Unsupervised Contact Learning for Humanoid Estimation and Control

This work presents a method for contact state estimation using fuzzy clustering to learn contact probability for full, six-dimensional humanoid contacts. The data required for training is solely from proprioceptive sensors - endeffector contact wrench sensors and inertial measurement units (IMUs) - and the method is completely unsupervised. The resulting cluster means are used to efficiently compute the probability of contact in each of the six endeffector degrees of freedom (DoFs) independently. This clustering-based contact probability estimator is validated in a kinematics-based base state estimator in a simulation environment with realistic added sensor noise for locomotion over rough, low-friction terrain on which the robot is subject to foot slip and rotation. The proposed base state estimator which utilizes these six DoF contact probability estimates is shown to perform considerably better than that which determines kinematic contact constraints purely based on measured normal force.Comment: Submitted to the IEEE International Conference on Robotics and Automation (ICRA) 201

Unsupervised Contact Learning for Humanoid Estimation and Control

This work presents a method for contact state estimation using fuzzy clustering to learn contact probability for full, six-dimensional humanoid contacts. The data required for training is solely from proprioceptive sensors - endeffector contact wrench sensors and inertial measurement units (IMUs) - and the method is completely unsupervised. The resulting cluster means are used to efficiently compute the probability of contact in each of the six endeffector degrees of freedom (DoFs) independently. This clustering-based contact probability estimator is validated in a kinematics-based base state estimator in a simulation environment with realistic added sensor noise for locomotion over rough, low-friction terrain on which the robot is subject to foot slip and rotation. The proposed base state estimator which utilizes these six DoF contact probability estimates is shown to perform considerably better than that which determines kinematic contact constraints purely based on measured normal force.Comment: Submitted to the IEEE International Conference on Robotics and Automation (ICRA) 201

State estimation of over-sensored systems applied to a low-cost robotic manipulator

There is an increasing demand for robotic manipulators to perform more complex and versatile tasks. In order to fulfill this need, expeditious calibration and estimation techniques are required as a first step for the correct usage of the manipulator. This article aims at finding a subset of these algorithms that could be used in a generic manipulator and should allow for its prompt use. Two models for the representation of the pose of the manipulator are described and used in the state estimation problem. The results of the implementation are tested, and some performance metrics are obtained.info:eu-repo/semantics/publishedVersio

High-Order Robotic Joint Sensing with Multiple Accelerometer and Gyroscope Systems

In recent years work into larger humanoid robotic systems and other highly dynamic legged robots has driven a need to increase control system performance and parameter estimation capability. This in turn has seen an increase in the use of higher order joint space derivative terms such as acceleration and jerk being introduced into the control systems and estimators. Although it is evident that the inclusion of these terms can increase the performance of the estimators and control systems, there is a distinct lack of high quality sensors or systems capable of providing this information. Instead it is apparent that those researchers aiming to employ the acceleration and jerk terms are having to resort to other poor quality methods of acquiring the information, which in turn limits the capability of the systems. The works examined suggest that in particular, access to higher quality sources of joint space acceleration measurement or estimation can lead to increases in the performance of control systems and estimators employing these terms. The aim of this work is to investigate the feasibility and capability of a new joint space sensor based on positional encoders and MEMs accelerometers that can estimate angular joint position, velocity and acceleration. The system proposed employs the accelerometer only IMU (AO-IMU) concept to estimate link angular acceleration and velocity in an inertial frame. This concept is then extended to obtain these angular components relative to the previous link. Sensor fusion techniques are then tasked with estimating the velocity states of the AO-IMU and ensuring consistency across the relative states. Two calibration schemes are proposed and demonstrated to correct for the bias, gain and cross axis effects present in the inertial sensors and to correct for the non-ideal placement of the sensors on the body frame. The performance of the system is compared to three online methods common in the literature with significant increases in performance being shown across all states, particularly in the acceleration and velocity states. The base sensor system is then augmented to explore alternate inertial sensor arrangements and structures. In this the effects of adding MEMs gyroscopes to the sensor system are studied and shown to have a small positive effect on the relative velocity state. The addition of multiple relative accelerometers are then studied to examine whether the initial system design choices could be improved upon, with this study showing greater increases in the relative acceleration and velocity states performance. Taking inspiration from the positive results of the multiple relative accelerometer study, an alternate sensor system structure is proposed whereby the robot is instrumented with AO-IMUs and the relative accelerometers omitted. This augmented structure may prove more useful in larger robotic systems. This study initially showed poor results with the low angular velocities experienced by the upper link AO-IMU introducing bias errors. This was corrected for by the inclusion of gyroscopes with the resulting system exhibiting good performance. The findings within this work show that with some modification, the AO-IMU is capable of directly measuring the relative angular acceleration and velocity of a robotic link. When combined with positional sensors this system can be extended to obtain high quality measurements of a joint’s angular position, velocity and acceleration.Thesis (MPhil) -- University of Adelaide, School of Mechanical Engineering, 201

Artificial Intelligence and Ambient Intelligence

This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on “Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules”, presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots’ ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science