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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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Proceedings ArticleDOI
10 Dec 2002
TL;DR: This paper discusses techniques to predict the dynamic vehicle response to various natural obstacles and opens up a new area of obstacle negotiation for UGVs, where the vehicle moves over certain obstacles, rather than avoiding them, thereby resulting in more effective achievement of objectives.
Abstract: We discuss techniques to predict the dynamic vehicle response to various natural obstacles. This method can then be used to adjust the vehicle dynamics to optimize performance (e.g. speed) while ensuring that the vehicle is not damaged. This capability opens up a new area of obstacle negotiation for UGVs, where the vehicle moves over certain obstacles, rather than avoiding them, thereby resulting in more effective achievement of objectives. Robust obstacle negotiation and vehicle dynamics prediction requires several key technologies that are discussed in this paper. We detect and segment (label) obstacles using a novel 3D obstacle algorithm. The material of each labelled obstacle (rock, vegetation, etc) is then determined using a texture or color classification scheme. Terrain load-bearing surface models are then constructed using vertical springs to model the compressibility and traversability of each obstacle in front of the vehicle. The terrain model is then combined with the vehicle suspension model to yield an estimate of the maximum safe velocity, and predict the vehicle dynamics as the vehicle follows a path. This end-to-end obstacle negotiation system is envisioned to be useful in optimized path planning and vehicle navigation in terrain conditions cluttered with vegetation, bushes, rocks, etc. Results on natural terrain with various natural materials are presented.

66 citations

Journal ArticleDOI
TL;DR: Outcomes of simulation flight experiments indicated that the UAV can autonomously determine optimal obstacle avoidance strategy and generate distance-minimized flight path based on the results of RGB-D information fusion.

66 citations

Patent
Masato Kageyama1
03 Nov 1999
TL;DR: In this paper, an obstacle is detected and its position is stored in obstacle memory unit (41) on the assumption that the obstacle is common to a plurality of vehicles (2, 2 ).
Abstract: If an obstacle ( 74 ) is detected, its position is stored in obstacle memory unit ( 41 ) on the assumption that the obstacle ( 74 ) is common to a plurality of vehicles ( 2, 2 ). As vehicles ( 2, 2 ) pass by, the content of the obstacle memory unit ( 44 ) is updated. When the vehicles ( 2, 2 ) are supplied with position data of the respective goal point ( 72, 72 ), the vehicles ( 2, 2 ) are guided by their goal point ( 72, 72 ) in accordance with the content of the obstacle memory unit ( 41 ) so that they can avoid the obstacle ( 74 ). Vehicles can be thus guided to avoid obstacles by knowing the existence of obstacles in the working sites where the positions of obstacles are always different.

66 citations

Journal ArticleDOI
TL;DR: Regulations and threat to privacy & security are the most critical barriers to implement drones in logistics sector, while public perception & psychological, environmental, technical issues, and economic aspects are the other identified critical barriers.
Abstract: Companies are adopting innovative methods for responsiveness and efficiency in the logistics sector The implementation of drones in logistics sector is a move in this direction Potential obstacle

65 citations

Journal ArticleDOI
TL;DR: A deep stacked auto-encoders (DSA) model that combines the greedy learning features with the dimensionality reduction capacity and employs an unsupervised nearest neighbors (KNN) algorithm to accurately and reliably detect the presence of obstacles in urban environment is proposed.
Abstract: Obstacle detection is an essential element for the development of intelligent transportation systems so that accidents can be avoided. In this paper, we propose a stereovision-based method for detecting obstacles in urban environment. The proposed method uses a deep stacked auto-encoders (DSA) model that combines the greedy learning features with the dimensionality reduction capacity and employs an unsupervised $k$ -nearest neighbors (KNN) algorithm to accurately and reliably detect the presence of obstacles. We consider obstacle detection as an anomaly detection problem. We evaluated the proposed method by using practical data from three publicly available data sets, the Malaga stereovision urban data set, the Daimler urban segmentation data set, and the Bahnhof data set. Also, we compared the efficiency of DSA-KNN approach to the deep belief network-based clustering schemes. Results show that the DSA-KNN is suitable to visually monitor urban scenes.

65 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