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.

Co-representation of others' task constraints in joint action.

Abstract: Previous research has demonstrated that humans tend to represent each other's tasks even if no interpersonal coordination is required. The present study asked whether coactors in a joint action rely on task co-representation to achieve temporal coordination even if this implies increased movement effort for an unconstrained actor. Pairs of participants performed reaching movements back and forth between two targets, with the aim of synchronizing their landing times. One of the participants needed to move over an obstacle while the other had no obstacle. The results of four experiments showed that the participant without obstacle moved as if an obstacle was obstructing her way. Further amplifying the demands on interpersonal coordination led to a significant increase of this effect, indicating that unconstrained actors represented their coactor's task constraint and adjusted their own actions accordingly, particularly under high coordination demands. The findings also showed that unconstrained actors represented the object property constraining their coactor's movement rather than parameters of this movement. We conclude that joint action partners rely on task co-representation to achieve temporal coordination in a task with asymmetric task constraints. (PsycINFO Database Record

Robot crowd navigation using predictive position fields in the potential function framework

Abstract: A potential function based path planner for a mobile robot to autonomously navigate an area crowded with people is proposed. Path planners based on potential functions have been essentially static, with very limited representation of the motion of obstacles as part of their navigation model. The static formulations do not take into account the possibility of using predicted workspace configuration to augment the performance of the path planner. The use of an elliptical region signifying the predicted position and direction of motion of an obstacle is proposed in this paper. The repulsive potential caused by an obstacle is defined relative to this elliptical field. An analytic switch is made when the robot enters this predicted elliptical zone of the obstacle. The development of navigation functions makes it possible to design a potential-based planner which is guaranteed to converge to the target.

Radar and vision fusion for the real-time obstacle detection and identification

Abstract: The purpose of this paper is the presentation and research of a novel sensor fusion-based system for obstacle detection and identification, which uses the millimeter-wave radar to detect the position and velocity of the obstacle. Afterwards, the image processing module uses the bounding box regression algorithm in deep learning to precisely locate and identify the obstacles.,Unlike the traditional algorithms that use radar and vision to detect obstacles separately, the purposed method of this paper uses radar to determine the approximate location of obstacles and then uses bounding box regression to achieve accurate positioning and recognition. First, the information of the obstacles can be acquired by the millimeter-wave radar, and the effective target is extracted by filtering the data. Then, use coordinate system conversion and camera parameter calibration to project the effective target to the image plane, and generate the region of interest (ROI). Finally, based on image processing and machine learning techniques, the vehicle targets in the ROI are detected and tracked.,The millimeter wave is used to determine the presence of an obstacle, and the deep learning algorithm of the image is combined to determine the shape and the class of the obstacle. The experimental results indicate that the detection rate of this method is up to 91.6 per cent, which can better implement the perception of the environment in front of the vehicle.,The originality is based on the combination of millimeter-wave sensors and deep learning. Using the bounding box regression algorithm in RCNN, the ROI detected by radar is analyzed to realize real-time obstacle detection and recognition. This method does not require processing the entire image, greatly reducing the amount of data processing and improving the efficiency of the algorithm.