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.

Autonomous obstacle avoidance of an unmanned surface vehicle based on cooperative manoeuvring

Abstract: Purpose Autonomous obstacle avoidance is important in unmanned surface vehicle (USV) navigation. Although the result of obstacle detection is often inaccurate because of the inherent errors of LIDAR, conventional methods typically emphasize on a single obstacle-avoidance algorithm and neglect the limitation of sensors and safety in a local region. Conventional methods also fail in seamlessly integrating local and global obstacle avoidance algorithms. This paper aims to present a cooperative manoeuvring approach including both local and global obstacle avoidance. Design/methodology/approach The global algorithm used in our USV is the Artificial Potential Field-Ant Colony Optimization (APF-ACO) obstacle-avoidance algorithm, which plans a relative optimal path on the specified electronic map before the cruise of USV. The local algorithm is a multi-layer obstacle-avoidance framework based on a single LIDAR to present an efficient solution to USV path planning in the case of sensor errors and collision risks. When obstacles are within a layer, the USV uses a corresponding obstacle-avoidance algorithm. Then the USV moves towards the global direction according to fuzzy rules in the fuzzy layer. Findings The presented method offers a solution for obstacle avoidance in a complex environment. The USV follows the global trajectory planed by the APF-ACO algorithm. While, the USV can bypass current obstacle in the local region based on the multi-layer method effectively. This fact was validated by simulations and field trials. Originality/value The method presented in this paper takes advantage of algorithm integration that remedies errors of obstacle detection. Simulation and experiments were also conducted for performance evaluation.

A Stereovision Method for Obstacle Detection and Tracking in Non-Flat Urban Environments

Abstract: Obstacle detection is an essential capability for the safe guidance of autonomous vehicles, especially in urban environments. This paper presents an efficient method to integrate spatial and temporal constraints for detecting and tracking obstacles in urban environments. In order to enhance the reliability of the obstacle detection task, we do not consider the urban roads as rigid planes, but as quasi-planes, whose normal vectors have orientation constraints. Under this flexible road model, we propose a fast, robust stereovision based obstacle detection method. A watershed transformation is employed for obstacle segmentation in dense traffic conditions, even with partial occlusions, in urban environments. Finally a UKF (Unscented Kalman filter) is applied to estimate the obstacles parameters under a nonlinear observation model. To avoid the difficulty of the computation in metric space, the whole detection process is performed in the disparity image. Various experimental results are presented, showing the advantages of this method.

Inverse problems for acoustic waves using the penalised likelihood method

Abstract: The authors consider the following inverse problem for scattering of time-harmonic waves by an impenetrable obstacle. Given data obtained from the scattered field at infinity, they determine the location of the boundary of the obstacle. To solve the forward scattering problem, they use the T matrix method, or null field approach. The ill-posedness of the inverse problem is discussed. To solve the inverse problem, an optimal control, or 'data fitting', approach is used, and stable approximate solutions are obtained using a technique known as the penalised likelihood method. Results of a numerical study are presented, showing the effects on the accuracy of the inversion of different types of far field data, various incident fields, and random error in the data. In two dimensions, several different obstacle shapes are considered. An example for an axially symmetric obstacle in three dimensions is also presented.

Self-reconstruction of partially coherent light beams scattered by opaque obstacles.

Abstract: Self-reconstruction refers to an ability of certain fully coherent optical beams to recover their spatial profiles after scattering by obstacles. In this communication, we extend the self-reconstruction concept to partially coherent beams. We show theoretically and verify experimentally that any partially coherent beam can self-reconstruct its intensity profile and state of polarization upon scattering from an opaque obstacle provided the beam coherence area is reduced well below the obstacle area. We stress that our self-reconstruction technique is independent of the obstacle shape and it is scalable to the case of multiple obstacles or even of inhomogeneous media as long as a characteristic obstacle area or a medium inhomogeneity scale is well in excess of the beam coherence area or length, respectively. We anticipate the technique to be instrumental in applications ranging from beam shaping to image transfer and trapped particle manipulation in turbid media.