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.

Fuzzy neural networks for obstacle pattern recognition and collision avoidance of fish robots

Abstract: The problems of detection and pattern recognition of obstacles are the most important concerns for fish robots' path planning to make natural and smooth movements as well as to avoid collision. We can get better control results of fish robot trajectories if we obtain more information in detail about obstacle shapes. The method employing only simple distance measuring IR sensors without cameras and image processing is proposed. The capability of a fish robot to recognize the features of an obstacle to avoid collision is improved using neuro-fuzzy inferences. Approaching angles of the fish robot to an obstacle as well as the evident features such as obstacles' sizes and shape angles are obtained through neural network training algorithms based on the scanned data. Experimental results show the successful path control of the fish robot without hitting on obstacles.

Obstacle avoidance procedure for mobile robots

Abstract: The common set of procedures provided by most of indoor mobile robots consists of Mapping Building, Auto- localization, and Navigation (Path Planning and Obstacle Avoidance). Considering that real indoor environments are commonly dynamic where known obstacles (e.g. chairs, tables, sofas, etc.) and unknown obstacles (persons walking close to the robot, unmapped environment characteristics, etc.) are constantly changing their positions, it is necessary to use sensor data to provide the robot a detailed real-time environmental view. Most of these obstacles may be located on a priori planned path and the robot controller must execute a skilful manoeuvre that avoids a collision and directs the robot to its goal position. Undoubtedly, it is essential that both navigation procedures (path planning and obstacle avoidance) work together and harmoniously, trying to solve possible conflicts between their two different behaviours (go to the goal position as soon as possible and avoid potential collisions during the path). In this article we describe the development of a new obstacle avoidance procedure based on the previous Obstacle Velocity approach. This procedure was denominated as BOA and was tested in a mobile robot in wide and narrow environments. The robot has been equipped with an intelligent control and navigation system and two SICK two-dimensional (2-D) laser range finder LMS 200.

A novel setup method of 3D LIDAR for negative obstacle detection in field environment

Abstract: Negative obstacle detection is an important task for Unmanned Ground Vehicle (UGV) driving safely in field environments. This paper presents a novel 3D LiDAR setup method to deal with this issue. The proposed setup method has two advantages: 1) the blind area near the vehicle is greatly shrunken, which is very important in driving on narrow roads or taking a turning for the field UGV; 2) compared to the traditional uprightly mounted LiDAR, the density of LiDAR data with this novel setup method is greatly improved, which is very useful both for positive and negative obstacle detection. With this new setup, a geometrical character based approach is introduced for the negative obstacle detection. Two cues, the width and the back of the negative obstacle are taken into consideration in this paper. Support Vector Machine (SVM) is employed to classify negative obstacles from the background. Meanwhile, these features are combined under a Bayesian framework. Experimental results show that the proposed setup method is useful and the proposed negative obstacle detection approach is effective.

Monocular Vision as a Range Sensor

Abstract: One of the most important abilities for a mobile robot is detecting obstacles in order to avoid collisions. Building a map of these obstacles is the next logical step. Most robots to date have used sensors such as passive or active infrared, sonar or laser range finders to locate obstacles in their path. In contrast, this work uses a single colour camera as the only sensor, and consequently the robot must obtain range information from the camera images. We propose simple methods for determining the range to the nearest obstacle in any direction in the robot’s field of view, referred to as the Radial Obstacle Profile. The ROP can then be used to determine the amount of rotation between two successive images, which is important for constructing a 360o view of the surrounding environment as part of map construction.