Learning Probabilistic Features for Robotic Navigation Using Laser Sensors

SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N2), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.

RGBD Human-Hand recognition for the Interaction with Robot-Hand

New low cost sensors and the new open free libraries for 3D image processing are permitting to achieve important advances for robot vision applications such as tridimensional object recognition, semantic mapping, navigation and localization of robots, human detection and/or gesture recognition for human-machine interaction. In this paper, a method to recognize the human hand and to track the fingers is proposed. This new method is based on point clouds from range images, RGBD. It does not require visual marks, camera calibration, environment knowledge and complex expensive acquisition systems. Furthermore, this method has been implemented to create a human interface in order to move a robot hand. The human hand is recognized and the movement of the fingers is analyzed. Afterwards, it is imitated from a Barret hand, using communication events programmed from ROS.

An improvement of a SLAM RGB-D method with movement prediction derived from a study of visual features

This paper presents a method for the fast calculation of a robot’s egomotion using visual features. The method is part of a complete system for automatic map building and Simultaneous Location and Mapping (SLAM). The method uses optical flow to determine whether the robot has undergone a movement. If so, some visual features that do not satisfy several criteria are deleted, and then egomotion is calculated. Thus, the proposed method improves the efficiency of the whole process because not all the data is processed. We use a state-of-the-art algorithm (TORO) to rectify the map and solve the SLAM problem. Additionally, a study of different visual detectors and descriptors has been conducted to identify which of them are more suitable for the SLAM problem. Finally, a navigation method is described using the map obtained from the SLAM solution.

3DCOMET: 3D compression methods test dataset

The use of 3D data in mobile robotics applications provides valuable information about the robot’s environment. However usually the huge amount of 3D information is difficult to manage due to the fact that the robot storage system and computing capabilities are insufficient. Therefore, a data compression method is necessary to store and process this information while preserving as much information as possible. A few methods have been proposed to compress 3D information. Nevertheless, there does not exist a consistent public benchmark for comparing the results (compression level, distance reconstructed error, etc.) obtained with different methods. In this paper, we propose a dataset composed of a set of 3D point clouds with different structure and texture variability to evaluate the results obtained from 3D data compression methods. We also provide useful tools for comparing compression methods, using as a baseline the results obtained by existing relevant compression methods.

Visual perception for the 3D recognition of geometric pieces in robotic manipulation

During grasping and intelligent robotic manipulation tasks, the camera position relative to the scene changes dramatically because the robot is moving to adapt its path and correctly grasp objects. This is because the camera is mounted at the robot effector. For this reason, in this type of environment, a visual recognition system must be implemented to recognize and “automatically and autonomously” obtain the positions of objects in the scene. Furthermore, in industrial environments, all objects that are manipulated by robots are made of the same material and cannot be differentiated by features such as texture or color. In this work, first, a study and analysis of 3D recognition descriptors has been completed for application in these environments. Second, a visual recognition system designed from specific distributed client-server architecture has been proposed to be applied in the recognition process of industrial objects without these appearance features. Our system has been implemented to overcome problems of recognition when the objects can only be recognized by geometric shape and the simplicity of shapes could create ambiguity. Finally, some real tests are performed and illustrated to verify the satisfactory performance of the proposed system.

Intraspecific Behavioural Diversity in a Freshwater Zooplankton: A study of the fitness consequences of vertical migration behaviour for distinct morphs of Daphnia pulicaria

The literature on niche separation and coexistence between species is large, but there is widespread variation in behavioural strategy between individuals of the same species that has received much less attention. Understanding what maintains this diversity is important because intraspecific behavioural diversity can affect population dynamics and community interactions. Multiple behavioural strategies can arise either as phenotype-dependent ‘conditional strategies’, where phenotypic variation causes individuals to adopt different strategies for optimizing fitness, or as internally-independent ‘alternative strategies’, where multiple fitness peaks exist for individuals and strategic ‘choice’ remains plastic. Though intraspecific variation in stable phenotypes is known to maintain intraspecific behavioural diversity through conditional strategies, when internal conditions are highly plastic or reversible, it is not clear whether individual behaviours are maintained as conditional strategies, or as alternative strategies of equal fitness. In this study, I combine an observational and experimental approach to identify the likely mechanisms maintaining behavioural diversity between hemoglobin-rich and hemoglobin-poor morphs in a natural population of Daphnia pulicaria. In Round Lake, individuals with low hemoglobin migrate daily from the hypolimnion to the epilimnion, whereas individuals with high hemoglobin remain in the hypolimnion. Using high-resolution depth and time sampling, I discovered behavioural diversity both within and among hemoglobin phenotypes. I tested the role of hemoglobin phenotype in maintaining behavioural diversity using automated migration robots that move individuals across the natural environmental gradients in the lake. By measuring the fitness of each morph undergoing either a natural migration behaviour, or the migration of the opposite morph, I found that the fitness of hemoglobin rich and poor morphs in their natural behaviour does not differ, but that Hb-rich individuals can obtain equal fitness from either behaviour, while Hb-poor morphs suffer substantial drops in survivorship in the alternate migration behaviour. Thus, migration behaviour in this system exists as a conditional strategy for some individuals, and as alternative strategies of equal fitness for others. The results of this study suggest that individual limits in the expression of highly flexible internal conditions can reinforce intraspecific behavioural diversity. Few studies have measured the fitness consequences of switching migration strategies and this study provides a rare example in the field.

The treatise of al-Jazarī on automata; leaves from a manuscript of the Kitāb fī maʻarifat al-hiyal al handasiya in the Museum of fine arts. Boston, and elsewhere,

Plates, accompanied by unnumbered leaf, laid in.

Development and evaluation of azobis-blown foam for molding Part 572 dummy flesh components. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Machine intelligence and robotics : report of the NASA Study Group : final report.

Mode of access: Internet.

Safe maintenance guidelines for robotic workstations /

Mode of access: Internet.

An architecture for robot hierarchical control system /

Mode of access: Internet.

Observations on the automaton chess player, now exhibited in London, at 4, Spring Gardens.

Mode of access: Internet.

Multi-robot control in highly dynamic, competitive environments

The control and coordination of multiple mobile robots is a challenging task; particularly in environments with multiple, rapidly moving obstacles and agents. This paper describes a robust approach to multi-robot control, where robustness is gained from competency at every layer of robot control. The layers are: (i) a central coordination system (MAPS), (ii) an action system (AES), (iii) a navigation module, and (iv) a low level dynamic motion control system. The multi-robot coordination system assigns each robot a role and a sub-goal. Each robot’s action execution system then assumes the assigned role and attempts to achieve the specified sub-goal. The robot’s navigation system directs the robot to specific goal locations while ensuring that the robot avoids any obstacles. The motion system maps the heading and speed information from the navigation system to force-constrained motion. This multi-robot system has been extensively tested and applied in the robot soccer domain using both centralized and distributed coordination.

Probabilistic world modelling for distributed team planning

This paper describes an application of decoupled probabilistic world modeling to achieve team planning. The research is based on the principle that tbe action selection mechanism of a member in a robot team cm select am effective action if a global world model is available to all team members. In the real world, the sensors are imprecise, and are individual to each robot, hence providing each robot a partial and unique view about the environment. We address this problem by creating a probabilistic global view on each agent by combining the perceptual information from each robot. This probsbilistie view forms the basis for selecting actions to achieve the team goal in a dynamic environment. Experiments have been carried ont to investigate the effectiveness of this principle using custom-built robots for real world performance, in addition, to extensive simulation results. The results show an improvement in team effectiveness when using probabilistic world modeling based on perception sharing for team planning.

Tuning pattern classifier parameters using a genetic algorithm with an application in mobile robotics

Support vector machines (SVMs) have recently emerged as a powerful technique for solving problems in pattern classification and regression. Best performance is obtained from the SVM its parameters have their values optimally set. In practice, good parameter settings are usually obtained by a lengthy process of trial and error. This paper describes the use of genetic algorithm to evolve these parameter settings for an application in mobile robotics.