Outdoor navigation of mobile robots

AGVs in the manufacturing industry currently constitute the largest application area for mobile robots. Other applications have been gradually emerging, including various transporting tasks in demanding environments, such as mines or harbours. Most of the new potential applications require a free-ranging navigation system, which means that the path of a robot is no longer bound to follow a buried inductive cable. Moreover, changing the route of a robot or taking a new working area into use must be as effective as possible. These requirements set new challenges for the navigation systems of mobile robots. One of the basic methods of building a free ranging navigation system is to combine dead reckoning navigation with the detection of beacons at known locations. This approach is the backbone of the navigation systems in this study. The study describes research and development work in the area of mobile robotics including the applications in forestry, agriculture, mining, and transportation in a factory yard. The focus is on describing navigation sensors and methods for position and heading estimation by fusing dead reckoning and beacon detection information. A Kalman filter is typically used here for sensor fusion. Both cases of using either artificial or natural beacons have been covered. Artificial beacons used in the research and development projects include specially designed flat objects to be detected using a camera as the detection sensor, GPS satellite positioning system, and passive transponders buried in the ground along the route of a robot. The walls in a mine tunnel have been used as natural beacons. In this case, special attention has been paid to map building and using the map for positioning. The main contribution of the study is in describing the structure of a working navigation system, including positioning and position control. The navigation system for mining application, in particular, contains some unique features that provide an easy-to-use procedure for taking new production areas into use and making it possible to drive a heavy mining machine autonomously at speed comparable to an experienced human driver.reviewe

Autonomous robot systems and competitions: proceedings of the 12th International Conference

This is the 2012’s edition of the scientific meeting of the Portuguese Robotics Open (ROBOTICA’ 2012). It aims to disseminate scientific contributions and to promote discussion of theories, methods and experiences in areas of relevance to Autonomous Robotics and Robotic Competitions. All accepted contributions are included in this proceedings book. The conference program has also included an invited talk by Dr.ir. Raymond H. Cuijpers, from the Department of Human Technology Interaction of Eindhoven University of Technology, Netherlands.The conference is kindly sponsored by the IEEE Portugal Section / IEEE RAS ChapterSPR-Sociedade Portuguesa de Robótic

Aeronautical engineering: A continuing bibliography with indexes (supplement 309)

This bibliography lists 212 reports, articles, and other documents introduced into the NASA scientific and technical information system in Oct. 1994. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Multimodal Hierarchical Dirichlet Process-based Active Perception

In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing actions on an object. However, performing many actions on a target object requires a long time. In a real-time scenario, i.e., when the time is limited, the robot has to determine the set of actions that is most effective for recognizing a target object. We propose an MHDP-based active perception method that uses the information gain (IG) maximization criterion and lazy greedy algorithm. We show that the IG maximization criterion is optimal in the sense that the criterion is equivalent to a minimization of the expected Kullback--Leibler divergence between a final recognition state and the recognition state after the next set of actions. However, a straightforward calculation of IG is practically impossible. Therefore, we derive an efficient Monte Carlo approximation method for IG by making use of a property of the MHDP. We also show that the IG has submodular and non-decreasing properties as a set function because of the structure of the graphical model of the MHDP. Therefore, the IG maximization problem is reduced to a submodular maximization problem. This means that greedy and lazy greedy algorithms are effective and have a theoretical justification for their performance. We conducted an experiment using an upper-torso humanoid robot and a second one using synthetic data. The experimental results show that the method enables the robot to select a set of actions that allow it to recognize target objects quickly and accurately. The results support our theoretical outcomes.Comment: submitte

Visual Attention Mechanism for a Social Robot

This paper describes a visual perception system for a social robot. The central part of this system is an artificial attention mechanism that discriminates the most relevant information from all the visual information perceived by the robot. It is composed by three stages. At the preattentive stage, the concept of saliency is implemented based on ‘proto-objects’ [37]. From these objects, different saliency maps are generated. Then, the semiattentive stage identifies and tracks significant items according to the tasks to accomplish. This tracking process allows to implement the ‘inhibition of return’. Finally, the attentive stage fixes the field of attention to the most relevant object depending on the behaviours to carry out. Three behaviours have been implemented and tested which allow the robot to detect visual landmarks in an initially unknown environment, and to recognize and capture the upper-body motion of people interested in interact with it

Solving inverse problems for medical applications

It is essential to have an accurate feedback system to improve the navigation of surgical tools. This thesis investigates how to solve inverse problems using the example of two medical prototypes. The first aims to detect the Sentinel Lymph Node (SLN) during the biopsy. This will allow the surgeon to remove the SLN with a small incision, reducing trauma to the patient. The second investigates how to extract depth and tissue characteristic information during bone ablation using the emitted acoustic wave. We solved inverse problems to find our desired solution. For this purpose, we investigated three approaches: In Chapter 3, we had a good simulation of the forward problem; namely, we used a fingerprinting algorithm. Therefore, we compared the measurement with the simulations of the forward problem, and the simulation that was most similar to the measurement was a good approximation. To do so, we used a dictionary of solutions, which has a high computational speed. However, depending on how fine the grid is, it takes a long time to simulate all the solutions of the forward problem. Therefore, a lot of memory is needed to access the dictionary. In Chapter 4, we examined the Adaptive Eigenspace method for solving the Helmholtz equation (Fourier transformed wave equation). Here we used a Conjugate quasi-Newton (CqN) algorithm. We solved the Helmholtz equation and reconstructed the source shape and the medium velocity by using the acoustic wave at the boundary of the area of interest. We accomplished this in a 2D model. We note, that the computation for the 3D model was very long and expensive. In addition, we simplified some conditions and could not confirm the results of our simulations in an ex-vivo experiment. In Chapter 5, we developed a different approach. We conducted multiple experiments and acquired many acoustic measurements during the ablation process. Then we trained a Neural Network (NN) to predict the ablation depth in an end-to-end model. The computational cost of predicting the depth is relatively low once the training is complete. An end-to-end network requires almost no pre-processing. However, there were some drawbacks, e.g., it is cumbersome to obtain the ground truth. This thesis has investigated several approaches to solving inverse problems in medical applications. From Chapter 3 we conclude that if the forward problem is well known, we can drastically improve the speed of the algorithm by using the fingerprinting algorithm. This is ideal for reconstructing a position or using it as a first guess for more complex reconstructions. The conclusion of Chapter 4 is that we can drastically reduce the number of unknown parameters using Adaptive Eigenspace method. In addition, we were able to reconstruct the medium velocity and the acoustic wave generator. However, the model is expensive for 3D simulations. Also, the number of transducers required for the setup was not applicable to our intended setup. In Chapter 5 we found a correlation between the depth of the laser cut and the acoustic wave using only a single air-coupled transducer. This encourages further investigation to characterize the tissue during the ablation process