A method for actuator comparison for a stable humanoid robot

A humanoid robot is being developed, intended to operate robustly in a typical day-to-day human environment. Its ability to react quickly to stochastic disturbances, especially its ability to avoid falling, termed stability in this context, is of prime importance. The choosing of a suitable actuator that affords such stability is seen as a...

A Deep Learning Prediction Model for Object Classification

The last decade has marked a rapid and significant growth of the global market of warehouse automation. The biggest challenge lies in the identification and handling of foreign objects. The aim of this research is to investigate whether a usable relation exist between object features such as size or shape, and barcode location, that can be used...

Comparison of Optimal Control Techniques for Learning-based RRT

Kinodynamic motion planning for a robot involves generating a trajectory from a given robot state to goal state while satisfying kinematic and dynamic constraints. Rapidly-exploring Random Trees (RRT) is a sampling-based algorithm that has been widely adopted for this. However, RRT is not fast enough to enable its use in industrial applications....

The Ring Screw: Modelling, Development and Evaluation of a Novel Screw Transmission

Rotational electric actuators are the most popular choice for actuation in modern day automation. These actuators are commonly used in combination with a transmission system. In this paper, the development of a new rotational to linear transmission technology, called the ring screw, is described, and its real-world performance is evaluated...

Intrinsically Passive Control for Ultrasound-guided Robotic Scanning of the Breast

Automated breast ultrasound imaging systems provide the ability to combine the high anatomical detail of preoperative imaging with real-time intraoperative imaging. Ultrasound imaging involves interaction between the transducer and the patient, which requires a compliant behaviour of the robot in manipulating the transducer. We present an energy...

Vision-based Terrain Segmentation and Roughness Estimation: Application on the CENTAURO Robot

Intelligent terrain perception for search-and-rescue robotic applications, requires a high-level understanding of both the terrain type and its chief physical characteristics. Roughness is one such important terrain property, since it could play a key role in robot control/planning strategies, while navigating<br/>in an unknown environment. In...

Control Strategies for Grasping and Holding Deformable Items Using a Gripper on a Robotic Arm

Because of the increasing e-commerce volume, resulting in increasing demands on the speed of delivery, logistical processes become more and more automated. Order picking is one of the last tasks that is done by humans in warehouses, because humans are flexible with respect to the large variability and changeability in items. However, it is a...

Hierarchical Reinforcement Learning for Spatio-temporal Planning

Reinforcement learning (RL) is an area of Machine Learning (ML) concerned with learning how a software-defined agent should act in an environment to maximize the rewards. Similar to many ML methods, RL suffers from the curse of dimensionality, the exponential increase in solution space with the increase in problem dimensions. Learning the...

The Learning of Linear Dynamical State Space Models According to the Principles of Free Energy: The improvement of acceptance and utilization of the Machine Learning technique: Expectation Maximization in Control theory

The Free Energy principle represents a Neuroscience theory that unlike any other theory can explain all behavioral aspects of adaptive agents (like humans). The issue of behavior is made computationally tractable by considering behavioral processes as various optimization problems with a single objective: minimization of Free Energy. The...

Suction Grasp Pose Planning Using Self-supervision and Transfer Learning

Planning grasp poses for a robot on unknown objects in cluttered environments is still an open problem. Recent research suggests that deep learning technique is a promising approach to plan grasp poses on unknown objects in cluttered environments. In this field, three types of data are used for training: (a) human labeled data; (b) synthetic...

POMDP based online parameter estimation for autonomous passenger vehicles: Researching online tyre parameter estimation performance by improving the trajectory using a POMDP algorithm.

The internal model is an important piece of the control system of an autonomous driving vehicle. In order for the model to deliver accurate predictions, a valid model structure and well chosen parameters are needed. Model parameters can be highly fluctuating or complex to predict, especially when looking into tyre ground surface interaction...

Online Vehicle Inertial Parameter Estimation: Testing Rozyn’s Algorithm Under More Realistic Conditions

The inertial parameters of a vehicle, which include the mass, centre of gravity position and the moments of inertia, influences the dynamics of the vehicle. Currently, the modelling of the vehicle is done by assuming fixed, conservative, values for the inertial parameters. Knowing the exact values may increase the performance, safety and comfort...

The Noisy Jackal: Measurement and analysis of the coloured noise on the longitudinal, lateral and rotational velocities and identifcation of its characteristics due to the unmodeled dynamics of a skid steer mobile robot during a steady-state turning manoeuvre

This research focuses on proving the presence of coloured noise on the longitudinal, lateral and rotational velocity in steady-state cornering of a skid steer mobile robot. Furthermore, it also focuses on the creation of a Gaussian filter which is able to recreate the characteristics of the measured coloured noise. This is done to determine the...