456 research outputs found
A review of relay network on UAVS for enhanced connectivity
One of the best evolution in technology breakthroughs is the Unmanned Aerial Vehicle (UAV). This aerial system is able to perform the mission in an agile environment and can reach the hard areas to perform the tasks autonomously. UAVs can be used in post-disaster situations to estimate damages, to monitor and to respond to the victims. The Ground Control Station can also provide emergency messages and ad-hoc communication to the Mobile Users of the disaster-stricken community using this network. A wireless network can also extend its communication range using UAV as a relay. Major requirements from such networks are robustness, scalability, energy efficiency and reliability. In general, UAVs are easy to deploy, have Line of Sight options and are flexible in nature. However, their 3D mobility, energy constraints, and deployment environment introduce many challenges. This paper provides a discussion of basic UAV based multi-hop relay network architecture and analyses their benefits, applications, and tradeoffs. Key design considerations and challenges are investigated finding fundamental issues and potential research directions to exploit them. Finally, analytical tools and frameworks for performance optimizations are presented
Location prediction and trajectory optimization in multi-UAV application missions
Unmanned aerial vehicles (a.k.a. drones) have a wide range of applications in e.g., aerial surveillance, mapping, imaging, monitoring, maritime operations, parcel delivery, and disaster response management. Their operations require reliable networking environments and location-based services in air-to-air links with cooperative drones, or air-to-ground links in concert with ground control stations. When equipped with high-resolution video cameras or sensors to gain environmental situation awareness through object detection/tracking, precise location predictions of individual or groups of drones at any instant possible is critical for continuous guidance. The location predictions then can be used in trajectory optimization for achieving efficient operations (i.e., through effective resource utilization in terms of energy or network bandwidth consumption) and safe operations (i.e., through avoidance of obstacles or sudden landing) within application missions. In this thesis, we explain a diverse set of techniques involved in drone location prediction, position and velocity estimation and trajectory optimization involving: (i) Kalman Filtering techniques, and (ii) Machine Learning models such as reinforcement learning and deep-reinforcement learning. These techniques facilitate the drones to follow intelligent paths and establish optimal trajectories while carrying out successful application missions under given resource and network constraints. We detail the techniques using two scenarios. The first scenario involves location prediction based intelligent packet transfer between drones in a disaster response scenario using the various Kalman Filtering techniques. The second scenario involves a learning-based trajectory optimization that uses various reinforcement learning models for maintaining high video resolution and effective network performance in a civil application scenario such as aerial monitoring of persons/objects. We conclude with a list of open challenges and future works for intelligent path planning of drones using location prediction and trajectory optimization techniques.Includes bibliographical references
Impact of drone route geometry on information collection in wireless sensor networks
The recent technological evolution of drones along with the constantly growing maturity of its commercialization, has led to the emergence of novel drone-based applications within the field of wireless sensor networks for information collection purposes. In such settings, especially when deployed in outdoor environments with limited external control, energy consumption and robustness are challenging problems for the system’s operation. In the present paper, a drone-assisted wireless sensor network is studied, the aim being to coordinate the routing of information (among the ground nodes and its propagation to the drone), investigating several drone trajectories or route shapes and examining their impact on information collection (the aim being to minimize transmissions and consequently, energy consumption). The main contribution lies on the proposed algorithms that coordinate the communication between (terrestrial) sensor nodes and the drone that may follow different route shapes. It is shown through simulations using soft random geometric graphs that the number of transmitted messages for each drone route shape depends on the rotational symmetry around the center of each shape. An interesting result is that the higher the order of symmetry, the lower the number of transmitted messages for data collection. Contrary, for those cases that the order of symmetry is the same, even for different route shapes, similar number of messages is transmitted. In addition to the simulation results, an experimental demonstration, using spatial data from grit bin locations, further validates the proposed solution under real-world conditions, demonstrating the applicability of the proposed approach.publishedVersio
Autonomous Hybrid Ground/Aerial Mobility in Unknown Environments
Hybrid ground and aerial vehicles can possess distinct advantages over
ground-only or flight-only designs in terms of energy savings and increased
mobility. In this work we outline our unified framework for controls, planning,
and autonomy of hybrid ground/air vehicles. Our contribution is three-fold: 1)
We develop a control scheme for the control of passive two-wheeled hybrid
ground/aerial vehicles. 2) We present a unified planner for both rolling and
flying by leveraging differential flatness mappings. 3) We conduct experiments
leveraging mapping and global planning for hybrid mobility in unknown
environments, showing that hybrid mobility uses up to five times less energy
than flying only
Communication and Control in Collaborative UAVs: Recent Advances and Future Trends
The recent progress in unmanned aerial vehicles (UAV) technology has
significantly advanced UAV-based applications for military, civil, and
commercial domains. Nevertheless, the challenges of establishing high-speed
communication links, flexible control strategies, and developing efficient
collaborative decision-making algorithms for a swarm of UAVs limit their
autonomy, robustness, and reliability. Thus, a growing focus has been witnessed
on collaborative communication to allow a swarm of UAVs to coordinate and
communicate autonomously for the cooperative completion of tasks in a short
time with improved efficiency and reliability. This work presents a
comprehensive review of collaborative communication in a multi-UAV system. We
thoroughly discuss the characteristics of intelligent UAVs and their
communication and control requirements for autonomous collaboration and
coordination. Moreover, we review various UAV collaboration tasks, summarize
the applications of UAV swarm networks for dense urban environments and present
the use case scenarios to highlight the current developments of UAV-based
applications in various domains. Finally, we identify several exciting future
research direction that needs attention for advancing the research in
collaborative UAVs
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