In this article, a multilayered and distributed architecture for mission-oriented miniature fixed-wing UAV swarms is presented. Based on the concept of modularity, the proposed architecture divides the overall system into five layers: 1) low-level control layer; 2) high-level control layer; 3) coordination layer; 4) communication layer; and 5) human interaction layer, and many modules that can be viewed as black boxes with interfaces of inputs and outputs. In this way, not only the complexity of developing a large system can be reduced but also the versatility of supporting diversified missions can be ensured. Furthermore, the proposed architecture is fully distributed that each UAV performs the decision-making procedure autonomously so as to achieve better scalability. Moreover, different kinds of aerial platforms can be feasibly extended by using the control allocation matrices and the integrated hardware box. A prototype swarm system based on the proposed architecture is built and the proposed architecture is evaluated through field experiments with a scale of 21 fixed-wing UAVs. Particularly, to the best of our knowledge, this article is the first work which successfully demonstrates formation flight, target recognition, and tracking missions within an integrated architecture for fixed-wing UAV swarms through field experiments.

Deep reinforcement learning has led to dramatic breakthroughs in the field of artificial intelligence for the past few years. As the amount of rollout experience data and the size of neural networks for deep reinforcement learning have grown continuously, handling the training process and reducing the time consumption using parallel and distributed computing is becoming an urgent and essential desire. In this article, we perform a broad and thorough investigation on training acceleration methodologies for deep reinforcement learning based on parallel and distributed computing, providing a comprehensive survey in this field with state-of-the-art methods and pointers to core references. In particular, a taxonomy of literature is provided, along with a discussion of emerging topics and open issues. This incorporates learning system architectures, simulation parallelism, computing parallelism, distributed synchronization mechanisms, and deep evolutionary reinforcement learning. Furthermore, we compare 16 current open-source libraries and platforms with criteria of facilitating rapid development. Finally, we extrapolate future directions that deserve further research.

MapReduce has become a prevalent programming model for building data processing applications in the cloud. While being widely used, existing MapReduce schedulers still suffer from an issue known as partitioning skew, where the output of map tasks is unevenly distributed among reduce tasks. Existing solutions follow a similar principle that repartitions workload among reduce tasks. However, those approaches often incur high performance overhead due to the partition size prediction and repartitioning. In this paper, we present DREAMS, a framework that provides run-time partitioning skew mitigation. Instead of repartitioning workload among reduce tasks, we cope with the partitioning skew problem by controlling the amount of resources allocated to each reduce task. Our approach completely eliminates the repartitioning overhead, yet is simple to implement. Experiments using both real and synthetic workloads running on a 21-node Hadoop cluster demonstrate that DREAMS can effectively mitigate the negative impact of partitioning skew, thereby improving the job completion time by up to a factor of $2.29$ over the native Hadoop YARN. Compared to the state-of-the-art solution, DREAMS can improve the job completion time by a factor of $1.65$ .

This paper presents an optimized image-based visual servo (IBVS) control scheme for tracking a ground target by using a fixed-wing unmanned aerial vehicle (UAV) with a monocular camera fixed on it. Unlike the widely used rotor UAVs, the fixed-wing UAV has much more dynamical constraints, such as it cannot move omni-direction and its minimum speed is limited by the stalling speed. This makes the target tracking problem more challenging. The proposed scheme leverages the image Jacobian matrix to build connection between the velocity of the feature point and that of the UAV. Afterwards, considering the camera is fixed on the body of the UAV, an “Ideal Camera” model is proposed to compensate the shifts of the feature point caused by the changes of the UAV’s attitude. Then, an optimized control law without solving the pseudo-inverse of image Jacobian matrix is proposed with the aid of the least square method from the target center in image coordinate systerm. This control law takes the velocity of the feature point as inputs and the yaw angular velocity of the UAV as outputs. The stability of the proposed law is analyzed with Lyapunov method, showing that the UAV will circle around the ground target asymptotically. Finally, the proposed scheme is evaluated by a hardware-in-the-loop (HIL) simulation based on the Gazebo simulator and the off-the-shelf autopilot hardware.