Three-dimensional (3D) Dynamic Obstacle Perception in a Detect-and-Avoid Framework for Unmanned Aerial Vehicles

Lim, Catrina; Li, Boyang; Ng, Ee Meng; Liu, Xin; Low, Kin Huat

doi:10.1109/icuas.2019.8797844

Cited by 9 publications

(3 citation statements)

References 22 publications

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“…In this work, potential collisions and occlusions are not considered, like, e.g., in [4], [17], since they do not rely on perception but rather use the apriori knowledge of the positions of these obstacles, and thus were not a critical point to stress according to our objectives. It will be the focus of future work with an actual sensor in the loop.…”

Section: Discussionmentioning

confidence: 99%

Perception-constrained and Motor-level Nonlinear MPC for both Underactuated and Tilted-propeller UAVS

Jacquet

Corsini

Bicego

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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In this paper, we present a Perception-constrained Nonlinear Model Predictive Control (NMPC) framework for the real-time control of multi-rotor aerial vehicles. Our formulation considers both constraints from a perceptive sensor and realistic actuator limitations that are the rotor minimum and maximum speeds and accelerations. The formulation is meant to be generic and considers a large range of multi-rotor platforms (such as underactuated quadrotors or tilted-propellers hexarotors) since it does not rely on differential flatness for the dynamical equations, and a broad range of sensors, such as cameras, lidars, etc... The perceptive constraints are expressed to maintain visibility of a feature point in the sensor's field of view, while performing a reference maneuver. We demonstrate both in simulation and real experiments that our framework is able to exploit the full capabilities of the multi-rotor, to achieve the motion under the aforementioned constraints, and control in real-time the platform at a motor-torque level, avoiding the use of an intermediate unconstrained trajectory tracker.

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Section: Discussionmentioning

confidence: 99%

Perception-constrained and Motor-level Nonlinear MPC for both Underactuated and Tilted-propeller UAVS

Jacquet

Corsini

Bicego

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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“…The detect and avoid (DAA) process can be resumed in three main steps: sense, detect and avoid [33]. The execution of the sense step is carried out by sensors which collect the spatial locations of obstacles, their speed and their rate of acceleration or deceleration [35]. Based on this information, the obstacles likely to collide with the UAV are identified in the detect step.…”

Section: Detect and Avoidmentioning

confidence: 99%

Decomposition and Modeling of the Situational Awareness of Unmanned Aerial Vehicles for Advanced Air Mobility

Kamkuimo,

Magalhaes,

Zrelli

et al. 2023

Drones

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The use of unmanned aerial aircrafts (UAVs) is governed by strict regulatory frameworks that prioritize safety. To guarantee safety, it is necessary to acquire and maintain situational awareness (SA) throughout the operation. Existing Canadian regulations require pilots to operate their aircrafts in the visual line-of-sight. Therefore, the task of acquiring and maintaining SA primary falls to the pilots. However, the development of aerial transport is entering a new era with the adoption of a highly dynamic and complex system known as advanced air mobility (AAM), which involves UAVs operating autonomously beyond the visual line-of-sight. SA must therefore be acquired and maintained primarily by each UAV through specific technologies and procedures. In this paper, we review these technologies and procedures in order to decompose the SA of the UAV in the AAM. We then use the system modeling language to provide a high-level structural and behavioral representation of the AAM as a system having UAV as its main entity. In a case study, we analyze one of the flagship UAVs of our industrial partner. Results show that this UAV does not have all of the technologies and methodologies necessary to achieve all of the identified SA goals for the safety of the AAM. This work is a theoretical framework intended to contribute to the realization of the AAM, and we also expect to impact the future design and utilization of UAVs.

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“…Particularly the signal-tonoise ratio of the produced data and the detection range stand out. Other work with UAV-mounted mmWave radars focuses on general depth perception [20], [21] and detection of other UAVs [22], [23]. Additionally, mmWave radars have been extensively studied in driver assistance and autonomous car research [24], [25], [26].…”

Section: Related Workmentioning

confidence: 99%

Onboard Powerline Perception System for UAVs Using mmWave Radar and FPGA-Accelerated Vision

2022

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Autonomous Unmanned Aerial Vehicle (UAV) interactions with powerlines, such as close-up inspections for fault detection or grasping and landing for recharging, require advanced onboard perception capabilities. To solve such tasks, the UAV must be equipped with perception abilities that allow it to navigate between powerlines and safely approach specific cables of interest. A perception system with such capabilities requires state-of-the-art sensor technologies and data processing while still being subject to the limited hardware and energy resources of the UAV. In this paper, we present an advanced embedded system based on the cutting-edge Multiprocessing System-on-Chip (MPSoC) for onboard UAV powerline perception. Our platform consists of a mmWave radar and an RGB camera with data processing carried out on the MPSoC, covering both CPU and Field-Programmable Gate Array (FPGA) computations. Following hardware-software co-design methodology, the heavy image processing tasks are accelerated in the FPGA and fused with computationally light mmWave data on the CPU, facilitating pose-estimation of the power lines. Utilizing the open-source autonomy frameworks PX4 and ROS2, we demonstrate integration of the system with onboard path planning based on the estimated cable positions. The robustness of the detection and pose-estimation methods have been demonstrated in several tests performed both in simulated and real-world powerline environments. The results show that our proposed perception system allows the UAV to safely navigate in close proximity to powerlines, by perceiving more individual cables at longer distances compared to previous work, while remaining lightweight, power-efficient, and low-cost.

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Three-dimensional (3D) Dynamic Obstacle Perception in a Detect-and-Avoid Framework for Unmanned Aerial Vehicles

Cited by 9 publications

References 22 publications

Perception-constrained and Motor-level Nonlinear MPC for both Underactuated and Tilted-propeller UAVS

Perception-constrained and Motor-level Nonlinear MPC for both Underactuated and Tilted-propeller UAVS

Decomposition and Modeling of the Situational Awareness of Unmanned Aerial Vehicles for Advanced Air Mobility

Onboard Powerline Perception System for UAVs Using mmWave Radar and FPGA-Accelerated Vision

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