1,721,124 research outputs found
Robot Behaviour-Design, Description, Analysis and Modelling
No full textBook review: "Robot Behaviour: Design, Description, Analysis and Modelling", by Ulrich Nehmzow, edited by Springer
Rough-Terrain Mobile Robot Localization and Traversability with Application to Planetary Exploration
No full textTesi di Dottorato di Ricerca
Mobility Analysis of a Lunar Rover
No full textFor mobile robots driving across natural outdoor terrain, it is critical to employ an efficient and reliable locomotion system. The success of rough-terrain missions greatly depends on the ability of this system to effectively traverse whatever terrain is encountered by the robot. Different locomotion solutions have been proposed, including wheel with suspension, leg mechanisms, tracks, hopping, and snake-like systems. In this paper, a mobile robot is presented for applications on rough terrain, built at the Università of Salento, and named Dun
Locomotion Performance Evaluation of an All-Terrain Rover
No full textIn the last few years, mobile robots are increasingly being used in natural outdoor terrain for applications such as forestry, mining, rescuing, precision farming, and planetary exploration. Future tasks will require robotic vehicles to travel over longer distances through challenging terrain, with limited human supervision. To accomplish this objective, a higher degree of mobility will be primarily required, ensuring, at the same time, the safety of the vehicle. In this paper, a robot with advanced mobility features is presented and its locomotion performance is evaluated, following an analytical approach. The proposed vehicle features an independently controlled 4-wheel-drive/4-wheel-steer architecture that allows the robot to perform maneuvers such as turn-on-the-spot and crab motion. It also employs a passive rocker-type suspension system, improving the ability to traverse uneven terrain, while ensuring good traction performance. An overview of modeling techniques for rover-like vehicles is introduced. First, a method for formulating a classical kinematic model of an articulated vehicle is presented. Next, a method for expressing a quasi-static model of forces acting on the robot is described. Note that quasi-static models are appropriate due to the relative low speed and acceleration of those vehicles. Two optimization methods are also proposed to control the rover's motion, minimizing slip and power consumption, respectively. These models are used to reproduce the behavior of the robot in typical obstacle-climbing scenarios, pointing to the advantages compared with conventional architectures
Consumer-grade imaging system for NDVI measurement at plant scale by a farmer robot
No full textThe Normalized Difference Vegetation Index (NDVI) is a valuable indicator of plant vigor that is frequently used in agronomic practices to make timely and targeted decisions with the aim of increasing the productivity of the system. NDVI measurements of large-scale fields are typically performed using remote sensing from satellite and aerial imaging devices. However, due to their low spatial and temporal resolution, these technologies may have limitations in precision viticulture. This paper investigates the potential of a proximal sensing system to characterize the vine foliage that makes use of data collected by a farmer robot equipped with an Intel RealSense D435 camera. The camera includes two infrared (IR) sensors in stereoscopic configuration and one RGB sensor, which provide, for each observation, both infrared and visible red channel information, thus making possible pixel-per-pixel NDVI calculation. Solutions to IR filtering and radiometric calibration issues are proposed that significantly improve measurement accuracy and reliability. Since the camera also provides stereo-based 3D scene reconstruction, depth information can be used to separate the plant canopy from the background before NDVI measurement. At the same time, range data can be employed to extract geometric properties of the crop, such as plant height and/or volume. The system is validated in the field in a commercial vineyard at different phenological stages, from the formation of the berries to leaf discoloration and fall. Experimental results show good agreement compared with ground truth provided by a GreenSeeker, with a mean absolute percentage error in the NDVI estimation of 4.6% and a R2 of 0.87 tested over the whole grapevine cycle. Therefore, the proposed sensing system could be a feasible solution to automated NDVI estimation at plant-scale
Methods for Wheel Slip and Sinkage Estimation in Mobile Robots
No full textFuture outdoor mobile robots will have to explore larger and larger areas, performing difficult tasks, while preserving, at the same time, their safety. This will primarily require advanced sensing and perception capabilities. Video sensors supply contact-free, precise measurements and are flexible devices that can be easily integrated with multi-sensor robotic platforms. Hence, they represent a potential answer to the need of new and improved perception capabilities for autonomous vehicles. One of the main applications of vision in mobile robotics is localization. For mobile robots operating on rough terrain, conventional dead reckoning techniques are not well suited, since wheel slipping, sinkage, and sensor drift may cause localization errors that accumulate without bound during the vehicle’s travel. Conversely, video sensors are exteroceptive devices, that is, they acquire information from the robot’s environment; therefore, vision-based motion estimates are independent of the knowledge of terrain properties and wheel-terrain interaction. Indeed, like dead reckoning, vision could lead to accumulation of errors; however, it has been proved that, compared to dead reckoning, it allows more accurate results and can be considered as a promising solution to the problem of robust robot positioning in high-slip environments. As a consequence, in the last few years, several localization methods using vision have been developed. Among them, visual odometry algorithms, based on the tracking of visual features over subsequent images, have been proved particularly effective.
Accurate and reliable methods to sense slippage and sinkage are also desirable, since these effects compromise the vehicle’s traction performance, energy consumption and lead to gradual deviation of the robot from the intended path, possibly resulting in large drift and poor results of localization and control systems. For example, the use of conventional dead-reckoning technique is largely compromised, since it is based on the assumption that wheel revolutions can be translated into correspondent linear displacements. Thus, if one wheel slips, then the associated encoder will register revolutions even though these revolutions do not correspond to a linear displacement of the wheel. Conversely, if one wheel skids, fewer encoder pulses will be counted. Slippage and sinkage measurements are also valuable for terrain identification according to the classical terramechanics theory. This chapter investigates vision-based onboard technology to improve mobility of robots on natural terrain. A visual odometry algorithm and two methods for online measurement of vehicle slip angle and wheel sinkage, respectively, are discussed. Tests results are presented showing the performance of the proposed approaches using an all-terrain rover moving across uneven terrain
Radar Image Processing
No full textIn a first aspect, the present invention provides a method for performing radar image segmentation, the method comprising: using a radar, generating a radar image of an area of terrain, the radar image deriving from radar observations taken along a plurality of azimuth angles; performing a background extraction process on the radar image to extract a background
image comprising extracted radar observations, the background extraction process comprising estimating a range spread of a radar echo from the surface of the terrain as function of the azimuth angle and a tilt of the radar relative to the surface of the terrain; fitting a model to the extracted radar observations
along a particular azimuth angle; determining a value of a parameter indicative of the goodness of fit between the model and the extracted radar observations along the particular azimuth angle; and determining a classification depending on the value of the parameter for that azimuth angle
On the Mobility of All-Terrain Rovers
No full textIn this paper, a robot with advanced mobility features is presented and its locomotion performance is evaluated, following an analytical approach via extensive simulations. The vehicle features an independently controlled 4-wheel-drive/4-wheel-steer architecture and it also employs a passive rocker-type suspension system that improves the ability to traverse uneven terrain. An overview of modeling techniques for rover-like vehicles is introduced. First, a method for formulating a kinematic model of an articulated vehicle is presented. Next, a method for expressing a quasi-static model of forces acting on the robot is described. A modified rocker-type suspension is also proposed that enables wheel camber change, allowing each wheel to keep an upright posture as the suspension conforms to ground unevenness
Slip Angle Estimation for Lunar and Planetary Robots
No full textVehicle slip is a critical issue for mobile robots driving across loose soil. It is responsible for gradual deviation of the vehicle from the intended course, resulting in large drift and poor performance of localization and control systems, even leading, in extreme cases, to the danger of vehicle entrapment with consequent mission failure. This paper presents a novel method for lateral slip estimation based on visually observing the trace produced by the wheels of the robot, during traverse of soft, deformable terrain, as that expected for lunar and planetary rovers. The proposed algorithm uses a robust Hough transform enhanced by fuzzy reasoning to estimate the angle of inclination of the wheel trace with respect to the vehicle reference frame. Any deviation of the wheel trace from the planned path of the robot suggests occurrence of sideslip that can be detected, and more interestingly, measured. This allows the vehicle to estimate its actual heading angle, usually referred to as the slip angle. The details of the various steps of the visual algorithm are presented and the results of experimental tests performed in the field with an all-terrain rover are described, proving the method to be effective and robust
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