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Obstacle

About: Obstacle is a research topic. Over the lifetime, 9517 publications have been published within this topic receiving 94760 citations. The topic is also known as: impediment & barrier.


Papers
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Journal ArticleDOI
TL;DR: The simulation results showed that the proposed flocking control algorithm provides a better tracking effect and consensus for the UAV swarm when avoiding obstacles.
Abstract: In recent years, with the development of the unmanned aerial vehicle (UAV) and battlefield environments, the UAV swarm has attracted significant research attention. To solve problems regarding poor state consensus among swarm individuals due to a small number of individuals easily falling into local minima upon encountering an obstacle, this paper proposes a flocking obstacle avoidance algorithm with local interaction of obstacle information. To make the UAV swarm follow the desired trajectory with better state consensus, we improved the flocking control algorithm of agents according to the characteristics and requirements of the UAV swarm. The obstacle avoidance algorithm for the UAV swarm is based on Olfati-Saber’s multi-agent obstacle avoidance algorithm. The proposed method has individuals in the swarm communicate obstacle information with their neighbors, and we present a simple analysis of this method. The method improves the cooperative obstacle avoidance capability of the flocking control algorithm. The simulation results showed that the proposed flocking control algorithm provides a better tracking effect and consensus for the UAV swarm when avoiding obstacles.

26 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a new encoder-decoder structured deep semantic segmentation network, which is a water obstacle detection network based on image segmentation (WODIS), to solve the problem of poor detection for small objects, low estimation accuracy caused by water surface reflection, and a high rate of false positive on water-sky interference.
Abstract: A reliable obstacle detection system is crucial for autonomous surface vehicles (ASVs) to realize fully autonomous navigation with no need of human intervention. However, the current detection methods have particular drawbacks, such as poor detection for small objects, low estimation accuracy caused by water surface reflection, and a high rate of false-positive on water-sky interference. Therefore, we propose a new encoder–decoder structured deep semantic segmentation network, which is a water obstacle detection network based on image segmentation (WODIS), to solve the abovementioned problems. The first design feature of WODIS utilizes the use of an encoder network to extract high-level data based on different sampling rates. In order to improve obstacle detection at sea–sky line areas, an attention refine module (ARM) activated by both global average pooling and max pooling to capture high-level information has been designed and integrated into WODIS. In addition, a feature fusion module (FFM) is introduced to help concatenate the multidimensional high-level features in the decoder network. The WODIS is tested and cross-validated using four different types of maritime datasets with the results demonstrating that the mean intersection over union (mIoU) of WODIS can achieve superior segmentation effects for sea-level obstacles to values as high as 91.3%.

26 citations

Patent
Xavier Groult1
26 Jan 2010
TL;DR: In this article, the authors present a system for monitoring the environment of a vehicle capable of moving according to a path, which comprises the steps of calculating a plurality of critical levels of driving depending on detection sensors, parameters related to the vehicle and an obstacle being in the environment.
Abstract: A device, program and method for monitoring the environment of a vehicle capable of moving according to a path. The device, program and method comprises the steps of calculating a plurality of critical levels of driving depending on detection sensors, parameters related to the vehicle and an obstacle being in the environment of the vehicle, determining if an obstacle is on the path of the vehicle and depending on the calculated critical levels of driving and determination of an obstacle on the path of the vehicle, making a decision concerning the braking of the vehicle.

26 citations

Proceedings ArticleDOI
01 Aug 2016
TL;DR: The experiments result indicates that the proposed method is able to estimate well the distance between wheelchair and obstacle and later, the smart wheelchair needs to decide whether it should turn left, right or just walk straight when facing certain obstacle to avoid it.
Abstract: The aim of the research is to present an approach of obstacle distance estimation and navigation for smart wheelchair. The smart wheelchair is an electric wheelchair equipped with camera and line laser to navigate and avoid an obstacle. The camera was used to capture images from the environment to sense the pathway condition. The line laser was used in combination with camera to recognize an obstacle in the pathway based on the shape of line laser image in certain angle. A blob method detection was applied on the line laser image to recognize the pattern of the detected obstacles. The line laser projector and camera were mounted in fixed-certain position to make sure a fixed relation between blobs-gaps and obstacle-to-wheelchair distance. A simple linear regression from 16 obtained data was used to represent this relation as the estimated obstacle distance. As a result, the average error between the estimation and actual distance was 1.25 cm from 7 data testing experiments. The experiments result indicates that the proposed method is able to estimate well the distance between wheelchair and obstacle. Later, the smart wheelchair needs to decide further action whether it should turn left, right or just walk straight when facing certain obstacle to avoid it.

26 citations

Proceedings ArticleDOI
10 Jun 2009
TL;DR: A nonlinear guidance law based on movement of obstacles in the camera field-of-view to “push” the obstacles to the edge of the camera screen to avoid collision and thus avoid collision is proposed.
Abstract: Traditional sensors for obstacle detection on aircraft, such as radar, are too heavy for fixed wing micro air vehicles and make obstacle detection and avoidance a difficult problem. EO/IR cameras are small and lightweight enough to offer an alternative to heavy sensors. Vision processing available on a ground station provides range and bearing to nearby obstacles. We propose a nonlinear guidance law based on movement of obstacles in the camera field-of-view to “push” the obstacles to the edge of the camera field-of-view and thus avoid collision. As the MAV passes obstacles, the original course is resumed. The guidance strategy is demonstrated in simulation and flight test. This reactive method intended for use in conjunction with high level path planners.

26 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20231,483
20223,389
2021407
2020817
2019873