Showing papers on "Obstacle published in 2016"

Nonlinear Control for Tracking and Obstacle Avoidance of a Wheeled Mobile Robot With Nonholonomic Constraint

Abstract: This brief presents a novel control scheme for some problems on tracking and obstacle avoidance of a wheeled mobile robot with nonholonomic constraint. An extended state observer is introduced to estimate the unknown disturbances and velocity information of the wheeled mobile robot. A nonlinear controller is designed to achieve tracking target and obstacle avoidance in complex environments. Note that tracking errors converge to a residual set outside the obstacle detection region. Moreover, the obstacle avoidance is also guaranteed inside the obstacle detection region. Simulation results are given to verify the effectiveness and robustness of the proposed design scheme.

Fast Image-Based Obstacle Detection From Unmanned Surface Vehicles

Abstract: Obstacle detection plays an important role in unmanned surface vehicles (USVs). The USVs operate in a highly diverse environments in which an obstacle may be a floating piece of wood, a scuba diver, a pier, or a part of a shoreline, which presents a significant challenge to continuous detection from images taken on board. This paper addresses the problem of online detection by constrained, unsupervised segmentation. To this end, a new graphical model is proposed that affords a fast and continuous obstacle image-map estimation from a single video stream captured on board a USV. The model accounts for the semantic structure of marine environment as observed from USV by imposing weak structural constraints. A Markov random field framework is adopted and a highly efficient algorithm for simultaneous optimization of model parameters and segmentation mask estimation is derived. Our approach does not require computationally intensive extraction of texture features and comfortably runs in real time. The algorithm is tested on a new, challenging, dataset for segmentation, and obstacle detection in marine environments, which is the largest annotated dataset of its kind. Results on this dataset show that our model outperforms the related approaches, while requiring a fraction of computational effort.

Vision-based Obstacle Detection and Navigation for an Agricultural Robot

Abstract: This paper describes a vision-based obstacle detection and navigation system for use as part of a robotic solution for the sustainable intensification of broad-acre agriculture. To be cost-effective, the robotics solution must be competitive with current human-driven farm machinery. Significant costs are in high-end localization and obstacle detection sensors. Our system demonstrates a combination of an inexpensive global positioning system and inertial navigation system with vision for localization and a single stereo vision system for obstacle detection. The paper describes the design of the robot, including detailed descriptions of three key parts of the system: novelty-based obstacle detection, visually-aided guidance, and a navigation system that generates collision-free kinematically feasible paths. The robot has seen extensive testing over numerous weeks of field trials during the day and night. The results in this paper pertain to one particular 3 h nighttime experiment in which the robot performed a coverage task and avoided obstacles. Additional results during the day demonstrate that the robot is able to continue operating during 5 min GPS outages by visually following crop rows.

Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles

Abstract: Detecting small obstacles on the road ahead is a critical part of the driving task which has to be mastered by fully autonomous cars. In this paper, we present a method based on stereo vision to reliably detect such obstacles from a moving vehicle. The proposed algorithm performs statistical hypothesis tests in disparity space directly on stereo image data, assessing freespace and obstacle hypotheses on independent local patches. This detection approach does not depend on a global road model and handles both static and moving obstacles. For evaluation, we employ a novel lost-cargo image sequence dataset comprising more than two thousand frames with pixelwise annotations of obstacle and free-space and provide a thorough comparison to several stereo-based baseline methods. The dataset will be made available to the community to foster further research on this important topic. The proposed approach outperforms all considered baselines in our evaluations on both pixel and object level and runs at frame rates of up to 20 Hz on 2 mega-pixel stereo imagery. Small obstacles down to the height of 5 cm can successfully be detected at 20 m distance at low false positive rates.

Performance comparison of Infrared and Ultrasonic sensors for obstacles of different materials in vehicle/ robot navigation applications

Abstract: In robotics, Ultrasonic sensors and Infrared sensors are commonly used for distance measurement. These low-cost sensors fundamentally address majority of problems related to the obstacle detection and obstacle avoidance. In this paper, the performance comparison of ultrasonic and infrared measurement techniques across obstacles of different types of materials presented. The Vehicle model integrated with the sensors, moving with constant velocity towards different types of obstacles for capturing the distance parameter. Based on the data acquired from the sensors, correlation analysis of the measured distance with actual distance performed. This analysis will be very much useful, to select the right sensor - Ultrasonic sensor / Infrared sensor or a combination of both sensors, while developing the algorithm for addressing obstacle detection problems. The detection range and inherent properties of sensors (reflection/ absorption etc.) also were tested in this experiment.

Effect of obstacle position in the flow of sheep through a narrow door

Abstract: In a recent work [Phys. Rev. E 91, 022808 (2015)PLEEE81539-375510.1103/PhysRevE.91.022808] it was reported that placing an obstacle in front of a gate has a beneficial effect in the flow of sheep through it. Here, we extend such results by implementing three different obstacle positions. We have observed that the flow is improved in two cases, while it worsens in the other one; the last instance happens when the obstacle is too close to the door. In this situation, the outcomes suggest that clogging develops between the doorjamb and the obstacle, contrary to the cases when the obstacle is farther, in which case clogging always occurs at the very door. The effectiveness of the obstacle (a strategy put forward to alleviate clogging in emergency exits) is therefore quite sensitive to its location. In addition, the study of the temporal evolution of the flow rate as the test develops makes evident a steady behavior during the entire duration of the entrance. This result is at odds with recent findings in human evacuation tests where the flow rate varies over time, therefore challenging the fairness of straightforward comparisons between pedestrian behavior and animal experimental observations.

Multilayered Mapping and Navigation for Autonomous Micro Aerial Vehicles

Abstract: Micro aerial vehicles, such as multirotors, are particularly well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance or disaster management. Key prerequisites for the fully autonomous operation of micro aerial vehicles are real-time obstacle detection and planning of collision-free trajectories. In this article, we propose a complete system with a multimodal sensor setup for omnidirectional obstacle perception consisting of a three-dimensional 3D laser scanner, two stereo camera pairs, and ultrasonic distance sensors. Detected obstacles are aggregated in egocentric local multiresolution grid maps. Local maps are efficiently merged in order to simultaneously build global maps of the environment and localize in these. For autonomous navigation, we generate trajectories in a multilayered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach and the involved components in simulation and with the real autonomous micro aerial vehicle. Finally, we present the results of a complete mission for autonomously mapping a building and its surroundings.

Unmanned aerial vehicle autonomous obstacle detection system and method based on binocular vision

Abstract: The invention relates to an unmanned aerial vehicle autonomous obstacle detection system and method based on binocular vision. The unmanned aerial vehicle autonomous obstacle detection system and method based on the binocular vision are characterized in that the system comprises a binocular visual system, other sensor modules and a flight control system which are mounted on an unmanned aerial vehicle; the method comprises the steps that the binocular visual system acquires visual information of the flight environment of the unmanned aerial vehicle, and obstacle information is obtained through processing; other sensor units acquire state information of the unmanned aerial vehicle; the flight control system receives the obstacle information and the state information of the unmanned aerial vehicle, establishes a flight path and generates a flight control instruction to send to the unmanned aerial vehicle; the unmanned aerial vehicle flies by avoiding obstacles according to the flight control instruction. According to the unmanned aerial vehicle autonomous obstacle detection system and method based on the binocular vision, the vision information is fused with other sensor information, the flight environment information is perceived, flight path control and path planning are conducted to avoid the obstacles, the problem of vision obstacle avoidance of the unmanned aerial vehicle is effectively solved, and the capacity of completing vision obstacle avoidance by means of a vehicle-mounted camera is achieved.

Obstacle detection, tracking and avoidance for a teleoperated UAV

Abstract: In this paper, we present a collision-free indoor navigation algorithm for teleoperated multirotor Unmanned Aerial Vehicles (UAVs). Assuming an obstacle rich environment, the algorithm keeps track of detected obstacles in the local surroundings of the robot. The detection part of the algorithm is based on measurements from an RGB-D camera and a Bin-Occupancy filter capable of tracking an unspecified number of targets. We use the estimate of the robot's velocity to update the obstacles state when they leave the direct field of view of the sensor. The avoidance part of the algorithm is based on the Model Predictive Control approach. By predicting the possible future obstacles states, it filters the operator commands to prevent collisions. The method is validated on a platform equipped with its own computational unit, which makes it self-sufficient in terms of external CPUs.

Obstacle Avoidance Approaches for Autonomous Underwater Vehicle: Simulation and Experimental Results

Abstract: Autonomous underwater vehicles (AUVs) operate in unknown underwater environments and must be able to avoid submerged obstacles such as cliffs, wrecks, and seabed changes. This paper proposes a methodology for obstacle avoidance by AUVs that are equipped with forward-looking sonars (FLSs). The data collected from two FLSs placed in horizontal and vertical orientation are processed in real time to provide obstacle detection information in the $xz$ - and $xy$ -planes, respectively. Due to the necessity of maintaining constant height when employing sidescan sonar (SSS) and lower energy consumption, horizontal avoidance maneuvers are preferred over vertical ones. For the horizontal obstacle avoidance, a two-layer obstacle avoidance algorithm (OAA) contains two different processes, an OAA based on Bandler and Kohout (BK) product of fuzzy relation used as a preplanning method and a reactive approach based on potential field and edge detection methods. The preplanning technique has a clear advantage on a line segment of the path, and the reactive method is more efficient on a curved segment. In case a horizontal approach cannot find a path to safely avoid the obstacle, the reactive vertical approach is activated. A seabed gradient detection technique that allows prediction of seabed and altitude changes up to 40 m ahead of the AUV is presented. Simulation and experimental results clearly demonstrate that the proposed methodology enables AUVs to navigate safely through obstacles and provide crucial information about the seafloor terrain changes.

The obstacle problem for nonlinear integro-differential operators

Abstract: We investigate the obstacle problem for a class of nonlinear equations driven by nonlocal, possibly degenerate, integro-differential operators, whose model is the fractional p-Laplacian operator with measurable coefficients. Amongst other results, we will prove both the existence and uniqueness of the solutions to the obstacle problem, and that these solutions inherit regularity properties, such as boundedness, continuity and Holder continuity (up to the boundary), from the obstacle.

Using Deep Learning to Challenge Safety Standard for Highly Autonomous Machines in Agriculture

Abstract: In this paper, an algorithm for obstacle detection in agricultural fields is presented. The algorithm is based on an existing deep convolutional neural net, which is fine-tuned for detection of a specific obstacle. In ISO/DIS 18497, which is an emerging standard for safety of highly automated machinery in agriculture, a barrel-shaped obstacle is defined as the obstacle which should be robustly detected to comply with the standard. We show that our fine-tuned deep convolutional net is capable of detecting this obstacle with a precision of 99 . 9 % in row crops and 90 . 8 % in grass mowing, while simultaneously not detecting people and other very distinct obstacles in the image frame. As such, this short note argues that the obstacle defined in the emerging standard is not capable of ensuring safe operations when imaging sensors are part of the safety system.

Obstacle detection using a 2D LIDAR system for an Autonomous Vehicle

Abstract: Obstacle detection is a requirement for Advanced Driver Assistance Systems (ADAS) which are the precursors to autonomous vehicle systems. A number of sensor systems have been used before to perform obstacle detection. One particular sensor system is the LIDAR system which is noted for its accuracy in measuring distances. However, most commercially available LIDAR (Light Detection and Ranging) systems are expensive and computationally intensive. This research characterizes an inexpensive 2D LIDAR system using the LIDAR-Lite v1 for use in obstacle detection for autonomous vehicles. Since data acquisition occurs only in a single plane, the system should be computationally fast. The field of vision should be capable of up to 360 degrees. The data acquired was median-filtered and pre-processed by the merging and segmentation of data points. Obstacle detection was then performed via clustering. Results show that obstacles with widths of 1 meter can be detected at distances of 10 meters.

Autonomous off-road navigation using stereo-vision and laser-rangefinder fusion for outdoor obstacles detection

Abstract: During the last decade, ground mobile robots that are able to drive autonomously in off-road environments have received a great deal of attention. Autonomous navigation in unstructured environments faces many new challenges compared to structured urban environments, these challenges increase the complexity of the localization, obstacle detection, path planning and navigation commands. Accordingly this paper presents a fusion system for stereo-vision and laser-rangefinder outdoor obstacle detection, which is implemented as an application for autonomous off-road navigation. The test platform is an electric golf-cart that is modified mechanically and electrically to operate in driver-less mode. This vehicle is equipped with binocular camera, laser-rangefinder, electronic compass and on-board embedded computer, which operates using Robotic Operating System (ROS) architecture. The proposed architecture gathers the data from all different sensors, in order to make navigation decisions from one point to another, avoiding obstacles in the path. Experimental results indicate the high performance of the proposed approaches, they show that the perception from the stereo-vision detection enhances the laser-rangefinder detection, which consequently makes a better decision in maneuvering the obstacle and returns back to the original path.

The obstacle problem for nonlinear integro-differential operators

Abstract: We investigate the obstacle problem for a class of nonlinear equations driven by nonlocal, possibly degenerate, integro-differential operators, whose model is the fractional $p$-Laplacian operator with measurable coefficients. Amongst other results, we will prove both the existence and uniqueness of the solutions to the obstacle problem, and that these solutions inherit regularity properties, such as boundedness, continuity and Holder continuity (up to the boundary), from the obstacle.

A Receding Horizon Control Strategy for Autonomous Vehicles in Dynamic Environments

Abstract: In this brief, a receding horizon control scheme for solving the obstacle avoidance motion planning problem of autonomous vehicles operating in uncertain dynamic environments is developed. By considering robots described by discrete-time linear time-invariant models, we propose a set-theoretic-based architecture to deal with disturbance effects, and input and state constraints. As one of its main merits, the strategy is capable of taking care of time-varying obstacle scenarios by jointly exploiting robust positively invariant region and one-step controllable set concepts. The resulting scheme guarantees uniformly ultimate boundedness and constraints fulfilment regardless of any admissible obstacle configuration.

Mobile robot path planning and obstacle avoidance method and system

Abstract: The invention discloses a mobile robot path planning and obstacle avoidance method and system. The mobile robot path planning method comprises the following steps: establishing a two-dimensional grid map by utilizing known obstacle environment information; in the two-dimensional grid map, establishing a global coordinate system at the place of a mobile robot, and setting a starting point and a terminal point of the mobile robot; determining the shortest path between the starting point and the terminal point through a jump point search algorithm, wherein the shortest path comprises a plurality of local target points connected in sequence; and in the process of controlling the mobile robot to move to each of the local target points, utilizing a local obstacle avoidance algorithm to avoid a dynamic obstacle. The mobile robot path planning and obstacle avoidance method adopts the jump point search algorithm to obtain the shortest path quickly, so that path search efficiency can be improved, and storage space is saved; and through the local obstacle avoidance algorithm, accuracy and real-time performance of mobile robot path planning and obstacle avoidance can be ensured, and autonomous navigation of the mobile robot is realized.

Monocular vision-based obstacle detection/avoidance for unmanned aerial vehicles

Abstract: Robust real-time obstacle detection/avoidance is a challenging problem especially for micro and small aerial vehicles due to the limited number of the on-board sensors due to the battery constraint and low payload. Usually lightweight sensors such as CMOS camera are the best choice comparing with laser or radar sensors. For real-time applications, most studies focus on using stereo cameras to reconstruct a 3D model of the obstacles or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the state of the approaching obstacles using single camera is proposed. During the flight, this method is able to detect the changes of the size area of the obstacles. First, the method detects the feature points of the obstacles, and then extracts the obstacles that has probability of getting close. In addition, by comparing the changes in the area ratios of the obstacle in the image sequence, the method can decide if it is obstacle or not. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV can take the action of avoidance.

A Virtual Blind Cane Using a Line Laser-Based Vision System and an Inertial Measurement Unit

Abstract: A virtual blind cane system for indoor application, including a camera, a line laser and an inertial measurement unit (IMU), is proposed in this paper. Working as a blind cane, the proposed system helps a blind person find the type of obstacle and the distance to it. The distance from the user to the obstacle is estimated by extracting the laser coordinate points on the obstacle, as well as tracking the system pointing angle. The paper provides a simple method to classify the obstacle’s type by analyzing the laser intersection histogram. Real experimental results are presented to show the validity and accuracy of the proposed system.

Unmanned aerial vehicle obstacle detection and avoidance

Abstract: Apparatuses and methods for detecting an obstacle in a path of an Unmanned Aerial Vehicle (UAV) are described herein, including, but not limited to, receiving data from a single image/video capturing device of the UAV, computing a score based on the received data, and performing at least one obstacle avoidance maneuver based on the score.

Anticipatory Postural Control of Stability during Gait Initiation Over Obstacles of Different Height and Distance Made Under Reaction-Time and Self-Initiated Instructions

Abstract: Despite the abundant literature on obstacle crossing in humans, the question of how the central nervous system (CNS) controls postural stability during gait initiation with the goal to clear an obstacle remains unclear. Stabilizing features of gait initiation include anticipatory postural adjustments (APAs) and lateral swing foot placement. To answer the above question, 14 participants initiated gait as fast as possible in three conditions of obstacle height, three conditions of obstacle distance and one obstacle-free (control) condition. Each of these conditions was performed with two levels of temporal pressure: reaction-time (high-pressure) and self-initiated (low-pressure) movements. A mechanical model of the body falling laterally under the influence of gravity and submitted to an elastic restoring force is proposed to assess the effect of initial (foot-off) center-of-mass position and velocity (or "initial center-of-mass set") on the stability at foot-contact. Results showed that the anticipatory peak of mediolateral (ML) center-of-pressure shift, the initial ML center-of-mass velocity and the duration of the swing phase, of gait initiation increased with obstacle height, but not with obstacle distance. These results suggest that ML APAs are scaled with swing duration in order to maintain an equivalent stability across experimental conditions. This statement is strengthened by the results obtained with the mechanical model, which showed how stability would be degraded if there was no adaptation of the initial center-of-mass set to swing duration. The anteroposterior (AP) component of APAs varied also according to obstacle height and distance, but in an opposite way to the ML component. Indeed, results showed that the anticipatory peak of backward center-of-pressure shift and the initial forward center-of-mass set decreased with obstacle height, probably in order to limit the risk to trip over the obstacle, while the forward center-of-mass velocity at foot-off increased with obstacle distance, allowing a further step to be taken. These effects of obstacle height and distance were globally similar under low and high-temporal pressure. Collectively, these findings imply that the CNS is able to predict the potential instability elicited by the obstacle clearance and that it scales the spatiotemporal parameters of APAs accordingly.

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.

Path planning and obstacle avoidance for vision guided quadrotor UAV navigation

Abstract: In this paper, we implement a path planning algorithm and combine with an obstacle avoidance system for a small commercial quadrotor vehicle. To keep the flight safe, the quadrotor navigates with the vision information for obstacle detection. A sequence of images are captured from the onboard camera and used for visual guidance. The optical flow is computed from the images and the derived depth cues are used to detect incoming obstacles. We implement the algorithm on an embedded system and use a single-board computer as the control platform. The experiments carried out in the outdoor environment show the feasibility of our technique for path planning and autonomous navigation.

LiDAR Based Negative Obstacle Detection for Field Autonomous Land Vehicles

Abstract: Negative obstacles for field autonomous land vehicles ALVs refer to ditches, pits, or terrain with a negative slope, which will bring risks to vehicles in travel. This paper presents a feature fusion based algorithm FFA for negative obstacle detection with LiDAR sensors. The main contributions of this paper are fourfold: 1 A novel three-dimensional 3-D LiDAR setup is presented. With this setup, the blind area around the vehicle is greatly reduced, and the density of LiDAR data is greatly improved, which are critical for ALVs. 2 On the basis of the proposed setup, a mathematical model of the point distribution of a single scan line is deduced, which is used to generate ideal scan lines. 3 With the mathematical model, an adaptive matching filter based algorithm AMFA is presented to implement negative obstacle detection. Features of simulated obstacles in each scan line are employed to detect the real negative obstacles. They are supposed to match with features of the potential real obstacles. 4 Grounded on AMFA algorithm, a feature fusion based algorithm is proposed. FFA algorithm fuses all the features generated by different LiDARs or captured at different frames. Bayesian rule is adopted to estimate the weight of each feature. Experimental results show that the performance of the proposed algorithm is robust and stable. Compared with the state-of-the-art techniques, the detection range is improved by 20%, and the computing time is reduced by an order of two magnitudes. The proposed algorithm had been successfully applied on two ALVs, which won the champion and the runner-up in the "Overcome Danger 2014" ground unmanned vehicle challenge of China.

A Monocular Vision Sensor-Based Obstacle Detection Algorithm for Autonomous Robots

Abstract: This paper presents a monocular vision sensor-based obstacle detection algorithm for autonomous robots. Each individual image pixel at the bottom region of interest is labeled as belonging either to an obstacle or the floor. While conventional methods depend on point tracking for geometric cues for obstacle detection, the proposed algorithm uses the inverse perspective mapping (IPM) method. This method is much more advantageous when the camera is not high off the floor, which makes point tracking near the floor difficult. Markov random field-based obstacle segmentation is then performed using the IPM results and a floor appearance model. Next, the shortest distance between the robot and the obstacle is calculated. The algorithm is tested by applying it to 70 datasets, 20 of which include nonobstacle images where considerable changes in floor appearance occur. The obstacle segmentation accuracies and the distance estimation error are quantitatively analyzed. For obstacle datasets, the segmentation precision and the average distance estimation error of the proposed method are 81.4% and 1.6 cm, respectively, whereas those for a conventional method are 57.5% and 9.9 cm, respectively. For nonobstacle datasets, the proposed method gives 0.0% false positive rates, while the conventional method gives 17.6%.

Mobile robot obstacle avoidance method based on Kinect

Abstract: The invention discloses a mobile robot obstacle avoidance method based on Kinect. In the indoor environment, environment information is obtained through a Kinect sensor, obstacle characteristics are recognized by processing depth information, different obstacles are separated out, the sizes of the obstacles can be estimated and the special obstacles are recognized; according to information of the obstacles, corresponding obstacle scenes are determined, so that corresponding obstacle avoidance strategies are determined, and on the basis of an artificial potential field method, the solution with the high adaptability, good real-time performance and smooth path is provided for real-time obstacle avoidance of an intelligent mobile robot in the unknown indoor environment. The environment information can be better mastered, so that the method is applicable to more indoor scenes, meanwhile, obstacle avoidance is performed on the basis of the artificial potential field method, and the defects existing in some artificial potential field methods are overcome through the environment information by means of advantages of the artificial potential field method.