Xiaohui Li

Protection of wild animals relies on understanding the interaction between the animals and their environment. With the ability to rapidly access rugged areas, aerial monitoring by drones is fast becoming a viable tool for ecologists to monitor wild animals. Unfortunately, this approach results in significant disturbance to different species of wild animals. Inspired by motion camouflage, this article explores a navigation method for a drone to covertly observe a group of animals and their habitat. Unlike existing motion-camouflage navigation approaches that deceive a single target, we introduce a sliding-mode-based method that reactively navigates the drone to induce less optical flow on multiple targets’ visual system. The proposed method is computationally simple and suitable for a drone to closely observe a group of moving animals with reduced visual disturbance. Computer simulations are conducted to demonstrate the performance of the proposed method.

This paper proposes a novel robotic animal herding system based on a network of autonomous barking drones. The objective of such a system is to replace traditional herding methods (e.g., dogs) so that a large number (e.g., thousands) of farm animals such as sheep can be quickly collected from a sparse status and then driven to a designated location (e.g., a sheepfold). In this paper, we particularly focus on the motion control of the barking drones. To this end, a computationally efficient sliding mode based control algorithm is developed, which navigates the drones to track the moving boundary of the animals’ footprint and enables the drones to avoid collisions with others. Extensive computer simulations, where the dynamics of the animals follow Reynolds’ rules, show the effectiveness of the proposed approach.

As a typical cyber-physical system, networked unmanned aerial vehicles (UAVs) have received much attention in recent years. Emerging communication technologies and high-performance control methods enable networked UAVs to operate as aerial sensor networks to collect more complete and consistent information with significantly improved mobility and flexibility than traditional sensing platforms. One of the main applications of networked UAVs is surveillance and monitoring, which constitute essential components of a well-functioning public safety system and many industrial applications. Although the existing literature on surveillance and monitoring UAVs is extensive, a comprehensive survey on this topic is lacking. This article classifies publications on networked UAVs for surveillance and monitoring using the targets of interest and analyzes several typical problems on this topic, including the control, navigation, and deployment optimization of UAVs. The related research gaps and future directions are also presented.

Aiming at the impact of high power dynamic load on energy measurement in smart grids, a fast random fluctuation test model for high power dynamic loads is proposed, a fast random fluctuation test method for high power dynamic loads is proposed, and a smart meter dynamic error test system is established. The case proves that the test method in this paper can effectively test the dynamic error of the energy meter.

This paper addresses a problem of autonomous navigation of unmanned aerial vehicles (UAVs) for the surveillance of multiple moving ground targets. The ground can be flat or uneven. A reactive real-time sliding mode control algorithm is proposed that navigates a team of communicating UAVs, equipped with ground-facing video cameras, towards moving targets to increase some measure of sensing coverage of the targets by the UAVs. Moreover, the Voronoi partitioning technique is adopted to reduce the movement range of the UAVs and decrease the revisit times of the targets. Extensive computer simulations, from the simple case with one UAV and multiple targets to the complex case with multiple UAVs and multiple targets, are conducted to demonstrate the performance of the developed autonomous navigation algorithm. The scenarios where the terrain is uneven are also considered. As shown in the simulation results, although the additional VP technique leads to some extra computation burden, the usage of the VP technique considerably reduces the target revisit time compared to the algorithm without this technique.

Shark attacks can make beach tourists anxious about sharing the ocean with apex predators. Although the raw number of shark attacks is deficient, the absolute terror caused by sharks is genuine. This paper introduces a novel method named as ’drone shark shield system’, which uses communicating autonomous drones to intervene and prevent shark attacks for protecting swimmers and surfers. We detail the design of the drone shark shield system and the strategy for repelling sharks through multiple intersections. A shark interception algorithm is developed to guide drones to predicted intersection points for deterring sharks. Computer simulations are conducted to illustrate our method.

In order to provide an increased capacity, throughput and QoS guarantee for terrestrial users in emergency scenarios, a low-medium-altitude aerial platform based time-division-duplex long term evolution (TD-LTE) system referred to as Aerial LTE, is presented in this paper. Additionally a power-efficient radio resource allocation mechanism is proposed for both the Aerial LTE downlink and uplink, which is modeled as a cooperative game. Our simulation results demonstrate that the proposed algorithm imposes an attractive tradeoff between the achievable throughput and the power consumption while ensuring fairness among users.