World Modeling for Intelligent Autonomous Systems

The functioning of intelligent autonomous systems requires constant situation awareness and cognition analysis. Thus, it needs a memory structure that contains a description of the surrounding environment (world model) and serves as a central information hub. This book presents a row of theoretical and experimental results in the field of world modeling. This includes areas of dynamic and prior knowledge modeling, information fusion, management and qualitative/quantitative information analysis

World Modeling for Intelligent Autonomous Systems

The functioning of intelligent autonomous systems requires constant situation awareness and cognition analysis. Thus, it needs a memory structure that contains a description of the surrounding environment (world model) and serves as a central information hub. This book presents a row of theoretical and experimental results in the field of world modeling. This includes areas of dynamic and prior knowledge modeling, information fusion, management and qualitative/quantitative information analysis

On Providing Efficient Real-Time Solutions to Motion Planning Problems of High Complexity

The holy grail of robotics is producing robotic systems capable of efficiently executing all the tasks that are hard, or even impossible, for humans. Humans, undoubtedly, from both a hardware and software perspective, are extremely complex systems capable of executing many complicated tasks. Thus, the complexity of many state-of-the-art robotic systems is also expected to progressively increase, with the goal to match or even surpass human abilities. Recent developments have emphasized mostly hardware, providing highly complex robots with exceptional capabilities. On the other hand, they have illustrated that one important bottleneck of realizing such systems as a common reality is real-time motion planning. This thesis aims to assist the development of complex robotic systems from a computational perspective. The primary focus is developing novel methodologies to address real-time motion planning that enables the robots to accomplish their goals safely and provide the building blocks for developing robust advanced robot behavior in the future. The proposed methods utilize and enhance state-of-the-art approaches to overcome three different types of complexity: 1. Motion planning for high-dimensional systems. RRT+, a new family of general sampling-based planners, was introduced to accelerate solving the motion planning problem for robotic systems with many degrees of freedom by iteratively searching in lowerdimensional subspaces of increasing dimension. RRT+ variants computed solutions orders of magnitude faster compared to state-of-the-art planners. Experiments in simulation of kinematic chains up to 50 degrees of freedom, and the Baxter humanoid robot validate the effectiveness of the proposed technique. 2. Underwater navigation for robots in cluttered environments. AquaNav, a real-time navigation pipeline for robots moving efficiently in challenging, unknown, and unstructured environments, was developed for Aqua2, a hexapod swimming robot with complex, yet to be fully discovered, dynamics. AquaNav was tested offline in known maps, and online in unknown maps utilizing vision-based SLAM. Rigorous testing in simulation, inpool, and open-water trials show the robustness of the method on providing efficient and safe performance, enabling the robot to navigate by avoiding static and dynamic obstacles in open-water settings with turbidity and surge. 3. Active perception of areas of interest during underwater operation. AquaVis, an extension of AquaNav, is a real-time navigation technique enabling robots, with arbitrary multi-sensor configurations, to safely reach their target, while at the same time observing multiple areas of interest from a desired proximity. Extensive simulations show safe behavior, and strong potential for improving underwater state estimation, monitoring, tracking, inspection, and mapping of objects of interest in the underwater domain, such as coral reefs, shipwrecks, marine life, and human infrastructure

Learning Algorithm Design for Human-Robot Skill Transfer

In this research, we develop an intelligent learning scheme for performing human-robot skills transfer. Techniques adopted in the scheme include the Dynamic Movement Prim- itive (DMP) method with Dynamic Time Warping (DTW), Gaussian Mixture Model (G- MM) with Gaussian Mixture Regression (GMR) and the Radical Basis Function Neural Networks (RBFNNs). A series of experiments are conducted on a Baxter robot, a NAO robot and a KUKA iiwa robot to verify the effectiveness of the proposed design.During the design of the intelligent learning scheme, an online tracking system is de- veloped to control the arm and head movement of the NAO robot using a Kinect sensor. The NAO robot is a humanoid robot with 5 degrees of freedom (DOF) for each arm. The joint motions of the operator’s head and arm are captured by a Kinect V2 sensor, and this information is then transferred into the workspace via the forward and inverse kinematics. In addition, to improve the tracking performance, a Kalman filter is further employed to fuse motion signals from the operator sensed by the Kinect V2 sensor and a pair of MYO armbands, so as to teleoperate the Baxter robot. In this regard, a new strategy is developed using the vector approach to accomplish a specific motion capture task. For instance, the arm motion of the operator is captured by a Kinect sensor and programmed through a processing software. Two MYO armbands with embedded inertial measurement units are worn by the operator to aid the robots in detecting and replicating the operator’s arm movements. For this purpose, the armbands help to recognize and calculate the precise velocity of motion of the operator’s arm. Additionally, a neural network based adaptive controller is designed and implemented on the Baxter robot to illustrate the validation forthe teleoperation of the Baxter robot.Subsequently, an enhanced teaching interface has been developed for the robot using DMP and GMR. Motion signals are collected from a human demonstrator via the Kinect v2 sensor, and the data is sent to a remote PC for teleoperating the Baxter robot. At this stage, the DMP is utilized to model and generalize the movements. In order to learn from multiple demonstrations, DTW is used for the preprocessing of the data recorded on the robot platform, and GMM is employed for the evaluation of DMP to generate multiple patterns after the completion of the teaching process. Next, we apply the GMR algorithm to generate a synthesized trajectory to minimize position errors in the three dimensional (3D) space. This approach has been tested by performing tasks on a KUKA iiwa and a Baxter robot, respectively.Finally, an optimized DMP is added to the teaching interface. A character recombination technology based on DMP segmentation that uses verbal command has also been developed and incorporated in a Baxter robot platform. To imitate the recorded motion signals produced by the demonstrator, the operator trains the Baxter robot by physically guiding it to complete the given task. This is repeated five times, and the generated training data set is utilized via the playback system. Subsequently, the DTW is employed to preprocess the experimental data. For modelling and overall movement control, DMP is chosen. The GMM is used to generate multiple patterns after implementing the teaching process. Next, we employ the GMR algorithm to reduce position errors in the 3D space after a synthesized trajectory has been generated. The Baxter robot, remotely controlled by the user datagram protocol (UDP) in a PC, records and reproduces every trajectory. Additionally, Dragon Natural Speaking software is adopted to transcribe the voice data. This proposed approach has been verified by enabling the Baxter robot to perform a writing task of drawing robot has been taught to write only one character

The State-of-Art of Underwater Vehicles - Theories and Applications

An autonomous underwater vehicle (AUV) is an underwater system that contains its own power and is controlled by an onboard computer. Although many names are given to these vehicles, such as remotely operated vehicles (ROVs), unmanned underwater vehicles (UUVs), submersible devices, or remote controlled submarines, to name just a few, the fundamental task for these devices is fairly well defined: The vehicle is able to follow a predefined trajectory. AUVs offer many advantages for performing difficult tasks submerged in water. The main advantage of an AUV is that is does not need a human operator. Therefore it is less expensive than a human operated vehicle and is capable of doing operations that are too dangerous for a person. They operate in conditions and perform task that humans are not able to do efficiently, or at all (Smallwood & Whitcomb, 2004; Horgan & Toal, 2006; Caccia, 2006)

The PInSoRo dataset: supporting the data-driven study of child-child and child-robot social dynamics

The study of the fine-grained social dynamics between children is a methodological challenge, yet a good understanding of how social interaction between children unfolds is important not only to Developmental and Social Psychology, but recently has become relevant to the neighbouring field of Human-Robot Interaction (HRI). Indeed, child-robot interactions are increasingly being explored in domains which require longer-term interactions, such as healthcare and education. For a robot to behave in an appropriate manner over longer time scales, its behaviours have to be contingent and meaningful to the unfolding relationship. Recognising, interpreting and generating sustained and engaging social behaviours is as such an important—and essentially, open—research question. We believe that the recent progress of machine learning opens new opportunities in terms of both analysis and synthesis of complex social dynamics. To support these approaches, we introduce in this article a novel, open dataset of child social interactions, designed with data-driven research methodologies in mind. Our data acquisition methodology relies on an engaging, methodologically sound, but purposefully underspecified free-play interaction. By doing so, we capture a rich set of behavioural patterns occurring in natural social interactions between children. The resulting dataset, called the PInSoRo dataset, comprises 45+ hours of hand-coded recordings of social interactions between 45 child-child pairs and 30 child-robot pairs. In addition to annotations of social constructs, the dataset includes fully calibrated video recordings, 3D recordings of the faces, skeletal informations, full audio recordings, as well as game interactions

World Modeling for Intelligent Autonomous Systems

Within the scope of this work, we have attained a row of theoretical and experimental results in the field of world modeling as well as gathered significant experience and expertise. The covered topics include concepts and approaches for dynamic and prior knowledge modeling, information association, fusion and management as well as their practical realization and experimental evaluation

Proceedings of the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

This book is a collection of 15 reviewed technical reports summarizing the presentations at the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. The covered topics include image processing, optical signal processing, visual inspection, pattern recognition and classification, human-machine interaction, world and situation modeling, autonomous system localization and mapping, information fusion, and trust propagation in sensor networks