Abstract Unmanned Aerial Vehicles (UAVs), also called drones, are used for various applications with two basic classifications: civilian and military drones. Civilian drones are used for various applications like construction site monitoring, natural disaster area monitoring, agriculture, etc. Military drones are used for applications like monitoring a country’s border, the transmission of information about the intruders to Ground Control Station (GCS), or to any other server which has been designed for the purpose. Before the real-time deployment of the Internet of Drones (IoD), the feasibility and efficiency of a proposed UAV framework should be tested in an open-source UAV simulator or any network simulator. AirSim simulator is an open-source UAV simulator that has different features like ease of development, efficient motion capture, efficient obstacle detections and collision detections, use of different sensor models, and physics models. Hence, a review has been done on recent UAV frameworks which have been designed and tested using the AirSim simulator. Since a vast amount of data is being transmitted between IoD devices in this era, there is a need for designing secure IoD communication frameworks, with the least compromise in the performance of the designed frameworks. Hence, an extensive review has been done on different secure IoD communication frameworks which have used different cryptography concepts like key agreement, authentication, encryption and decryption, integrity, blockchain, digital signatures, and have implemented their proposed frameworks in real-time, or by using network simulators. The common network simulators that have been used for simulating secure IoD frameworks/mechanisms are NS3 and OMNeT++.

https://www.tandfonline.com/doi/pdf/10.1080/23311916.2022.2029080?needAccess=true

Security in Internet of Drones: A Comprehensive Review

This work presents both spiking and conventional neural network architectures, trained using reinforcement learning, for high-speed collision avoidance using dynamic vision sensors. Dynamic vision sensors are a novel class of event-based sensing equipment that enable extremely high sampling rates, but with unordinary, one-dimensional image data. A parallelized, event-stream simulation framework was developed to train reinforcement learning agents using the Microsoft AirSim UAV simulator. Event-stream data modeling output from a dynamic vision sensor was extrapolated from conventional frame-based camera feeds taken from the AirSim environment. Once trained, the spiking and conventional neural networks can be deployed to physical hardware with minimal modifications, as supported by previous work making use of the AirSim simulator.

A Neuro-inspired Approach to Intelligent Collision Avoidance and Navigation

Heterogeneous Unmanned Aerial Vehicle (UAV) swarms offer unique opportunities for solving multi-robot missions, but also introduce novel implementation challenges. In our study, we develop Heterogeneous Decentralized Receding Horizon Control (HD-RHC) for swarm management in search & rescue missions. This new technique builds upon existing multiagent UAV work, but adds the capacity to manage a fleet of heterogeneous, diverse robot platforms that are equipped for different mission capabilities. Through high-fidelity simulation (AirSim), we derive an optimal controller, develop a method to find optimal weights for a specific mission focus, and provide a path to physical system validation. We analyze the efficiency and performance of HD-RHC controller, and discuss different ways this new method can be integrated into mission-management scenarios.

Optimal Control Techniques for Heterogeneous UAV Swarms

Spiking Neural Networks (SNN) promise extremely low-power and low-latency inference on neuromorphic hardware. Recent studies demonstrate the competitive performance of SNNs compared with Artificial Neural Networks (ANN) in conventional classification tasks. In this work, we present an energy-efficient implementation of a Reinforcement Learning (RL) algorithm using SNNs to solve an obstacle avoidance task performed by an Unmanned Aerial Vehicle (UAV), taking a Dynamic Vision Sensor (DVS) as event-based input. We train the SNN directly, improving upon state-of-art implementations based on hybrid (not directly trained) SNNs. For this purpose, we devise an adaptation of the Spatio-Temporal Backpropagation algorithm (STBP) for RL. We then compare the SNN with a state-of-art Convolutional Neural Network (CNN) designed to solve the same task. To this aim, we train both networks by exploiting a photorealistic training pipeline based on AirSim. To achieve a realistic latency and throughput assessment for embedded deployment, we designed and trained three different embedded SNN versions to be executed on state-of-art neuromorphic hardware, targeting state-of-the-art. We compared SNN and CNN in terms of obstacle avoidance performance showing that the SNN algorithm achieves better results than the CNN with a factor of 6× less energy. We also characterise the different SNN hardware implementations in terms of energy and spiking activity. 

Directly-trained Spiking Neural Networks for Deep Reinforcement Learning: Energy efficient implementation of event-based obstacle avoidance on a neuromorphic accelerator

Quadcopters are widely used in luggage delivery and aerial photography However, it is difficult to fly quadcopters outdoors freely due to laws such as aviation law Quadcopters simulation modeling solves this problem, but few studies consider the influence of wind in the simulation In this work, we construct a program considering the influence of wind and implement the wind on AirSim The experimental results show that flying time in a simulator using the proposed program is almost the same as the flying time calculated by the formula

A Quadcopters Flight Simulation Considering the Influence of Wind

In recent years, unmanned aerial vehicle aerial photography has developed rapidly. Unmanned aerial vehicle can get a different perspective and allow us to do more difficult tasks. Controlling unmanned aerial vehicle requires a lot of manpower, so there are a number of studies that use reinforcement learning to make the unmanned aerial vehicle fly autonomously. It is an expensive and time-consuming task to use reinforcement learning and training unmanned aerial vehicle to accomplish specific tasks in a realistic environment. Therefore this study in a virtual environment using the Q - learning training unmanned aerial vehicle landing, then transplanted model of virtual environment in which to train good into real environment, makes the realistic environment of unmanned aerial vehicle can use cheaper and quickly achieve the same task.

A Parameter Sharing Method for Reinforcement Learning Model between AirSim and UAVs

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A Parameter Sharing Method for Reinforcement Learning Model between AirSim and UAVs