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.

A vision-based obstacle detection system for Unmanned Surface Vehicle

Abstract: This paper describes a vision-based obstacle detection system for Unmanned Surface Vehicle (USV) towards the aim of real-time and high performance obstacle detection on the sea surface. By using both the monocular and stereo vision methods, the system offers the capacity of detecting and locating multiple obstacles in the range from 30 to 100 meters for high speed USV which runs at speeds up to 12 knots. Field tests in the real scenes have been taken and the obstacle detection system for USV is proven to provide stable and satisfactory performance.

Guiding locomotion in complex, dynamic environments

Abstract: Locomotion in complex dynamic environments is an integral part of many daily activities, including walking in crowded spaces, driving on busy roadways, and playing sports. Many of the tasks that humans perform in such environments involve interactions with moving objects -- that is, they require people to coordinate their own movement with the movements of other objects. A widely adopted framework for research on the detection, avoidance, and interception of moving objects is the bearing angle model, according to which observers move so as to keep the bearing angle of the object constant for interception and varying for obstacle avoidance. The bearing angle model offers a simple, parsimonious account of visual control but has several significant limitations and does not easily scale up to more complex tasks. In this paper, I introduce an alternative account of how humans choose actions and guide locomotion in the presence of moving objects. I show how the new approach addresses the limitations of the bearing angle model and accounts for a variety of behaviors involving moving objects, including (1) choosing whether to pass in front of or behind a moving obstacle, (2) perceiving whether a gap between a pair of moving obstacles is passable, (3) avoiding a collision while passing through single or multiple lanes of traffic, (4) coordinating speed and direction of locomotion during interception, (5) simultaneously intercepting a moving target while avoiding a stationary or moving obstacle, and (6) knowing whether to abandon the chase of a moving target. I also summarize data from recent studies that support the new approach.

High-speed autonomous obstacle avoidance with pushbroom stereo

Abstract: We present the design and implementation of a small autonomous unmanned aerial vehicle capable of high-speed flight through complex natural environments. Using only onboard GPS-denied sensing and computation, we perform obstacle detection, planning, and feedback control in real time. We present a novel integrated approach to perception and control using pushbroom stereo, which exploits forward motion to enable efficient obstacle detection and avoidance using lightweight processors on an unmanned aerial vehicle. Our use of model-based planning and control techniques allows us to track precise trajectories that avoid obstacles identified by the vision system. We demonstrate a complete working system detecting obstacles at 120 Hz and avoiding trees at up to 14 m/s (31 MPH). To the best of our knowledge, this is the fastest lightweight aerial vehicle to perform collision avoidance using three-dimensional geometric information.

Rear view monitor device for vehicle

Abstract: PURPOSE:To provide a driver with a real feeling of distance to ensure safety in the driver's operation of backing up by electrically indicating several sets of markers in an overlapping manner on a TV monitor screen, and changing the position of markers suitably in response to the existing situation. CONSTITUTION:When there is an obstacle 11 in the rear of a vehicle, a distance sensor 6 measures the distance from the vehicle to the obstacle 11, and sends the data to CPU8 of a marker signal generating circuit 7. The CPU8 compares the data with indicated data memorized by ROM9. The indicated data responsive to the ROM9 are outputted to a monitor TV 2 through an interface 10. The obstacle 11 in the rear is shown on a screen 3 of the TV, and a marker 4 is also overlapped on it. When the obstacle comes closer by reversing the vehicle, a marker 4 remote from the obstacle 11 out of the marker 4 is erased. Therefore, the distance to the obstacle 11 can be recognized accurately.

Q learning algorithm based UAV path learning and obstacle avoidence approach

Abstract: As Unmanned Aerial Vehicle (UAV) having been applied in more complex and adverse environments, the requirements of automatic techniques for obstacle avoidance are becoming more and more important. Reinforcement learning (RL) is a well-known technique in the domain of Machine Learning (ML), which interacts with the environment and learning the knowledge without the requirement of massive priori training samples. Thus it is attractive to implement the idea of RL to support UAV tasks in unknown environments. This paper adopts an Adaptive and Random Exploration approach (ARE) to accomplish both the tasks of UAV navigation and obstacle avoidance. Search mechanisms will be conducted to guide the UAV escape to a proper path. Simulations on different scenarios show that our approach can effectively guide UAVs to reach their targets in quite rational paths.