This article presents a survey of simultaneous localization and mapping (SLAM) and data fusion techniques for object detection and environmental scene perception in unmanned aerial vehicles (UAVs). We critically evaluate some current SLAM implementations in robotics and autonomous vehicles and their applicability and scalability to UAVs. SLAM is envisioned as a potential technique for object detection and scene perception to enable UAV navigation through continuous state estimation. In this article, we bridge the gap between SLAM and data fusion in UAVs while also comprehensively surveying related object detection techniques such as visual odometry and aerial photogrammetry. We begin with an introduction to applications where UAV localization is necessary, followed by an analysis of multimodal sensor data fusion to fuse the information gathered from different sensors mounted on UAVs. We then discuss SLAM techniques such as Kalman filters and extended Kalman filters to address scene perception, mapping, and localization in UAVs. The findings are summarized to correlate prevalent and futuristic SLAM and data fusion for UAV navigation, and some avenues for further research are discussed.

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Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Integrating unmanned aerial vehicles with RIS (UAV–RIS) can offer ubiquitous deployment services in communication-disabled areas, but is limited by the on-board energy of the UAVs. In this paper, a novel energy harvesting (EH) scheme on top of the UAV–RIS system, called EH-RIS scheme, is developed for the next generation high performance wireless system. The proposed EH-RIS scheme extends the simultaneous wireless information and power transfer (SWIPT) system by splitting the passive reflected arrays on the geometric space for transporting information and harvesting energy simultaneously. However, pedestrian mobility, and rapid channel changes post challenges to efficient resource allocation in wireless systems. Thus, a robust deep reinforcement learning (DRL)-based algorithm is developed to improve the proposed EH-RIS scheme for guaranteeing the quality of service (QoS) under dynamic wireless environments. The simulation results demonstrate the effectiveness and efficiency of the proposed robust DRL-based EH-RIS system, which not only outperform the existing state-of-the-art solutions but also approach to the performance of the exhaustive search method. 

Energy Harvesting Reconfigurable Intelligent Surface for UAV Based on Robust Deep Reinforcement Learning

Reconfigurable intelligent surface (RIS) is a promising technology for energy efficient wireless communications and has drawn significant attention recently. Combining unmanned aerial vehicle with RIS (UAV-RIS) can provide on-demand deployment services in dynamic scenarios. However, reaping the benefits of UAV-RIS will be limited by the energy of the battery-powered UAV. To enhance the endurance of UAV-RISs, we develop a novel energy harvesting scheme for simultaneous wireless information and power transfer (SWIPT), resource allocation, and energy harvest from impinging radio-frequency (RF) signals. Different from the exist works, the proposed scheme creatively splits the passive reflected arrays on geometric space for transporting information and harvesting energy simultaneously. Furthermore, a deep deterministic policy gradient (DDPG) scheme is designed to continuously allocate UAV-RIS’s resources on both the time and space domains to maximize the total harvested energy, while guaranteeing the communication quality for each user. As shown by our simulation results, the proposed UAV-RIS SWIPT system improves performance significantly over the benchmark.

Long-Lasting UAV-aided RIS Communications based on SWIPT

Resource management in UAV-assisted MEC: state-of-the-art and open challenges

Reconfigurable intelligent surfaces (RIS) have emerged as a prominent and widely debated solution to enhance the energy efficiency of wireless communications. This article explores the potential of combining RIS with unmanned aerial vehicles (UAVs)-RIS, to provide on-demand deployment services in dynamic environments. However, the energy limitations of battery-powered UAVs can curtail the advantages of UAV-RIS systems. To address this challenge and enhance the durability of UAV-RIS deployments, we introduce an energy harvesting technique for simultaneous wireless information and power transfer (SWIPT) coupled with optimized resource allocation and energy harvesting from incoming radio frequency (RF) signals. In contrast to previous research, our approach involves the division of passive reflected arrays across geometric space, facilitating simultaneous information transfer and energy harvesting. Additionally, we are developing deep Q-network (DQN) and deep deterministic policy gradient (DDPG) techniques to dynamically allocate UAV-RIS resources in both temporal and spatial dimensions. This allocation maximizes the overall harvested energy while upholding communication quality for every user. Our simulation results conclusively demonstrate the substantial superiority of the proposed UAV-RIS SWIPT system over the benchmark. 

Enhancing UAV communication links with Reconfigurable intelligent surfaces

In this paper, we investigate a low-altitude unmanned aerial vehicle (UAV)-assisted wireless power- ed device-to-device (D2D) network with the “harvest-transmit-store” model, where a UAV broadcasts energy to all D2D transmitters and the transmitters then store or use the energy for transmission information. In the proposed model, the UAV does not transmit energy in the transmission information time, resulting in a limited charging time. In addition, the interference may be caused by communicating information of an arbitrary number of D2D pairs simultaneously. In this paper, we propose a hybrid time division duplex/frequency division duplex communication scheme, which performs the power transfer and information transmission simultaneously in the transmission information time. The scheme cancels the cochannel interference by using the time division duplex scheme and guarantees the charging time by using the frequency division duplex. We consider optimizing the throughput based on the scheme via joint power control, spectrum allocation, and scheduling time. We propose a two-step algorithm to solve the formulated nonconvex problem. The simulation results show that the proposed algorithm improves the average throughput compared to the other schemes.

Throughput maximization for UAV-assisted wireless powered D2D communication networks with a hybrid time division duplex/frequency division duplex scheme

In this mymargin letter, we investigate a backbone-assisted wireless powered communication network (WPCN) under the protocol of “harvest-then-transmit” protocol, where user nodes (UNs) harvest energy from the sinks in the downlink (DL), then they use the energy to transmit signals to the sinks by a non-orthogonal multiple access in the uplink (UL). We consider joint successive interference cancellation and backbone-assisted sink cooperation to reduce the impact of co-channel interference on the throughput performance. We maximize the system throughput by jointly optimizing user scheduling and the time allocation for DL energy harvesting and UL transmission. For solving the maximum throughput problem efficiently, we propose a column generation method based on a greedy algorithm. Numerical results show that our proposed energy and information transfer design is superior to other reference methods in WPCNs.

Throughput Maximization in Backbone-Assisted Wireless Powered Communication Networks With Successive Interference Cancellation

Joint power control and user scheduling for backbone-assisted industrial wireless networks with successive interference cancellation

Rate-monotonic algorithm is a classical algorithm for scheduling periodical tasks. Lots of work has been carried out to analyze the schedulability of RMS algorithm. While most of the work focus on the classical Liu and Layland task model. In this paper, we propose an approach to testing the schedulability of RMS algorithm on digraph task models. Our approach has higher efficiency since we just analyze schedulability of subtasks in the scheduling point set of one time interval rather than all possible cases. 

Schedulability Analysis of Rate-Monotonic Algorithm on Concurrent Execution of Digraph Real-Time Tasks

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Throughput maximization for UAV-assisted wireless powered D2D communication networks with a hybrid time division duplex/frequency division duplex scheme

Throughput Maximization in Backbone-Assisted Wireless Powered Communication Networks With Successive Interference Cancellation

Joint power control and user scheduling for backbone-assisted industrial wireless networks with successive interference cancellation

Schedulability Analysis of Rate-Monotonic Algorithm on Concurrent Execution of Digraph Real-Time Tasks