Artificial intelligence is penetrating various fields. The demand for interdisciplinary talent is increasingly important, while interdisciplinary educational activities for high school students are lagging behind. Project-based learning (PBL) in artificial intelligence (AI) and robotic education activities supported by a robotic sailboat platform, the sailboat test arena (STAr), has been shown to popularise AI and robotic knowledge in young students. In the implementation of the programme, PBL was provided for students, and gamification pedagogy was applied to increase participants' learning motivation and engagement. The results show that the proposed STAr-based programme is capable of delivering the desired knowledge and skills to students at high school levels. The assessment results suggest that most students achieve learning outcomes on average. Students showed more interest in AI and marine disciplines and were willing to participate in more such educational programs. The findings fill the research gap that few existing education platforms have facilitated the teaching and learning of AI and marine disciplines for high school students.

In this paper, a novel deep learning dataset, called Air2Land, is presented for advancing the state-of-the-art object detection and pose estimation in the context of one fixed-wing unmanned aerial vehicle autolanding scenarios. It bridges vision and control for ground-based vision guidance systems having the multi-modal data obtained by diverse sensors and pushes forward the development of computer vision and autopilot algorithms targeted at visually assisted landing of one fixed-wing vehicle. The dataset is composed of sequential stereo images and synchronised sensor data, in terms of the flying vehicle pose and Pan-Tilt Unit angles, simulated in various climate conditions and landing scenarios. Since real-world automated landing data is very limited, the proposed dataset provides the necessary foundation for vision-based tasks such as flying vehicle detection, key point localisation, pose estimation etc. Hereafter, in addition to providing plentiful and scene-rich data, the developed dataset covers high-risk scenarios that are hardly accessible in reality. The dataset is also open and available at https://github.com/micros-uav/micros_air2land as well. The cover image is based on the Research Article Air2Land: A deep learning dataset for unmanned aerial vehicle autolanding from air to land by Tianjiang Hu et al., https://doi.org/10.1049/csy2.12045.

Without the dependence of depth ground truth, self-supervised learning is a promising alternative to train monocular depth estimation. It builds its own supervision signal with the help of other tools, such as view synthesis and pose networks. However, more training parameters and time consumption may be involved. This paper proposes a monocular depth prediction framework that can jointly learn the depth value and pose transformation between images in an end-to-end manner. The depth network creatively employs an asymmetric convolution block instead of every square kernel layer to strengthen the learning ability of extracting image features when training. During inference time, the asymmetric kernels are fused and converted to the original network to predict more accurate image depth, thus bringing no extra computations anymore. The network is trained and tested on the KITTI monocular dataset. The evaluated results demonstrate that the depth model outperforms some State of the Arts (SOTA) approaches and can reduce the inference time of depth prediction. Additionally, the proposed model performs great adaptability on the Make3D dataset.

In this work, a new active fault tolerant control (FTC) is developed for an unmanned bicycle robot based on an integration between a sliding mode control (SMC), fault detection (FD), and fault estimation (FE) via a residual signal. A sliding surface in accordance with the fault tolerant sliding mode control (FTSMC) is designed for the bicycle robot to get multiple exciting features such as fast transient response with finite time convergence, small overshoot and quick stabilisation in the presence of an actuator fault. To obtain an effective performance for the FTSMC, a fault estimation system is employed and in order to attain estimation, an extended Kalman filter (EKF) as an estimator and a change detection algorithm called cumulative sum (CUSUM) as a residual evaluation function are developed. The innovative features of the proposed approach, that is FTSMC, are verified when compared with the other up-to-date control techniques like fault tolerant model-based predictive control with feedback linearisation (FTMPC + FBL) and fault tolerant linear quadratic regulator with feedback linearisation (FTLQR + FBL) on an unmanned bicycle robot.

本文从两个角度研究了欠驱动机械系统的鲁棒互联和阻尼分配无源控制问题(IDA-PBC)。首先，本研究分析了IDA-PBC在非消失匹配和非匹配不确定性条件下的鲁棒性，得到了保证系统最终有界的充分条件。其次，通过在原有的IDA-PBC控制器基础上增加一个新的控制输入，实现了闭环系统在非消失匹配不确定性下的渐近稳定性。最后，以惯性轮摆和单连杆弹性关节机器人为例，对所提出的鲁棒控制器进行了仿真分析。仿真结果清楚地表明了该鲁棒控制器的有效性。

Driving and tilt-hovering help unmanned aerial vehicles extend their time of operation and the visible range of the equipped sensors. Standard quadrotors are underactuated, whose translational and rotational motions are strongly coupled. As a result, it is impossible to maintain the attitude invariably while changing position. Meanwhile, larger flying speed results in greater air resistance, which is also not desirable. A novel aerial platform with tiltable rotors is proposed. Compared with standard quadrotors, the aerial vehicle has three special capabilities: vector-flying, tilt-hovering, and driving. The platform consists of two tilting-axes to change the direction of thrusts with respect to the aircraft body. To guarantee flexible manoeuvres, a geometric tracking control scheme that adapts on the fly to the thrust vectoring is exploited. Manoeuvrability of the proposed vehicle in different manoeuvres is demonstrated, such as flight with vectoring thrust that can reduce the flight resistance, tilt-hovering expanding the reachable set of the equipped sensors. The prototype equipped with an electromechanical controller is constructed, and several associated preliminary experiments are performed. The feasibility of the mechanical design and the geometric control strategy are demonstrated.

Image matching is a well-studied problem in computer vision. Conventional image matching is solved using image feature matching algorithms, and later deep learning techniques are also applied to tackle the problem. Here, a slope-modelling framework is proposed by adopting the image matching techniques. First, image pairs of a slope scene are captured and camera calibration as well as image rectification are performed. Then, PatchMatch Filter (PMF-S) and PWC-Net techniques are adapted to solve the matching of image pairs. In the proposed PatchMatch Filter-Census (PMF-Census), slanted-plane modelling, image census transform and gradient difference are employed in matching cost formulation. Later, nine matching points are manually selected from an image pair. Matching point pairs are further used in fitting a transformation matrix to relate the matching between the image pair. Then, the transformation matrix is applied to obtain a ground truth matching image for algorithm evaluation. The challenges in this matching problem are that the slope is of a homogenous region and it has a slanted-surface geometric structure. In this work, it is found out that the error rate of the proposed PMF-Census is significantly lower as compared with the PWC-Net method and is more suitable in this slope-modelling task. In addition, to show the robustness of the proposed PMF-Census against the original PMF-S, further experiments on some image pairs from Middlebury Stereo 2006 dataset are conducted. It is demonstrated that the error percentage by the proposed PMF-Census is reduced significantly especially in the low-texture and photometric distorted region, in comparison to the original PMF-S algorithm. This further verifies the suitability of the PMF-Census in modelling the outdoor low-texture slope scene.

This study addresses the problem of deploying a group of mobile robots over a non-convex region with obstacles. Assuming that the robots are equipped with omnidirectional range sensors of common radius, disjoint subsets of the sensed area are assigned to the robots. These proximity-based subsets are calculated using the visibility notion, where the cell of each robot is treated as an opaque obstacle for the other robots. Based on that, optimal spatially distributed coordination algorithms are derived for the area coverage problem and for the homing problem, where the swarm needs to move to specific locations. Experimental studies demonstrate the results.

With a wide range of applications in autonomous driving and robotics, semantic segmentation for large-scale outdoor point clouds is a critical and challenging issue. Due to the large number and irregular arrangement of point clouds, it is difficult to balance the efficiency and effectiveness. In this paper, we propose LessNet, a lightweight and efficient voxel-based method for LiDAR-only semantic segmentation, taking advantage of cylindrical partition and intra-voxel feature fusion. Specifically, we use a cylindrical partition method to distribute the outdoor point clouds more evenly in voxels. To better encode the voxel features, we adopt an intra-voxel aggregation method without querying neighbours. The voxel features are further input into a lightweight and effective 3D U-net to aggregate local features and dilate the receptive field. Extensive experiments prove the satisfied semantic segmentation performance and the improvement of each component in our proposed framework. Our method is capable of processing more than one million point clouds at a time while retaining low latency and few parameters. Moreover, our method achieves comparable performance with state-of-the-art approaches and outperforms all projection-based methods on the SemanticKITTI benchmark.