This paper presents some results within Pedatronics; a fusion between pedagogics and mechatronics. Our research interest is to study what emerges in the play with robotic toys. Field-experimental studies of 67 year old children’s purposeless play with robotic toys created a self-developmental sphere, as well as evoked young girl’s technological interest. Both girls and boys prolonged and intensified their interest according to the amount of gadgets involved. The results disclose a learning potential, indicating the importance to develop strategies at an early stage in order to encourage girls to choose technological and engineering educations. The study recommend engineers and toy designers, in cooperation with children, to move towards ’Integrated Play Systems’. Due to an ethological method, the results differ from other studies of children’s play with technological advanced artefacts. © 2002 IEEE
Classical reinforcement learning mechanisms and a modular neural network are unified for conceiving an intelligent autonomous system for mobile robot navigation. The conception aims at inhibiting two common navigation deficiencies: generation of unsuitable cyclic trajectories and ineffectiveness in risky configurations. Distinct design apparatuses are considered for tackling these navigation difficulties, for instance: 1) neuron parameter for memorizing neuron activities (also functioning as a learning factor), 2) reinforcement learning mechanisms for adjusting neuron parameters (not only synapse weights), and 3) a inner-triggered reinforcement. Simulation results show that the proposed system circumvents difficulties caused by specific environment configurations, improving the relation between collisions and captures.
An autonomous system able to construct its own navigation strategy for mobile robots is proposed. The navigation strategy is molded from navigation experiences (succeeding as the robot navigates) according to a classical reinforcement learning procedure. The autonomous system is based on modular hierarchical neural networks. Initially the navigation performance is poor (many collisions occur). Computer simulations show that after a period of learning the autonomous system generates efficient obstacle avoidance and target seeking behaviors. Experiments also offer support for concluding that the autonomous system develops a variety of object discrimination capability and of spatial concepts.
In this paper a low-cost and low-weight attitude estimation system for an autonomous helicopter is presented. The system is based on an inclinometer and a rate gyro. The data coming from the sensors is fused through a complementary filter. In this way the slow dynamics of the inclinometer can be effectively compensated. Tests have shown that we obtained a very effective attitude estimation system.
Previously we have presented a new scan-matching algorithm, based on the IDC - Iterative Dual Correspondence- algorithm, which showed a good localization performance even in the case of severe changes in the environment. The Problem of the IDC-algorithm is that there is no good way to estimate the covariance matrix of the position estimate, which prohibits an effective fusion with other position estimates from other sensors, e.g by means of the Kalman filter. In this paper we present a new way to estimate the covariance matrix, by estimating the Hessian matrix of the error function that is minimized by the IDC scan-matching algorithm. Simulation results show that the estimated covariance matrix correspond well to the real one.
We present a novel scan matching algorithm, IDC-S, Iterative Dual Correspondence-Sector, that matches range scans. The algorithm is based on the known Iterative Dual Correspondence, IDC, algorithm which has shown good performance in real environments. The improvement is that IDC-S is able to deal with relatively large changes in the environment. It divides the scan in several sectors, detects and removes those sectors that are changed and matches the scans only using unchanged sectors. IDC-S and other variants of IDC are extensively simulated and evaluated. The simulations show that IDC-S is very robust and can locate in many different kind of environments. We also show that it is possible to effectively combine the existing IDC algorithms with IDC-S, thus obtaining an algorithm that performs very well both in rectilinear as well as nonrectilinear environments, even when changed as much as 65%. © 1999 IEEE.
Although often neglected, finger extension force is of great importance for developing grip strength. This paper describes the design and evaluation of a new finger extension force measurement device (EX-it) based on the biomechanics of the hand. Measurement accuracy and test-retest reliability were analysed. The device allows measurements on single fingers as well as all the fingers (excluding the thumb) of both healthy and deformed hands. The coefficient of variation in the device was 1.8% of the applied load, and the test-retest reliability showed a coefficient of variation no more than 7.1% for healthy subjects. This study also provides reference values for finger extension force in healthy subjects and patients with rheumatoid arthritis (RA). Significant differences were found in extension strength between healthy subject and RA patients (men, p < 0.05 and women, p < 0.001). EX-it provides objective and reliable data on the extension force capacity of normal and dysfunctional hands and can be used to evaluate the outcome of therapeutic interventions after hand trauma or disease
This paper investigates whether a stereo vision system based on points of interest is robust enough to detect obstacles for applications like a mobile robot in an industrial environment and for the visually impaired. Points of interest are extracted with a known method, called KLT. Two algorithms to solve the correspondence problem (Sum of Squared Difference and Variance Normalized Correlation) are used and evaluated as well as a combination of the two. An improvement is made if the two algorithms are combined. The tests show that stereo vision based on points of interest only can be used robustly for obstacle detection if there is enough texture on the obstacle. Otherwise too few points of interest on the object are detected and a reliable estimation of the distance to the object cannot be made.
The purpose was to develop an extension finger-force measurement device, and investigate the intra-individual repeatability. The design of the measuring device allows single finger force and whole hand measurements, and the repeatability error on extension finger forces was measured, both on the whole hand, as well as on individual fingers. The tests showed that a repeatability error of less then 15 % can be achieved for single finger measurements and less then 21 % for whole hand measurements.
In the future, mobile robots will most probably navigate through the fields autonomously to perform different kind of agricultural operations. As most crops are cultivated in rows, an important step towards this long-term goal is the development of a row-recognition system, which will allow a robot to accurately follow a row of plants. In this paper we describe a new method for robust recognition of plant rows based on the Hough transform. Our method adapts to the size of plants, is able to fuse information coming from two rows or more and is very robust against the presence of many weeds. The accuracy of the position estimation relative to the row proved to be good with a standard deviation between 0.6 and 1.2 cm depending on the plant size. The system has been tested on both an inter-row cultivator and a mobile robot. Extensive field tests have showed that the system is sufficiently accurate and fast to control the cultivator and the mobile robot in a closed-loop fashion with a standard deviation of the position of 2.7 and 2.3 cm, respectively. The vision system is also able to detect exceptional situations by itself, for example the occurrence of the end of a row.