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.

Binary-constrained inversion of a buried cylindrical obstacle from complete and phaseless magnetic fields

Abstract: The investigation herein is two-fold: specialization to a homogeneous obstacle of recent work (Lambert M et al 1998 Inverse Problems 14 1265-83) carried on the retrieval of an inhomogeneous cylindrical obstacle buried in a half-space in the transverse electric (or H) polarization case; extension from the usual case of complete wavefield data (known amplitude and phase) to the more severe case of amplitude-only data (absent phase). The developed inversion method belongs to the class of modified gradient methods. The field distribution (here, the magnetic field) and the distribution of the obstacle parameters (here, the permittivity and conductivity) are simultaneously sought in a search domain . This is done by minimizing a two-component objective function, one of which is characterizing the satisfaction of the wave equations within , the other the data fit. But now the electrical parameters of the sought obstacle are prescribed beforehand; this allows one to equate an appropriate complex-valued contrast function either to 0 (outside the obstacle) or to a known constant (inside). Two variants of a binary-constrained modified gradient algorithm are developed accordingly, tailored to either complete or phaseless data. Numerical experimentation illustrates how they behave in a variety of obstacle configurations, for both exact and erroneous prescribed contrasts.

Vision Navigator: A Smart and Intelligent Obstacle Recognition Model for Visually Impaired Users

Abstract: Vision impairment is a major challenge faced by humanity on a large scale throughout the world. Affected people find independently navigating and detecting obstacles extremely tedious. Thus, a potential solution for accurately detecting obstacles requires an integrated deployment of the Internet of Things and predictive analytics. This research introduces “Vision Navigator,” a novel framework for assisting visually impaired users in obstacle analysis and tracking so that they can move independently. An intelligent stick named “Smart-fold Cane” and sensor-equipped shoes called “Smart-alert Walker” are the main constituents of our proposed model. For object detection and classification, the stick uses a single-shot detection (SSD) mechanism, which is followed by frame generation using the recurrent neural network (RNN) model. Smart-alert Walker is a lightweight shoe that acts as an emergency unit that notifies the user regarding the presence of any obstacle within a short distance range. This intelligent obstacle detection model using the SSD-RNN approach was deployed in real time and its performance was validated in indoor and outdoor environments. The SSD-RNN model computed an optimum accuracy of 95.06% and 87.68% indoors and outdoors, respectively. The model was also evaluated in the context of users’ distance from obstacles. The proposed SSD-RNN model had an accuracy rate of 96.4% and 86.8% for close and distant obstacles, respectively, outperforming other models. Execution time for the SSD-RNN model was 4.82 s with the highest mean accuracy rate of 95.54% considering all common obstacles.

Overcoming obstacles: the effect of obstacles on locomotor performance and behaviour

Abstract: Sprinting and jumping ability are key performance measures that have been widely studied in vertebrates. The vast majority of these studies, however, use methodologies that lack an ecological context by failing to consider the complex habitats in which many animals live. Because successfully navigating obstacles within complex habitats is critical for predator escape, running, climbing, and/or jumping performance are each likely to be exposed to selection. In the present study, we quantify how behavioural strategies and locomotor performance change with increasing obstacle height. Obstacle size had a significant influence on behaviour (e.g. obstacle crossing strategy, intermittent locomotion) and performance (e.g. sprint speed, jump distance). Jump frequency and distance increased with obstacle size, suggesting that it likely evolved because it is more efficient (i.e. it reduces the time and distance required to reach a target position). Jump angle, jump velocity, and approach velocity accounted for 58% of the variation in jump distance on the large obstacle, and 33% on the small obstacle. Although these variables have been shown to significantly influence jump distance in static jumps, they do not appear to be influential in running (dynamic) jumps onto a small obstacle. Because selection operates in simple and complex habitats, future studies should consider quantifying additional measures such as jumping or climbing with respect to the evolution of locomotion performance. © 2012 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, ••, ••–••.

Path planning of mobile robot by mixing experience with modified artificial potential field method

Abstract: In this article, a new method is proposed to help the mobile robot to avoid many kinds of collisions effectively, which combined past experience with modified artificial potential field method. In the process of the actual global obstacle avoidance, system will invoke case-based reasoning algorithm using its past experience to achieve obstacle avoidance when obstacles are recognized as known type; otherwise, it will invoke the modified artificial potential field method to solve the current problem and the new case will also be retained into the case base. In case-based reasoning, we innovatively consider that all the complex obstacles are retrieved by two kinds of basic build-in obstacle models (linear obstacle and angle-type obstacle). Our proposed experience mixing with modified artificial potential field method algorithm has been simulated in MATLAB and implemented on actual mobile robot platform successfully. The result shows that the proposed method is applicable to the dynamic real-time obstacle avo...

Interoperable multi-agent framework for unmanned aerial/ground vehicles: towards robot autonomy

Abstract: Multi-agent architectures for autonomous robots are generally mission and platform oriented. Autonomous robots are commonly employed in patrolling, surveillance, search and rescue and human-hazardous missions. Irrespective of the differences in unmanned aerial and ground robots, the algorithms for obstacle detection and avoidance, path planning and path-tracking can be generalized. Service-oriented interoperable framework for robot autonomy (SOIFRA) proposed in this paper is an interoperable multi-agent framework focusing on generalizing platform-independent algorithms for unmanned aerial and ground vehicles. As obstacle detection and avoidance are standard requirements for autonomous robot operation, platform-independent collision avoidance algorithms are incorporated into SOIFRA. SOIFRA is behaviour based and is interoperable across unmanned aerial and ground vehicles. Obstacle detection and avoidance are performed utilizing computer vision-based algorithms, as these are generally platform independent. Obstacle detection is achieved utilizing Hough transform, Canny contour and Lucas–Kanade sparse optical flow algorithm. Collision avoidance performed utilizing optical flow-based and expansion of object-based time-to-contact demonstrates SOIFRA’s modularity. Experiments performed, utilizing TurtleBot, Clearpath Robotics Husky, AR Drone and Hector-quadrotor, establish SOIFRA’s interoperability across several robotic platforms.