Integrated pest management (IPM) seeks to minimize the environmental impact of pesticide application. IPM is based on two important aspects —prevention and monitoring of diseases and insect pests— which today are being assisted by sensing and artificial-intelligence (AI). Particularly, AI helps to identify, monitor, control and make decisions about pests in crops. In this paper, we present a comparison among five machine-learning models to classify the population of the boll weevil in cotton into three classes: low, medium and high. Weather data (average daily rainfall, humidity and temperature) were used to classify the population of the boll weevil in the department of Córdoba, Colombia. The results showed that XGBoost obtained the highest accuracy (88%). Results showed that it is possible to classify boll-weevil populations using weather data.

Hand Gesture Recognition (HGR) has application in Human Machine Interfaces (HMIs), to control robots, games, and machines. Here we demonstrate a soft-matter multi-layer printed electronic circuit, that can be used to detect the human gesture without the need for physical contact, except for unlocking the system. The film is able to detect touch and proximity of the hand at various nodes, and thus is able to construct an image of the hand. This is performed through fabrication of a thin, stretchable, high resolution, and multilayer circuit, that embeds a mesh of capacitive sensors for human gesture detection, through proximity sensing. The film is fabricated by superposing 6 layers: 3 insulating polymer layers, two EGaIn liquid metal layers patterned in shape of a mesh, and one flexible printed circuit board (FPCB) layer. The fabrication technique absed on spray deposition of EGaIn liquid metal is fast, low-cost, and easy, and can be used for scalable fabrication of large area multi-node sensing surfaces, with application in Robotic e-skins, and general Human Machine Interfaces. The system is trained with five gestures, including dynamic gestures. We demonstrate classification of these gestures using a support vector machine (SVM) based algorithm.

Sensors that detect human presence received an increasing attention due to the recent advances in smart homes, collaborative fabrication cells, and human robot interaction. These sensors can be used in collaborative robot cells and mobile robots, in order to increase the robot awareness about the presence of humans, in order to increase safety during their operation. Among proximity detection systems, capacitive sensors are interesting, since they are low cost and simple human proximity detectors, however their detection range is limited. In this article, we show that the proximity detection range of a capacitive sensor can be enhanced, when the sensor is placed near a water container. In addition, the signal can pass trough several adjacent water containers, even if they are separated by a few centimeters. This phenomenon has an important implication in establishing low cost sensor networks. For instance, a limited number of active capacitive sensor nodes can be linked with several simple passive nodes, i.e. water containers, to detect human or animal proximity in a large area such as a farm, a factory or home. Analysis on the change of the maximum proximity range with sensor dimension, container size and liquid filler was performed in order to study this effect. Examples of application are also demonstrated.

In this paper, several multiflux, three-phase induction motors, dual-winding configurations are experimentally analysed. Using a motor with two reconfigurable stator windings sharing the same slots, several dual-winding configurations with different spatial displacement between the two partial windings, are experimentally tested. A programmable voltage source is used to adjust the line-to-line voltage at the terminals of the motor so that the fundamental flux level is the same for all the configurations. Then, the experimentally determined value is compared to the calculated value of the electromotive force and the per-unit values of line-to-line voltage per flux or current per flux are measured and compared to simulated results. Finally, the locked-rotor impedance was measured in order to assess its variation with the fundamental winding factor.

Efforts in soft-matter sensing with liquid metal (LM) have largely focused on pressure, strain, and bend sensing by measuring changes in capacitance or resistance of LM circuits embedded in a soft elastomer. Here, we show that LM electronics can be used to combine pressure sensing with proximity detection and remote finger tracking - sensing modalities not previously demonstrated in a soft electronic skin (E-Skin). This is accomplished with a novel architecture composed of 9 capacitive sensors arranged in two layers and placed below a spiral-shaped pressure transducer. The E-skin is composed of eutectic gallium-indium (EGaIn) LM alloy and Pt-cured EcoFlex 0030 elastomer. The multi-modal sensor is able to detect the proximity and motion of a finger as it moves above the sensor and is able to detect touch and pressure once the finger comes into contact. Proximity sensing and contact pressure are distinguish by comparing changes in capacitance and resistance of the embedded LM circuitry. Measurements on a cm-scale prototype suggest proximity sensing with a range of 8.7 cm and maximum contact pressure of 110 kPa.

This article presents an inchworm climbing robot that is designed with switchable magnets and a single DOF arm. The InchwormClimber works on ferromagnetic structures and consumes little energy when climbing and can descend safely with almost zero energy consumption. Furthermore, considering the critical role of the adhesion unit in the overall functionality of the robot (weight, climbing speed and the payload), we optimized the switchable magnet unit for a higher adhesion force per mass unit.

Omnidirectional wheels used on Omniclimber inspection robot and in other robots enable a holonomic drive and a good maneuverability. On the other hand, they have a poor wheel traction and suffer from vertical and horizontal vibration, decreasing the trajectory following accuracy of the robot. In this study, we address this problem by integrating an orientation estimation and correction algorithm in the Omniclimber control by integration of an accelerometer. Moreover, since the Omniclimber chassis adapts to curved structures, the kinematics of the robot change when moving on a curved surface. We integrated an additional algorithm which corrects the robot's kinematics based on the curvature diameter and the current robot orientation. By integrating these two algorithms we could make remarkable improvements on the path following accuracy of the Omniclimber on flat and curved structures.

The goal of this work is to study the application of switchable magnets (SM) for climbing robots. A switchable magnet is a device which uses moving permanent magnets to change the magnetic flux path and switch on or off the magnetic attraction force. In our work we used Comsol Multiphysics, a physics simulation software in order to simulate the flow of the magnetic flux on switchable magnets on its different states and study the effect of different design and material parameters on the attraction force of the unit. Bearing in mind the lessons learned from this study, we developed a novel device in a smaller scale with the best holding force/mass ratio, for using in climbing robot applications. As a case study, three of these optimized SM units are then equipped with an actuator that can rotate the moving magnets, turning the device on and off. This new device is employed in a novel adaptive adhesion unit for the OmniClimber robot, replacing its previous system which was relying on electromagnets magnets. We demonstrate this novel device application on a climbing robot and the advantages relative to electromagnet or permanent magnet based devices.

ISR-Softhand is a novel under-actuated self contained anthropomorphic robotic hand which takes advantage of elastic joints and soft pads. The integrated compliance in the hand's joint allows for a very good adaptability to a wide range of objects. It can achieve the top ten grasps with highest frequency of usage by humans, with only three actuators.

This paper aims to analyze the number of actuators as well as the actuation strategy for an underactuated prosthetic hands. Two comprehensive analysis were made for this purpose. 16 possible actuation strategies in five categories of one to five actuators were defined. Based on these 16 strategies two analysis were performed: grasp diversity and grasp functionality. For the first analysis we defined a performance metric based on all possible grasps by the human hand, while in the second analysis only the top grasps with the highest frequency of usage were considered. By comparing the performance of these strategies we obtained some interesting results regarding the best actuation strategies for the underactuated anthropomorphic hands.

This paper introduces, OmniClimber II, an evolution of OmniClimber I, a novel climbing robot with high maneuverability for inspection of ferromagnetic 3D human made structures. The robot takes advantage of novelties which allows an excellent maneuverability on the structure and can adapt to flat and non-flat structures. In this paper we present the conceptual and detailed design of the robot, the implementation and improvements over the first version and a set of tests: laboratory tests on a flat surface; and a set of field tests performed on a wind turbine foundation and another field test on a curved steel structure in Mechanical Engineering, to prove the applicability of the robot on both structures.

This paper introduces a novel grasping mechanism that combines caging and force closure approaches in order to grasp an object. The main advantage of the gripper is its adaptability to various object shapes and sizes with low DOF. The two DOF gripper takes advantage of two tendon driven trunks. Inspired from the continuum manipulator concept, the trunks do not include discrete joints and rather an elastomer plays the role of the joints. The tendon driven trunks in the first phase of grasping act as rigid links and in the second phase act as flexible mechanisms. Two prototype of the gripper are developed, tested and evaluated. The underactuated prototypes showed a very good adaptability to different object shapes and sizes.

This paper describes design and development of omnidirectional magnetic climbing robots with high maneuverability for inspection of ferromagnetic 3D human made structures. The main focus of this article is design, analysis and implementation of magnetic omnidirectional wheels for climbing robots. We discuss the effect of the associated problems of such wheels, e.g. vibration, on climbing robots. This paper also describes the evolution of magnetic omnidirectional wheels throughout the design and development of several solutions, resulting in lighter and smaller wheels which have less vibration and adapt better to smaller radius structures. These wheels are installed on a chassis which adapts passively to flat and curved structures, enabling the robot to climb and navigate on such structures.

The field of robotics has experienced a strong growth worldwide in 2011, with record sales in all areas. Over 166.000 industrial robots has been sold to a variety of industries to improve productivity as well as quality control, being able to operate in dangerous environments for human operators. Over 16.000 professional service robots were sold with defense (unmanned aerial vehicles), agriculture (milking), logistics and medicine (robotic assisted surgery and therapy) being the most relevant application areas. The area of personal domestic service robotics is also booming, with over 2.5 million robots being sold for the applications (e.g. vacuum and floor cleaning, lawn mowing, and entertainment).

This paper introduces a novel climbing robot with high maneuverability for inspection of ferromagnetic 3D human made structures. The robot takes advantage of novelties which allows an excellent maneuverability on the structure and can adapt to flat and non-flat structures. In this paper we present the conceptual and detailed design of the robot, the implementation and two tests: laboratory tests on a flat surface; and a set of field tests performed on a wind turbine foundation to prove the applicability of the robot on both structures.

This paper proposes a method based on cooperation between climbing and ground robots in order to address the mapping problem for autonomous inspection of 3D structures. In the proposed method ground robots act as a mobile observer with a wide coverage for a climbing robot, to detect and estimate the size of the structure being climbed. We will present a case study in which multiple terrain robots provide the model of a structure which should be explored by a pole climbing robot. Each terrain robot is equipped with a low cost wide angle VGA camera, and some markers are fixed on the climbing robot. At each navigation step, the climbing robot and terrain robots cooperate to model a part of the structure which should be explored by the climbing robot.

Exploration of an unknown environment is a fundamental concern in mobile robotics. This paper presents an approach for cooperative multi-robot exploration, fire searching and mapping in an unknown environment. The proposed approach aims to minimize the overall exploration time, making it possible to localize fire sources in an efficient way. In order to achieve this goal, the robots should cooperate in an effective way, so they can individually and simultaneously explore different areas of the environment while they identify fire sources. The proposed approach employs a decentralized frontier based exploration method which evaluates the cost-gain ratio to navigate to target way-points. The target way-points are obtained by an A* search variant algorithm. The potential field method is used to control the robots motion while avoiding obstacles. When a robot detects a fire, it estimates the flame's position by triangulation. The communication between the robots is done in a decentralized control way where they share the necessary data to generate the map of the environment and to perform cooperative actions in a behavioral decision making way. This paper presents simulation and experimental results of the proposed exploration and fire search method and concludes with a discussion of the obtained results and future improvements.

Fine manipulation of large industrial manipulators faces many problems due to well known error sources. Most climbing robots can be seen as mobile manipulators whose base is also moving across the climbing structure, and consequently adds some additional positioning errors. 3DCLIMBER is a serial mechanism pole climbing robot, developed at ISR-UC. The preliminary tests of the robot showed that it is particularly important to position the grippers precisely in the appropriate pose before grasping the structure. Otherwise the accumulating error will impair autonomous climbing process by forcing the operator to stop the operation after a couple of steps in order to calibrate the robot. This paper describes a self calibrating method proposed to measure and compensate these errors.

This paper presents new advances in the area of chemical trail following with mobile robots. For that purpose, the properties of chemical volatiles and their evaporation rates were tested in order to discover an appropriate candidate to mark chemical trails and at the same time with ability to be detected by the robot olfactory system along the time. The capability of chemical detection was provide by a metal oxide-based olfactory system, mounted in a Khepera III mobile robot. An algorithm to search and track chemical trails is proposed. This algorithm was validated by simulations and by laboratory tests with a Khepera III mobile robot.

This paper characterizes four commercial laser range finders (LRF) while operating under adverse conditions, namely: low visibility, and multiple types of target surfaces, including different optical properties, angles and radiant surfaces. The study considered two scanning LRF commonly used in mobile robotics: the Sick LMS200 and the Hokuyo URG-04LX, and two industrial punctual LRFs: the IFM Efector O1D100 and the Sick DT60. Based on the results obtained, a set of conclusions and recommendations are taken considering the utilization of LRF in mobile robots operating in risky and adverse environments, like firefighting applications.

3DCLIMBER is a running project in the University of Coimbra for developing a climbing robot with the capability of manipulating over 3D human-made structures. This paper mainly discuss the conceptual and detailed design and development of a Pole Climbing robot with minimum degrees of freedom which can climb over 3D structures with bends and branches followed by Preliminary test results of the robot performance. Electronics architecture and control algorithms are briefly described. The paper finishes with discussion of the current results and identifies some future works.

On this paper, inertial orientation measurements are exploited to compensate the rotational degrees of freedom for an aerial vehicle carrying a perspective camera, taking a sequence of images of the ground plane. It is known that, on the pure translation case, full homographies are reduced to planar homologies, and the relative scene depth of two points equals the reciprocal ratio of their image distances to the the FOE. The first part of this paper covers trajectory recovery for an airship carrying a perspective camera taking a sequence of images of the ground plane, as a series of relative poses between successive camera poses. This is commonly named “Visual Odometry“. Previous results showed that the ratio of heights over the ground plane on two views can be calculated more accurately, and thus the altitude component of the trajectory, and here these results are extended by recovering the full 3D camera trajectory. In the second part, the same rotation-compensated imagery is exploited on the mapping domain: from pixel correspondences between successive images the height of points over the ground plane can be recovered, and placed on a DEM grid, performing 3D mapping from monocular aerial images. These results may be useful on the SLAM context.

This paper proposes a multi-stage approach for landmine detection. It is based on a feature-level data fusion which combines data from several nonspecific sensors. The sensor fusion process is analysed and divided in three stages in order to improve the final result. During the first stage all suspected objects are detected against a background. Then, the detected objects are classified to be a man-made or natural object. And, finally, the landmines are distinguished among the identified manmade objects. The last two stages are described in detail in this paper, demonstrating the advantages for their separation. Classification features, which enable the sensor fusion, are also presented in this work together with an approach for their integration. The proposed ideas are tested using real experimental data obtained from pulsed and continuous metal detectors, infrared camera and ground penetrating radar

To enable automated landmine detection a number of practical problems must be overcome. One of them is the mapping of data from landmine detection sensors using a mobile demining robot in an unstructured outdoor environment. The odometry of the robot used for mapping can be improved by using an additional vision system. It is proposed in this paper to utilize natural landmarks which can be found on the ground for this purpose. New simple algorithms for detection and association of natural landmarks are developed and described. Finally, the experimental results of applying these algorithms on a set of images obtained from a camera mounted on the robot are presented. The results show their robustness for different environmental conditions and robot motions

For mobile vehicle navigation in dynamic environments it is desirable that robots have a model of the dynamic aspects of the world. In this paper we present a method for detection and tracking of multiple moving objects using sensor information. The method uses particle filters to estimate objects states, and sample based joint probabilistic data association filters to perform the assignment of features detected from sensor data to filters. A perception mechanism, based on occupancy grids, is presented to distinguish between mobile features and static objects. Experimental results from a real-time implementation using a laser range sensor are presented demonstrating the feasibility and effectiveness of the presented methods.

Humanitarian demining is a dangerous and time consuming task which leads to more victims among deminers. Thus large efforts are being made worldwide in order to develop autonomous robots able to detect landmines without human participation. Any single sensor currently available for antipersonnel landmine detection can not provide the required detection/false alarm ratio, thus a combination of several sensors together with sensor fusion techniques is used. One of the main problems in the practical implementation of sensor fusion for a demining robot is the lack of experimental data for training the algorithms. Formally using a small training and evaluation set does not allow to take conclusions about the results of sensor fusion. This work presents a sensor fusion strategy which helps to overcome the problem of having few experimental data by creating an unified landmine signatures database. Using this database for sensor fusion in general and for features selection in particular is described. In addition to commonly used statistical features several new features are proposed which reflect the shape and the nature of the object. Moreover the designed ROIs extraction algorithm provides additional information allowing to improve features extraction. The results of features selection using a combination of Mutual information and Hausdorff distance are presented. Preliminary results of sensor fusion for pulsed and continuous metal detectors are also presented.

This paper describes an architecture for estimating environmental odor maps and presents experimental results obtained using that architecture with five mobile robots inside a large laboratory with two odor sources and forced ventilation. The mobile sensing agents employed in the experiments have self localization capabilities and carry an electronic nose and a thermal anemometer. The proposed architecture allows integrating sparse olfaction data gathered by the mobile agents along their trajectories and dynamically estimate the spatial concentration of different odor fields. The data assimilation process is made centrally by a PC that polls periodically each robot through a RF network in order to get the data gathered during the previous acquisition period. The estimation of odor fields is made in two steps: first each agent estimates the odor mixture and concentration by means of a neural network-based regression algorithm that converts values from gas sensor space to the corresponding odor space. Then the sensed data is assimilated into an advection-diffusion model by means of a reduced order Kalman filter. In the current implementation the central controller, responsible for the maps estimation, specifies to each agent their target area to explore. The proposed architecture was validated with a set of experiments that demonstrated its ability to estimate and capture the dynamics of overlapping odor fields. This work can easily be adapted to city buses or other local transportation systems in order to monitor the pollution or quickly detect hazardous chemicals inside cities.

This paper presents a pre-processing method to allow the implementation of landmine sensor fusion techniques in a pneumatic demining robot. The proposed method is based on a two-level strategy for the sensor fusion. This strategy allows separating two different classification tasks: object/background and mine/another object. This work is focused in the first stage of the process, particularly in the identification of regions-of-interest (ROI). The proposed ROI extraction algorithm decreases the number of parameters, is reliable in different environmental conditions and can be implemented online. Results of experimental implementation and conclusions are presented.

The enormous amount of anti-personal land mines scattered over many countries worldwide is a major humanitarian problem asking for an efficient solution at an affordable cost. The article describes a robust and low cost mobile robot that can transport equipment of about 1.5 times its own weight over natural terrains with inclinations superior to 40 degrees. The robot mechanical structure is optimised for precisely scanning large areas of natural environments with land mine detection sensors. The paper also describes the control architecture of the robot with the possibility to correct the motion direction. The land mine sensing system of the robot is presented.

Perspective camera calibration has been a research subject for a large group of researchers. However, only a small number of those methods base their approaches on the use of monoplane calibration points. We developed one of those methodologies using monoplane calibration points to perform an explicit three-dimensional (3-D) camera calibration. This methodology is based on an iterative approach. To avoid the singularities obtained with the calibration equations when monoplane calibration points are used, this method computes the calibration parameters in a multistep procedure and requires a first-guess solution for the intrinsic parameters. These intrinsic parameters are updated and their accuracy increased during the iterative procedure. All computations required are linear and in addition to the extrinsic parameters the proposed method also computes the first coefficient of the radial distortion (k/sub 1/) and the skew angle. A first-guess value for the focal length of the lens is required but its value is iteratively updated using the Gauss lens model. This methodology also includes the uncertainty horizontal image scale factor (S/sub x/) on the set of calibration parameters to be computed, which makes this approach independent of the accuracy of the horizontal scale factor. The proposed methodology has the advantage that it can be used with monoplane calibration data with no restrictions for the pose geometry of the camera. In this approach the pose estimation problem is treated separately, computing the pose orientation of the camera in a first step and using this information to compute the pose location. A model for the expected stability of the camera look angles from noisy image data and its stability analysis as a function of the pose geometry of the camera is presented. Several experimental and simulated analyzes were performed and are presented.

In this paper, we address the problem of navigating an autonomous mobile robot in an unknown indoor environment. The parti-game multiresolution learning approach is applied for simultaneous and cooperative construction of a world model, and learning to navigate through an obstacle-free path from a starting position to a known goal region. The paper introduces a new approach, based on the application of the fuzzy ART neural architecture, for on-line map building from actual sensor data. This method is then integrated, as a complement, on the parti-game world model, allowing the system to make a more efficient use of collected sensor information. Then, a predictive on-line trajectory filtering method, is introduced in the learning approach. Instead of having a mechanical device moving to search the world, the idea is to have the system analyzing trajectories in a predictive mode, by taking advantage of the improved world model. The real robot will only move to try trajectories that have been predicted to be successful, allowing lower exploration costs. This results in an overall improved new method for goal-oriented navigation. It is assumed that the robot knows its own current world location-a simple dead-reckoning method is used for localization in our experiments. It is also assumed that the robot is able to perform sensor-based obstacle detection (not avoidance) and straight-line motions. Results of experiments with a real Nomad 200 mobile robot are presented, demonstrating the effectiveness of the discussed methods.

Perspective camera calibration has been in the last decades a research subject for a large group of researchers and as a result several camera calibration methodologies can be found in the literature. However only a small number of those methods base their approaches on the use of monoplane calibration points. This paper describes one of those methodologies that uses monoplane calibration points to realize an explicit 3D camera calibration. To avoid the singularity obtained with the calibration equations when monoplane calibration points are used, this method computes the calibration parameters in a multi-step procedure and requires a first-guess solution for the intrinsic parameters. These parameters are updated and their accuracy increased through an iterative procedure. A stability analysis as a function of the pose of the camera is presented. Camera pose view strategies for accurate camera orientation computation can be extracted from the pose view stability analysis.

This paper introduces a new approach, based on the application of the fuzzy ART neural architecture, for online map building from actual sensor data collected with a mobile robot. This method is then integrated as a complement, on the parti-game learning approach, allowing the system to make a more efficient use of collected sensor information. Also, a predictive online trajectory filtering method is introduced on the learning approach. Instead of having a mechanical device (robot) moving to search the world the idea is to have the system analysing trajectories in a predictive mode, by taking advantage of the improved world model. The real robot will only move to try trajectories that have been predicted to be successful, allowing lower exploration costs. This results on an overall new and powerful method for simultaneous and cooperative construction of a world model, and learning to navigate from an initial position to a goal region on an unknown world. It is assumed that the robot knows its own current world location. It is additionally assumed that the mobile robot is able to perform sensor-based obstacle detection (not avoidance), and straight-line motions. Results of experiments with a real Nomad 200 mobile robot will be presented, demonstrating the effectiveness of the proposed methods.

This article describes one solution for the problem of pursuit of objects moving on a plane by using a mobile robot and an active vision system. The solution deals with the interaction of different control systems using visual feedback and it is accomplished by the implementation of a visual gaze holding process interacting cooperatively with the control of the trajectory of a mobile robot. These two systems are integrated to follow a moving object at constant distance and orientation with respect to the mobile robot. The orientation and the position of the active vision system running a gaze holding process give the feedback signals to the control used to pursuit the target in real-time. The paper addresses the problems of visual fixation, visual smooth pursuit, navigation using visual feedback and compensation for system's movements. The algorithms for visual processing and control are described in the article. The mechanisms of cooperation between the different control and visual algorithms are also described. The final solution is a system able to operate at approximately human walking rates as the experimental results show in the paper.

This paper addresses the problem of learning sensor-based navigation of a mobile robot on, an indoor environment, where the location, size, and shape of obstacles is assumed to be initially unknown to the robot. We use the parti-game multiresolution approach for simultaneous learning of a world model, and learning to navigate from a start position to a goal region on the world. These two learning abilities are cooperating and enhancing each other in order to improve the overall system performance. It is assumed that the robot knows its own current world location. It is only additionally assumed that the mobile robot is able to perform sensor-based obstacle detection (not avoidance), and that it is able to perform straight-line motions. Results of experiments with a real Nomad 200 mobile robot will be presented.

The main purpose of this paper is not to present a new camera calibration algorithm or a new pose estimation method, but just to analyse the stability of the camera pose estimation using the Haralick observation which is based on the fact that there is sufficient information on the 2D perspective projection of a rectangle of unknown size in 3D space to determine the three camera look angles. A brief description of the camera look angles computation is presented and a model for the expected stability of the camera look angles computation front noisy image data is described. A stability analysis as a function of the pose of the camera is also described, and the influence of the camera pose is confirmed with simulated data. As a result, camera pose view strategies for accurate camera orientation computation can be extracted from this pose view stability analysis.

This article describes one solution for the problem of pursuit by using a mobile robot and an active vision system. The solution deals with the interaction of different control systems using visual feedback and it is accomplished by the implementation of a visual gaze holding process interacting cooperatively with the control of the trajectory of a mobile robot. These two systems are integrated to follow a moving object at constant distance and orientation with respect to the mobile robot. The orientation and the position of the active vision system running a gaze holding process give the feedback signals to the control used to pursuit the target in real-time. The mechanisms of cooperation between the different control and visual algorithms are described. The final solution is a system able to operate at approximately human walking rates.

Many industrial operations can be automated using robot arms but, however, require the use of robots being able to perform surface following in an adaptive way. Contour-following in real-time based on sensor information is a delicate task, yet very useful in operations like painting, welding, glues administration and surface polishing. The contour-following can be accomplished either by using previous knowledge of the shape of the surface, by executing a sequence of movements point-by-point, or in an adaptive way. In the last case a sensor or a set of sensors for collecting data about the environment are used. Furthermore, the contour-following can occur with or without contact. The paper describes a 3-D contour-following behavior controller. The contour-following operation is performed in real-time without contact using proximity information. Results of simulations and real-time implementation are presented and discussed.>

In practice focusing can be obtained by displacing the sensor plate with respect to the image plane, by moving the lens or by moving the object with respect to the optical system. Moving the lens or sensor plate with respect to each other, causes changes of the magnification and corresponding changes on the object coordinates. In order to overcome these problems, the authors propose a technique to vary the degree of focusing by moving the camera with respect to the object position. The camera is attached to the tool of a manipulator in a hand-eye configuration with the position of the camera always known. This approach ensures that the focused areas of the image are always subjected to the same magnification. To measure the focus quality operators are used to evaluate the quantity of high-frequency components on the image. Different types of these operators was tested and the results compared.>

Addresses the geometric modelling aspects related with the use of a pair of video cameras mounted on a six degrees-of-freedom manipulator in an hand-eye configuration. The information obtained by these sensors shall be used to generate the depth map of the environment where the manipulator works. The procedure to determining the image formation parameters is known by camera calibration and a method to optimize these calibration parameters is presented. After presenting the calibration technique used, the authors develop a calibration matrix invariant to movement. The optimization process is applied to this matrix to obtain a better performance in the numerical data.>

Flexible robotic systems, for example systems to be used in parts assembly automation, require robot manipulators featuring among other capabilities, the capacity of executing reliable fine motions. This requirement leads to the need of using sensors and so to the need of searching for techniques for efficient processing and integration of sensory data. The paper addresses issues regarding the integration of sensors for flexible robotics, namely, force/torque, tactile and proximity range sensors. The authors are concerned with sensory integration for fine motion control, the following subjects being addressed: the architecture of the control system integrating force/torque, tactile and distance sensors and algorithms for position and force control using task-space sensory feedback.>