Obstacle Avoidance for Low-Cost UAVs

Aguilar, Wilbert G.; Casaliglla, Veronica P.; Polit, Jose L.

doi:10.1109/icsc.2017.96

Cited by 17 publications

(9 citation statements)

References 26 publications

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“…Kong et al ( 2014 ) proposed edge detection to detect the boundary of potential obstacles in an image. Saha et al ( 2014 ) and Aguilar et al ( 2017 ) used feature detection techniques in order to extract potential obstacles from images. Alvarez et al ( 2016 ) used consecutive images to extract a depth map (a technique known as “motion parallax”), albeit the accuracy of this method is dependent on the ego-motion estimation of the quadrotor.…”

Section: Local Ego-state Estimation and Controlmentioning

confidence: 99%

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

et al. 2020

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This work presents a review and discussion of the challenges that must be solved in order to successfully develop swarms of Micro Air Vehicles (MAVs) for real world operations. From the discussion, we extract constraints and links that relate the local level MAV capabilities to the global operations of the swarm. These should be taken into account when designing swarm behaviors in order to maximize the utility of the group. At the lowest level, each MAV should operate safely. Robustness is often hailed as a pillar of swarm robotics, and a minimum level of local reliability is needed for it to propagate to the global level. An MAV must be capable of autonomous navigation within an environment with sufficient trustworthiness before the system can be scaled up. Once the operations of the single MAV are sufficiently secured for a task, the subsequent challenge is to allow the MAVs to sense one another within a neighborhood of interest. Relative localization of neighbors is a fundamental part of self-organizing robotic systems, enabling behaviors ranging from basic relative collision avoidance to higher level coordination. This ability, at times taken for granted, also must be sufficiently reliable. Moreover, herein lies a constraint: the design choice of the relative localization sensor has a direct link to the behaviors that the swarm can (and should) perform. Vision-based systems, for instance, force MAVs to fly within the field of view of their camera. Range or communication-based solutions, alternatively, provide omni-directional relative localization, yet can be victim to unobservable conditions under certain flight behaviors, such as parallel flight, and require constant relative excitation. At the swarm level, the final outcome is thus intrinsically influenced by the on-board abilities and sensors of the individual. The real-world behavior and operations of an MAV swarm intrinsically follow in a bottom-up fashion as a result of the local level limitations in cognition, relative knowledge, communication, power, and safety. Taking these local limitations into account when designing a global swarm behavior is key in order to take full advantage of the system, enabling local limitations to become true strengths of the swarm.

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Section: Local Ego-state Estimation and Controlmentioning

confidence: 99%

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

et al. 2020

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“…As a future work, we extrapolate video freeze detection to onboard applications as people detection [28] [29], navigation [30] [31] [32], obstacle avoidance [33] [34] [35], and mapping [36].…”

Section: Discussionmentioning

confidence: 99%

Motion intention optimization for multirotor robust video stabilization

Aguilar

Ángulo

Pardo

2017

2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

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Abstract-In this paper we present an optimization algorithm for simultaneously detecting video freeze and obtaining the minimum number of the frame required in motion intention estimation for real time robust video stabilization on multirotor unmanned aerial vehicles. A combination of a filter and a threshold is used to the video freeze detection, and for optimizing the algorithm, we find the minimum number of frames for motion intention estimation without decrease the performance.

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“…Wilbert G. Aguilar et.al [16] has proposed real-time micro aerial-based object detection and collision avoidance system. In this method, SURF descriptor extracts the obstacle feature points.…”

Section: Related Workmentioning

confidence: 99%

Obstacle avoidance and distance measurement for unmanned aerial vehicles using monocular vision

Aswini

Uma

2019

IJECE

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<span lang="EN-US">Unmanned Aerial Vehicles or commonly known as drones are better suited for "dull, dirty, or dangerous" missions than manned aircraft. The drone can be either remotely controlled or it can travel as per predefined path using complex automation algorithm built during its development. In general, Unmanned Aerial Vehicle (UAV) is the combination of Drone in the air and control system on the ground. Design of an UAV means integrating hardware, software, sensors, actuators, communication systems and payloads into a single unit for the application involved. To make it completely autonomous, the most challenging problem faced by UAVs is obstacle avoidance. In this paper, a novel method to detect frontal obstacles using monocular camera is proposed. Computer Vision algorithms like Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) are used to detect frontal obstacles and then distance of the obstacle from camera is calculated. To meet the defined objectives, designed system is tested with self-developed videos which are captured by DJI Phantom 4 pro.</span>

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Obstacle Avoidance for Low-Cost UAVs

Cited by 17 publications

References 26 publications

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

Motion intention optimization for multirotor robust video stabilization

Obstacle avoidance and distance measurement for unmanned aerial vehicles using monocular vision

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