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 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.

Autonomous vehicle navigation requires the desired trajectory and the current localization to be able to calculate the command that must be sent to the actuators. The localization of the vehicle (usually defined by a position vector and an orientation vector), can be provided by external systems. GPS localization is the most accurate solution but when it is no longer available or precise, an on-board localization estimation based on proprioceptive and exteroceptive sensors is needed. Visual odometry is a well-known approach to estimate the vehicle motion from a camera. Unfortunately, visual localization is subject to errors that increase over time (drift). In this paper, we provide a study of the impact of localization errors on the control of an autonomous vehicle. In order to validate visual odometry algorithms in simulation, a drift model is proposed. Real navigation experiments with errors on the localization are presented to characterize the drift model and the propagation of localization errors in the controller module and the associated command signal.

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.

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.