Obstacle Recognition
Obstacle Recognition - Related Videos
09:29
A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision
0 Views •
Cited by 26 •
2014
We describe an indoor, portable, standardized course that can be used to evaluate obstacle avoidance in persons who have ultralow vision. The course is relatively inexpensive, simple to administer, and has been shown to be reliable and reproducible.
09:43
Reversible Cooling-induced Deactivations to Study Cortical Contributions to Obstacle Memory in the Walking Cat
0 Views •
Cited by 3 •
2017
Complex locomotion in naturalistic environments requiring careful coordination of the limbs involves regions of the parietal cortex. The following protocol describes the use of reversible cooling-induced deactivation to demonstrate the role of parietal area 5 in memory-guided obstacle avoidance in the walking cat.
11:55
Physiological Correlates of Emotion Recognition
0 Views •
2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California The autonomic nervous system (ANS) controls the activity of the body's internal organs and regulates changes in their activity depending on the current environment. The vagus nerve, which innervates many of the internal organs, is an important part of the system. When our brain senses danger, vagal tone is inhibited, leading to a set of changes in the body designed to make us more prepared to fight or...
06:58
Behavioral Tasks for Examining Identity Recognition In Mice
0 Views •
2025
By pairing negatively or positively valenced experiences with individual social targets, we developed behavior tasks for examining the identity recognition of C57BL/6 mice. These tasks allow for the study of mechanisms of social memory of individual social targets in healthy and disease-related mouse models with impaired social cognition.
01:22
Revenue Recognition
0 Views •
2025
The revenue recognition principle is a central guideline in accrual accounting that dictates when revenue should be recorded in financial statements. It requires that revenue be recognized when a company satisfies a performance obligation, meaning when it has delivered goods or completed services as agreed in a contract, regardless of when the customer pays. This principle ensures that income is reported in the accounting period in which it is actually earned, providing a more accurate...
FAQs
Obstacle recognition depends on linking raw measurements to a perception decision. Sensor data are processed so algorithms can separate objects or hazards from the surrounding environment, then estimate each detected item’s position, distance, and movement. Those estimates turn observations into information that downstream navigation, collision avoidance, and path-planning functions can use.
Combining cameras, lidar, radar, ultrasonic devices, and other measurement systems provides a broader basis for perception than relying on one data source alone. In an engineering system, the resulting sensor data are interpreted together to distinguish obstacles in changing or cluttered environments. This supports more dependable decisions when a vehicle or robot must interact with infrastructure or people.
These estimates allow an automated system to reason about whether an object may interfere with its current or intended operation. Position indicates where the obstacle is, distance indicates its separation, and movement indicates how the situation may change. Together, they support navigation, collision avoidance, and path planning rather than detection alone.
A practical workflow begins by collecting measurements from one or more sensing systems. Perception algorithms then distinguish likely obstacles from the surrounding environment and estimate their position, distance, and movement. The system passes those results to navigation, collision-avoidance, or path-planning functions, which can use the information to select safer interactions or movement.
Obstacle recognition becomes particularly important when environments change or contain many nearby objects. Vehicles and robots can use the resulting perception information while navigating, whereas industrial automation systems can use it around infrastructure and assistive technologies can use it during interaction with people. In each case, recognition contributes to safer, more autonomous operation.
Within engineering, the technique connects measurement hardware with autonomous decision-making. Its outputs help systems operate without treating the environment as static or empty, supporting safe interaction with people and infrastructure. Applications span robotics, autonomous vehicles, industrial automation, and assistive technologies, where reliability affects operational safety, autonomy, and performance.