach from the literature extended to incorporate obstacle avoidance. Simulation results are provided showing the ability of the presented controllers to achieve rendezvous while simultaneously avoiding obstacles.

In this paper, a centralized control strategy is proposed to perform the formation tracking (FT) of a multi-robot fleet, without using absolute position information. The core of the approach lies in the predictive regulation of inter-agent distances using model predictive control (MPC) to improve robustness against deformations. The proposed strategy allows the formations to be efficiently maintained around the moving leader, even in GPS-denied conditions. To evaluate the advantages of using a distance-based formalism for local formation tracking, the proposed strategy is then compared with another approach inspired by the literature, in which robots maintain positions with respect to the fleet centroid. Simulation results show the efficiency of the proposed MPC framework in maintaining a local formation around the leader and highlight the benefits of using the distance-based formalism in constrained settings. To conclude the study, further discussions are made about the specification of each formalism.

In this paper, we present an improved methodology for scheduling electric vehicle (EV) charging at a single charging station, taking into account vehicle arrival and departure times, as well as drivers' charging requirements. The objective is to minimize the difference between desired and final state-of-charge levels while adhering to constraints on power capacity and charger availability. As electric vehicles increasingly contribute to mitigating greenhouse gas emissions, efficient charging strategies become crucial to manage their impact on the electrical grid. Our study delves into the complex task of scheduling EV charging at public stations, where drivers pre-communicate their charging needs. This study proposes improved solution methods, including a standalone mathematical programming model for scheduling, a hybrid heuristic algorithm for assignment, which combines mathematical modeling techniques for evaluation and energy allocation, and a hybrid tabu search algorithm, which uses the same mathematical model employed in the heuristic. Our results demonstrate the effectiveness of our approach in tackling the challenges of EV charging scheduling, highlighting its relevance for sustainable energy management.

This study delves into the electric vehicle charging scheduling problem within a public charging service station. The scheduling task entails managing charging demands from drivers, including arrival and departure times, current battery state-of-charge, and desired state-of-charge at departure. The scheduler must determine whether to accept or reject charging requests based on charger availability and the maximum grid capacity of the station. The primary objective is to minimize the cumulative discrepancy between the desired and final state-of-charge levels for all electric vehicles. To address this challenge, we introduce a novel architecture that harnesses the capabilities of population-based meta-heuristics as a promising method for identifying near-optimal solutions.

In this article, a novel Control Allocation (CA) approach based on Daisy Chaining and Kalman Filter CA (DCKFCA) approaches is presented. The proposed algorithm aims at overcoming the most common limitations of the existing algorithms: compensation of the different actuator dynamics and switching between different groups of actuators. These two limitations impact negatively the performance of the overall system and the closed loop stability. Daisy Chaining rearranges the actuators into groups, and then the CA problem is solved using the Kalman Filter. This approach has already shown promising results on a realistic simulator of the longitudinal control of an autonomous vehicle.

Increasing focus is being paid to ensuring safety in autonomous driving. The current paper addresses the challenge of collision avoidance with dynamic surrounding vehicles in different driving situations. The established solution formulated utilizing Model Predictive Control (MPC) includes decision making and trajectory planning. A simplified prediction model is used, which takes into account the relative positions and velocities of the surrounding vehicles and the ego vehicle. Depending on traffic conditions, which are stated as constraints in the MPC formulation, the ego vehicle may perform lane keeping, lane shift, overtaking or braking to avoid collision with the road participants. The decision making constraints are included into the MPC in a mixed integer formulation-like manner. The safety constraints are defined using the Sigmoid function and the braking barrier to define the navigable zone of the ego vehicle. The proposed algorithm has been evaluated through simulation, with different scenarios revealing its effectiveness.

This paper introduces a vehicle guidance control architecture capable of autonomously resolving emergency situations due to a steering system failure. This situation requires a safe stop in the emergency lane by means of differential braking. The proposed approach is based on a three-level hierarchical architecture composed, from the highest to the lowest, by a reference generation, a guidance control, and a control allocation level. The reference generation function computes the trajectory and the speed profile to be tracked by the vehicle according to the active mode of operation: normal or emergency. Switching mode information are received by the fault detection and isolation (FDI) supervisor. The guidance control function generates the steering angle and the braking/accelerating wheels’ torques commands based on longitudinal and lateral tracking errors. At the lowest level of hierarchy, the control allocation function dynamically redistributes the control commands to the available set of actuators, according to FDI information. For instance, in the proposed study, promoting differential braking in case of a steering system failure, guaranteeing acceptable tracking performance both in longitudinal and lateral directions. Simulation results prove the efficacy of the proposed approach.

The Li-ion batteries are commonly used for Electric Vehicles (EVs) and aerospace applications. One of the essential parameters in Li-ion batteries is state of charge (SOC) that shows the available energy in a battery. Various methods were proposed for SOC estimation. Since the battery has a nonlinear equations, it is important to use a method that does not require the system model. In the present study, a new Adaptive Online Gated Recurrent Unit (GRU) method is proposed for the State of Charge (SOC) estimation. It is a kind of deep Recurrent Neural Network(RNN) which solved the vanishing gradient problem in RNNs with GRU units. For Optimization a robust adaptive Online gradient learning method is used. This method is able to tune online the learning rate in the process. Adaptive GRU is a nondependent method from the nonlinear batteries model and simplifies the mathematical computation. The proposed technique is implemented on the real dataset of LifePO4 Li-ion batteries for finding SOC estimation. The exprimental result indicate that the Adaptive GRU method is more accurate than simple RNN.

Obstacle avoidance requires three main levels in autonomous vehicles, namely, perception, path planning and guidance control. In this paper, a global architecture is proposed by taking into account the link between the three levels. On the environment perception level, an evidential occupancy-grid-based approach is used for dynamic obstacle detection. The poses of objects are therefore considered for trajectory generation. The latter is based on a smooth trajectory sigmoid function. Finally, the control guidance employs this obstacle avoidance trajectory to generate the appropriate steering angle. The whole strategy is validated on our experimental test car. The experimental results show the effectiveness of the proposed approach.

The autonomous vehicle guidance needs a steering system which is able to handle the lateral dynamics and a throttle/braking system to handle the longitudinal dynamics. However, a failure in the steering system leads the vehicle in dangerous situation. In order to manage this situation, an emergency guidance control architecture aims to guide and stop the vehicle in a safe area is proposed here. To that end, an emergency guidance controller (EGC) is developed to ensure the lateral guidance as well as the longitudinal guidance using braking torques. Since the same actuators (brakes) are employed for both control objectives a managing mechanism is proposed. Finally, simulation tests are carried out to show the effectiveness of the proposed approach.

Due to their lack of assessment mechanisms of link quality in VANET environments, routing protocols do not deal efficiently with highly volatile links. One way to fill this gap would be to anticipate links breakages with new route computation. Currently available link quality indicators are not sufficiently responsive to consider forecasting. In this paper we present a novel predictive link quality indicator that is based on the OFDM decoding steps into the PHY layer. The events generated by these steps are threated by a data fusion algorithms. The resulting link quality indicator presents interesting forecasting characteristics and is suitable for a cross-layer usage in routing protocols.

This paper focuses on the development of an IMU measurement simulator for navigation estimation algorithms validation. Its aim is to generate the sensor measurements thanks to an input trajectory described by the position and the orientation. The proposed models are derived from an inverse kinematic modeling of the sensors and an identification of their stochastic errors. These latter are composed of the biases instability, random walks and finally the sensors dynamics and bandwidth. The error model parameters of a low cost MEMS-IMU are determined using the Allan Variance method. In a second step, a Matlab simulator is built gathering the aforementioned models. Thanks to their completeness, this simulation tool is characterized by its wide range of application fields and dynamics that can be described. Its aim is to determine, from the time-dependent position and orientation data, the IMU measurements (3D accelerations and angular rates) without any object model. Finally, the simulator is validated using real experiments performed with an instrumented test car in normal driving as well as in obstacle avoidance situations.

Mobile Ad-hoc Networks (MANET) and more specifically their vehicular variant (VANET) have to deal with fast changing channel conditions, specifically in urban areas. Routing protocols that have to build end to end paths over such volatile links typically react to link failure. In this paper we present a novel PHY layer based link state indicator which aims to predict such failure. The proposed link state indicator is related to the IEEE 802.11 standard and relies on the OFDM decoding process. PhySimWifi is a detailed and accurate implementation of the OFDM-based IEEE 802.11 standard that provide access to all the steps of a packet reception and incorporates realistic channel models. When using it in the ns-3 simulator, the received packets decoding errors / success gives us the material to compute our predictive estimator. This new link state indicator is entirely based on the PHY level. The efficiency of the proposed indicator is validated by reference to PHY and NET packet reception ratio of the monitored link. The efficiency of the proposed new indicator is validated by comparing it with an Signal to Noise Ratio based predictive link state estimator.

This paper describes a new strategy to assess a priori driving risk. The originality lies in the simultaneous consideration of the information related to the Vehicle, the Driver and the Environment (VDE). The heterogeneity and the imperfections of the information are taken into account thanks to the belief functions. A first fusion level determines the local risks related to the VDE entities, while their combination allows to determine the global a priori risk of the current driving situation. Both fusion levels are processed considering three combination rules to manage the eventual conflict. Comparative simulation results help to show the validity and the coherency of this multi-level risk assessment.

This paper describes a vehicle guidance strategy with a focus placed on the reference generation and the control levels. Further to a perception step, performed through the fusion of a Geographic Information System (GIS) and a vision system, the reference generation leads to the computation of a constrained smooth trajectory and a smooth speed profile integrating safety and comfort criteria. The obtained reference set is then used by a longitudinal and NLMPC-based lateral controller providing the steering angle and traction torque. The complete system performance are presented through simulation results based on real-time measurements.

International audience

This paper presents a new constrained trajectory generation method dedicated to Advanced Driver Assistance Systems (ADAS). Based on the information provided by the digital map database of a navigation system, and considering different constraints related to the road profile, the vehicle and the driver, a convex optimization algorithm generates specific Spline-based trajectories. Characteristically, these trajectories are safe, they stay within the traffic lane borders, at the same time minimize an energy criterion along the path, and finally, they are curvature continuous. The present solution has been tested on several roads and the results show the efficiency of the energy constrained trajectory generation method.

In the context of aircraft, shimmy is an oscillatory phenomenon of the landing gear mainly due the tire dynamics and the landing gear structural dynamics. This phenomenon, which can result in severe structural damages of the landing gear, is here actively damped by a direct adaptive output feedback controller. The difficulties to model the ground/wheel interface require the use of an adaptive controller that can modify its behaviour in accordance with the plant dynamics. Thus, the proposed controller uses a fuzzy system to approximate the ideal feedback linearization law. Based on Lyapunov's theory, it is proven that the proposed adaptive control solution guarantees that all the error signals of the close-loop system are bounded. The main advantage of this control solution lies in the fact that the states of the system are not required for the synthesis of the controller. Simulation results show that the proposed control law creates a realistic control input which properly damps the oscillations. This work is supported by the European DRESS project (Distributed and Redundant Electromechanical nose gear steering System).