Creating safe paths in unknown and uncertain environments is a challenging aspect of leader–follower formation control. In this architecture, the leader moves toward the target by taking optimal actions, and followers should also avoid obstacles while maintaining their desired formation shape. Most of the studies in this field have inspected formation control and obstacle avoidance separately. This article proposes a new approach based on deep reinforcement learning for end-to-end motion planning and control of underactuated autonomous underwater vehicles (AUVs). The aim is to design optimal adaptive distributed controllers based on actor-critic structure for AUVs formation motion planning. This is accomplished by controlling the speed and heading of AUVs. In obstacle avoidance, two approaches are developed. In the first approach, the goal is to design control policies for the leader and followers such that each learns its own collision-free path. Moreover, the followers adhere to an overall formation maintenance policy. In the second approach, the leader solely learns the control policy and safely leads the whole group toward the target. Here, the control policy of the followers is to maintain the predetermined distance and angle. In the presence of ocean currents, communication delays, and sensing errors, the robustness of the proposed method under realistically perturbed circumstances is shown. The efficiency of the algorithms has been evaluated and approved using a number of computer-based simulations.

An automatic vehicle parallel parking method, including path planning, controller design, and state estimation is proposed in this article. Accordingly, a path is planned using clothoid sequences along with a straight line which avoids stopping the car to reorient the wheels. This method also enables the car to park from different initial poses. The generated path is followed by adjusting control inputs. The control inputs, including speed and steering angle, are solely a function of traveled distance. Reduced parking time along with the ability of parking in one or two maneuvers is achieved, smaller than standard places. An evolutionary optimization algorithm is used to calculate the best speed parameter according to the defined criteria. Furthermore, the Unscented Kalman Filter (UKF) is proposed to estimate the traveled distance, resulting in a smaller error compared to the conventional Extended Kalman Filter (EKF). The research aims to propose an optimal automatic parking algorithm which considers the shortcomings of the existing methods such as parking duration, the required space size for parking in the maximum of two maneuvers, and path continuity. Finally, the fidelity and improved performance of the proposed method are assessed in various probable conditions using the powerful Monte Carlo simulations.

This paper presents a range control approach for implementing hard geofencing for unmanned air vehicles (UAVs), and especially remotely piloted versions (RPVs), via a proposed anticipatory range calculator. The approach employs turning circle intersection tests that anticipate the fence perimeter on approach. This ensures the vehicle turns before penetrating the geofence and remains inside the allowable operational airspace by accounting for the vehicles’ turning dynamics. Allowance is made for general geozone shapes and locations, including those located at the problematic poles and meridians where nonlinear angle mapping is dealt with, concave geozones, narrow corners with acute internal angles, and transient turn dynamics. The algorithm is shown to prevent any excursions using a high-fidelity simulation of a small remotely piloted vehicle. The algorithm relies on a single tuning parameter which can be determined from the closed-loop rise time in the aircraft’s roll command tracking.