Cognitive Chaotic UWB-MIMO Detect-Avoid Radar for Autonomous UAV Navigation

Nijsure, Yogesh; Kaddoum, Georges; Mallat, Nazih Khaddaj; Gagnon, Ghyslain; Gagnon, François

doi:10.1109/tits.2016.2539002

Cited by 23 publications

(19 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, using forward facing radars, radar's simultaneous multitarget range capability, the detection of targets in the wide angular range of ±60 o in azimuth is shown by experimental results. In [73], in order to implement the proposed autonomous collision avoidance strategy, the authors utilised Ultra-Wideband (UWB) collocated MIMO radar. One of the key benefits of radar cognition is the ability to adapt UWB-MIMO radar transmission waveform for providing improved detection and therefore to guide the UAV it provides approximation of the collision points.…”

Section: ) Radarmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

et al. 2020

View full text Add to dashboard Cite

Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). A lot of work is being done to make the CAS as safe and reliable as possible, necessitating a comparative study of the recent work in this important area. The paper provides a comprehensive review of collision avoidance strategies used for unmanned vehicles, with the main emphasis on unmanned aerial vehicles (UAV). It is an in-depth survey of different collision avoidance techniques that are categorically explained along with a comparative analysis of the considered approaches w.r.t. different scenarios and technical aspects. This also includes a discussion on the use of different types of sensors for collision avoidance in the context of UAVs. INDEX TERMS autonomous aerial vehicles, autonomous vehicles, collision avoidance, active and passive sensors, optimisation-based, force-field based, sense and avoid, geometry based

show abstract

Section: ) Radarmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

et al. 2020

View full text Add to dashboard Cite

show abstract

“…There are also many other communications technologies that can be used by DL-UAVs, like 3G/4G/5G [106,107], ZigBee (IEEE 802.15.4) [103], Long-Range Wide Area Network (LoRaWAN) [108], Ultra Wide Band (UWB) [109], IEEE 802.11ah [110], or Wi-Fi Direct [111]. Table 3 shows a comparison on the main characteristics of the latest communications technologies for DL-UAVs [4], indicating their frequency band, coverage, data rate, power consumption, and potential applications.…”

Section: Communications Subsystemmentioning

confidence: 99%

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

et al. 2019

View full text Add to dashboard Cite

Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed. Furthermore, this article enumerates the most relevant open challenges for current DL-UAV solutions, thus allowing future researchers to define a roadmap for devising the new generation affordable autonomous DL-UAV IoT solutions.

show abstract

“…They enable effective responses in areas like disaster zones, farming, reconnaissance, environmental observation and monitoring, meteorological observation and research, and mineral exploitation etc. [10] - [12]. Key for swift and flexible mobility controllability and operation of flying robots is the wireless communications for which antennas are an essential element.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Planar Inkjet-Printed Antennas on a Low-Cost Origami Flapping Robot

et al. 2020

View full text Add to dashboard Cite

An investigation on antenna solutions for expendable origami paper flapping robots is presented. An origami flapping robotic bird that can be produced using standard A4 paper is employed. Antennas resonating at the two commonly used frequency bands, 2.4 GHz and 5.2 GHz, are designed for the limited space available on the folded origami structure. Two developments of the same dual-band monopole antenna are discussed. The first antenna is located on the robot's spine and the second on its tail. A space diversity configuration is also studied. The antennas are printed directly onto a photo paper substrate and then folded into an origami robotic crane's body structure. An ordinary desktop inkjet printer fitted with silver nanoparticle conductive ink cartridges has been employed. CST Microwave Studio TM has been used to design the antennas. A good agreement between the measured and simulated S11 results is achieved with a reasonable −10 impedance bandwidth realized in the three cases studied. The radiation patterns are omnidirectional in the XZ plane which is desirable for the specific application. The diversity configuration has a mutual coupling of < −23 and a gain of 1.4 dB and 2.8 dB at 2.4 GHz and 5.2 GHz respectively. The aim is to provide a new vision for antennas embedded into expandable flying robots based on traditional origami structures.

show abstract

Cognitive Chaotic UWB-MIMO Detect-Avoid Radar for Autonomous UAV Navigation

Cited by 23 publications

References 39 publications

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

Evaluation of Planar Inkjet-Printed Antennas on a Low-Cost Origami Flapping Robot

Contact Info

Product

Resources

About