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We present a method to autonomously land an Unmanned Aerial Vehicle on a moving vehicle with a circular (or elliptical) pattern on the top. A visual servoing controller approaches the ground vehicle using velocity commands calculated directly in image space. The control laws generate velocity commands in all three dimensions, eliminating the need for a separate height controller. The method has shown the ability to approach and land on the moving deck in simulation, indoor and outdoor environments, and compared to the other available methods, it has provided the fastest landing approach. It does not rely on additional external setup, such as RTK, motion capture system, ground station, offboard processing, or communication with the vehicle, and it requires only a minimal set of hardware and localization sensors. The videos and source codes can be accessed from http://theairlab.org/landing-on-vehicle.
In this paper, we present an autonomous unmanned aerial vehicle (UAV) landing system based on visual navigation. We design the landmark as a topological pattern in order to enable the UAV to distinguish the landmark from the environment easily. In addition, a dynamic thresholding method is developed for image binarization to improve detection efficiency. The relative distance in the horizontal plane is calculated according to effective image information, and the relative height is obtained using a linear interpolation method. The landing experiments are performed on a static and a moving platform, respectively. The experimental results illustrate that our proposed landing system performs robustly and accurately.
In autonomous driving, using a variety of sensors to recognize preceding vehicles in middle and long distance is helpful for improving driving performance and developing various functions. However, if only LiDAR or camera is used in the recognition stage, it is difficult to obtain necessary data due to the limitations of each sensor. In this paper, we proposed a method of converting the tracking data of vision into birds eye view (BEV) coordinates using an equation that projects LiDAR points onto an image, and a method of fusion between LiDAR and vision tracked data. Thus, the newly proposed method was effective through the results of detecting closest in-path vehicle (CIPV) in various situations. In addition, even when experimenting with the EuroNCAP autonomous emergency braking (AEB) test protocol using the result of fusion, AEB performance is improved through improved cognitive performance than when using only LiDAR. In experimental results, the performance of the proposed method was proved through actual vehicle tests in various scenarios. Consequently, it is convincing that the newly proposed sensor fusion method significantly improves the ACC function in autonomous maneuvering. We expect that this improvement in perception performance will contribute to improving the overall stability of ACC.
This paper proposes a life-long adaptive path tracking policy learning method for autonomous vehicles that can self-evolve and self-adapt with multi-task knowledge. Firstly, the proposed method can learn a model-free control policy for path tracking directly from the historical driving experience, where the property of vehicle dynamics and corresponding control strategy can be learned simultaneously. Secondly, by utilizing the life-long learning method, the proposed method can learn the policy with task-incremental knowledge without encountering catastrophic forgetting. Thus, with continual multi-task knowledge learned, the policy can iteratively adapt to new tasks and improve its performance with knowledge from new tasks. Thirdly, a memory evaluation and updating method is applied to optimize memory structure for life-long learning which enables the policy to learn toward selected directions. Experiments are conducted using a high-fidelity vehicle dynamic model in a complex curvy road to evaluate the performance of the proposed method. Results show that the proposed method can effectively evolve with continual multi-task knowledge and adapt to the new environment, where the performance of the proposed method can also surpass two commonly used baseline methods after evolving.
Model predictive control (MPC) is widely used for path tracking of autonomous vehicles due to its ability to handle various types of constraints. However, a considerable predictive error exists because of the error of mathematics model or the model linearization. In this paper, we propose a framework combining the MPC with a learning-based error estimator and a feedforward compensator to improve the path tracking accuracy. An extreme learning machine is implemented to estimate the model based predictive error from vehicle state feedback information. Offline training data is collected from a vehicle controlled by a model-defective regular MPC for path tracking in several working conditions, respectively. The data include vehicle state and the spatial error between the current actual position and the corresponding predictive position. According to the estimated predictive error, we then design a PID-based feedforward compensator. Simulation results via Carsim show the estimation accuracy of the predictive error and the effectiveness of the proposed framework for path tracking of an autonomous vehicle.
Autonomous Underwater Vehicle-Manipulator systems (AUVMS) is a new tool for ocean exploration, the AUVMS path planning problem is addressed in this paper. AUVMS is a high dimension system with a large difference in inertia distribution, also it works in a complex environment with obstacles. By integrating the rapidly-exploring random tree(RRT) algorithm with the AUVMS kinematics model, the proposed RRTAUVMS algorithm could randomly sample in the configuration space(C-Space), and also grow the tree directly towards the workspace goal in the task space. The RRTAUVMS can also deal with the redundant mapping of workspace planning goal and configuration space goal. Compared with the traditional RRT algorithm, the efficiency of the AUVMS path planning can be significantly improved.