Search results

The purpose of this paper is to evaluate the locomotion performance of all‐terrain rovers employing rocker‐type suspension system.

In 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 four‐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.

The proposed models can be used to assess the locomotion performance of a mobile robot on rough‐terrain for design, control and path planning purposes. The advantage of the rocker‐type suspension over conventional spring‐type counterparts is demonstrated. The variable camber suspension is shown to be effective in improving a robot's traction and climbing ability.

The paper can be of great value when studying and optimizing the locomotion performance of mobile robots on rough terrain. These models can be used as a basis for advanced design, control and motion planning.

The paper describes an analytical approach for the study of the mobility characteristics of vehicles endowed with articulated suspension systems. A variable camber mechanism is also presented.

This research aims to address the issue of safe navigation for autonomous vehicles in highly challenging outdoor environments. Indeed, robust navigation of autonomous mobile robots over long distances requires advanced perception means for terrain traversability assessment.

The use of visual systems may represent an efficient solution. This paper discusses recent findings in terrain traversability analysis from RGB-D images. In this context, the concept of point as described only by its Cartesian coordinates is reinterpreted in terms of local description. As a result, a novel descriptor for inferring the traversability of a terrain through its 3D representation, referred to as the unevenness point descriptor (UPD), is conceived. This descriptor features robustness and simplicity.

The UPD-based algorithm shows robust terrain perception capabilities in both indoor and outdoor environment. The algorithm is able to detect obstacles and terrain irregularities. The system performance is validated in field experiments in both indoor and outdoor environments.

The UPD enhances the interpretation of 3D scene to improve the ambient awareness of unmanned vehicles. The larger implications of this method reside in its applicability for path planning purposes.

This paper describes a visual algorithm for traversability assessment based on normal vectors analysis. The algorithm is simple and efficient providing fast real-time implementation, since the UPD does not require any data processing or previously generated digital elevation map to classify the scene. Moreover, it defines a local descriptor, which can be of general value for segmentation purposes of 3D point clouds and allows the underlining geometric pattern associated with each single 3D point to be fully captured and difficult scenarios to be correctly handled.

The purpose of this paper is to present a mobile robot with an olfactory capability for hazardous site survey. Possible applications include detection of gas leaks and dangerous substances along predefined paths, inspection of pipes in factories, and mine sweeping.

The mobile sentry is equipped with a transducer array of tin oxide chemical sensors, compliant with the standard interface IEEE 1451, which provides odour‐sensing capability, and uses differential drive and spring‐suspended odometric trackballs to move and localize in the environment. The monitoring strategy comprises two stages. First, a path learning operation is performed where the vehicle is remotely controlled through some potential critical locations of the environment, such as valves, pressure vessels, and pipelines. Then, the robot automatically tracks the prerecorded trajectory, while serving as an electronic watch by providing a real‐time olfactory map of the environment. Laboratory experiments are described to validate the approach and assess the performance of the proposed system.

The approach was shown to be effective in experimental trials where the robot was able to detect multiple odour sources and differentiate between sources very close to one another.

One limitation of the methodology is that it has been specifically designed for odour detection along a well‐defined path in a highly structured environment, such as that expected in the industrial field. The problem of detection of leakages outside the search path is not addressed here.

This mobile robot can be of great value to detect hazardous fluid leakages in chemical warehouses and industrial sites, thus increasing the safety level for human operators.

The paper describes a mobile robotic system, which employs an odour‐sensing capability to perform automated monitoring of hazardous industrial sites. A dynamic model of the mobile nose is also discussed and it is shown that it well describes the behaviour of the system.

Aims at developing vision‐based algorithms to improve efficiency and quality in agricultural applications. Two case studies are analyzed dealing with the harvest of radicchio and the post‐harvest of fennel, respectively.

Presents two visual algorithms, which are called the radicchio visual localization (RVL) and fennel visual identification (FVI). The RVL serves as a detection system of radicchio plants in the field for a robotic harvester. The FVI provides information to an automated cutting device to remove the parts of fennel unfit for the market, i.e. root and leaves. Laboratory and field experiments are described to validate our approach and asses the performance of our visual modules.

Both the visual systems presented showed to be effective in experimental trials, computational efficient, accurate, and robust to noises and lighting variations. Computer vision could be successfully adopted in the intelligent and automated production of fresh market vegetables to improve quality and efficiency.

Provides guidance in the development of vision‐based algorithms for agricultural applications.

Describes visual algorithms based on intelligent morphological and color filters which lends themselves very well to agricultural applications and allow robustness and real‐time performance.