Autonomous boat dynamics: how far away is simulation from the high sea?

The study demonstrates the process of implementing a 3-degrees-of-freedom surge-sway-yaw boat dynamic model in a numeric simulation environment. Estimated environmental disturbance force introduced in the simulation provides a scope for determining boat thrust force range and thrust angle range. The basic simulation framework allows the designer of a small robotic boat to change control logics in relation to the actuator (thruster) layout without the construction of a prototype. The study draws on the key assumptions of hydrodynamic added masses and damping coefficients, and indicates ways to estimate these parameters. The framework offers a starting point for anyone working on mechanical design of a robotic test boat for developing any control algorithms

Particle swarm optimization for cooperative multi-robot task allocation: a multi-objective approach

This paper presents a new Multi-Objective Particle Swarm Optimization (MOPSO) approach to a Cooperative Multi Robot Task Allocation (CMRTA) problem, where the robots have to minimize the total team cost and, additionally, balance their workloads. We formulate the CMRTA problem as a more complex variant of multiple Travelling Salesman Problems (mTSP) and, in particular, address how to minimize the total travel distance of the entire robot team, as well as how to minimize the highest travel distance of an individual robot. The proposed approach extends the standard single-objective Particle Swarm Optimization (PSO) to cope with the multiple objectives, and its novel feature lies in a Pareto front refinement strategy and a probability-based leader selection strategy. To validate the proposed approach, we first use three benchmark functions to evaluate the performance of finding the true Pareto fronts in comparison with four existing well-known algorithms in continuous spaces. Afterwards, we use six datasets to investigate the task allocation mechanisms in dealing with the CMRTA problem in discrete spaces.benchmark functions to evaluate the performance of findingthe true Pareto fronts in comparison with four existing wellknownalgorithms in continuous spaces. Afterwards, we use sixdatasets to investigate the task allocation mechanisms in dealingwith the CMRTA problem in discrete spaces

Development of tangible acoustic interfaces for human computer interaction

Tangible interfaces, such as keyboards, mice, touch pads, and touch screens, are widely used in human computer interaction. A common disadvantage with these devices is the presence of mechanical or electronic devices at the point of interaction with the interface. The aim of this work has been to investigate and develop new tangible interfaces that can be adapted to virtually any surface, by acquiring and studying the acoustic vibrations produced by the interaction of the user's finger on the surface. Various approaches have been investigated in this work, including the popular time difference of arrival (TDOA) method, time-frequency analysis of dispersive velocities, the time reversal method, and continuous object tracking. The received signal due to a tap at a source position can be considered the impulse response function of the wave propagation between the source and the receiver. With the time reversal theory, the signals induced by impacts from one position contain the unique and consistent information that forms its signature. A pattern matching method, named Location Template Matching (LTM), has been developed to identify the signature of the received signals from different individual positions. Various experiments have been performed for different purposes, such as consistency testing, acquisition configuration, and accuracy of recognition. Eventually, this can be used to implement HCI applications on any arbitrary surfaces, including those of 3D objects and inhomogeneous materials. The resolution with the LTM method has been studied by different experiments, investigating factors such as optimal sensor configurations and the limitation of materials. On plates of the same material, the thickness is the essential determinant of resolution. With the knowledge of resolution for one material, a simple but faster search method becomes feasible to reduce the computation. Multiple simultaneous impacts are also recognisable in certain cases. The TDOA method has also been evaluated with two conventional approaches. Taking into account the dispersive properties of the vibration propagation in plates, time-frequency analysis, with continuous wavelet transformation, has been employed for the accurate localising of dispersive signals. In addition, a statistical estimation of maximum likelihood has been developed to improve the accuracy and reliability of acoustic localisation. A method to measure and verify the dispersive velocities has also been introduced. To enable the commonly required "drag & drop" function in the operation of graphical user interface (GUI) software, the tracking of a finger scratching on a surface needs to be implemented. To minimise the tracking error, a priori knowledge of previous measurements of source locations is needed to linearise the state model that enables prediction of the location of the contact point and the direction of movement. An adaptive Kalman filter has been used for this purpose.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

On fault diagnosis for high-g accelerometers via data-driven models

Shock test is a pivotal stage for designing and manufacturing space instruments. As the essential components in shock test systems to measure shock signals accurately, high-g accelerometers are usually exposed to hazardous shock environment and could be subjected to various damages. Owing to that these damages to the accelerometers could result in erroneous measurements which would further lead to shock test failures, accurately diagnosing the fault type of each high-g accelerometer can be vital to ensure the reliability of the shock test experiments. Additionally, in practice, an accelerometer in one malfunction form usually outputs mutable signal waveforms, so that it is difficult to empirically judge the fault type of the accelerometer based on the erroneous readings. Moreover, traditional hardware diagnosis approaches require disassembling the sensor’s package shell and manually observing the damage of the elements inner the sensor, which are less-efficient and uneconomical. Aiming at these problems, several data-driven approaches are incorporated to diagnose the fault types of high-g accelerometers in this work. Firstly, several high-g accelerometers with most frequent types of damage are collected, and a shock signal dataset is gathered by conducting shock tests on these faulty accelerometers. Then, the obtained dataset is used to train several base classifiers to identify the fault types in a supervised fashion. Lastly, a hybrid ensemble learning model is established by integrating these base classifiers with both heterogeneous and homogeneous models. Experimental results show that these data-driven methods can accurately identify the fault types of high-g accelerometers from their mutable erroneous readings

Distributed human 3D pose estimation and action recognition.

In this paper, we propose a distributed solution for3D human pose estimation using a RGBD camera network. Thekey feature of our method is a dynamic hybrid consensus filter(DHCF) is introduced to fuse the multiple view informationof cameras. In contrast to the centralized fusion solution,the DHCF algorithm can be used in a distributed network,which requires no central information fusion center. Therefore,the DHCF based fusion algorithm can benefit from manyadvantages of distributed network. We also show that theproposed fusion algorithm can handle the occlusion problemseffectively, and achieve higher action recognition rate comparedto the ones using only single view information

Development of tangible acoustic interfaces for human computer interaction

Tangible interfaces, such as keyboards, mice, touch pads, and touch screens, are widely used in human computer interaction. A common disadvantage with these devices is the presence of mechanical or electronic devices at the point of interaction with the interface. The aim of this work has been to investigate and develop new tangible interfaces that can be adapted to virtually any surface, by acquiring and studying the acoustic vibrations produced by the interaction of the user's finger on the surface. Various approaches have been investigated in this work, including the popular time difference of arrival (TDOA) method, time-frequency analysis of dispersive velocities, the time reversal method, and continuous object tracking. The received signal due to a tap at a source position can be considered the impulse response function of the wave propagation between the source and the receiver. With the time reversal theory, the signals induced by impacts from one position contain the unique and consistent information that forms its signature. A pattern matching method, named Location Template Matching (LTM), has been developed to identify the signature of the received signals from different individual positions. Various experiments have been performed for different purposes, such as consistency testing, acquisition configuration, and accuracy of recognition. Eventually, this can be used to implement HCI applications on any arbitrary surfaces, including those of 3D objects and inhomogeneous materials. The resolution with the LTM method has been studied by different experiments, investigating factors such as optimal sensor configurations and the limitation of materials. On plates of the same material, the thickness is the essential determinant of resolution. With the knowledge of resolution for one material, a simple but faster search method becomes feasible to reduce the computation. Multiple simultaneous impacts are also recognisable in certain cases. The TDOA method has also been evaluated with two conventional approaches. Taking into account the dispersive properties of the vibration propagation in plates, time-frequency analysis, with continuous wavelet transformation, has been employed for the accurate localising of dispersive signals. In addition, a statistical estimation of maximum likelihood has been developed to improve the accuracy and reliability of acoustic localisation. A method to measure and verify the dispersive velocities has also been introduced. To enable the commonly required "drag & drop" function in the operation of graphical user interface (GUI) software, the tracking of a finger scratching on a surface needs to be implemented. To minimise the tracking error, a priori knowledge of previous measurements of source locations is needed to linearise the state model that enables prediction of the location of the contact point and the direction of movement. An adaptive Kalman filter has been used for this purpose

Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective

As a popular concept proposed in the field of psychology, affordance has been regarded as one of the important abilities that enable humans to understand and interact with the environment. Briefly, it captures the possibilities and effects of the actions of an agent applied to a specific object or, more generally, a part of the environment. This paper provides a short review of the recent developments of deep robotic affordance learning (DRAL), which aims to develop data-driven methods that use the concept of affordance to aid in robotic tasks. We first classify these papers from a reinforcement learning (RL) perspective, and draw connections between RL and affordances. The technical details of each category are discussed and their limitations identified. We further summarise them and identify future challenges from the aspects of observations, actions, affordance representation, data-collection and real-world deployment. A final remark is given at the end to propose a promising future direction of the RL-based affordance definition to include the predictions of arbitrary action consequences.Comment: This paper is under revie

Abstract Demonstrations and Adaptive Exploration for Efficient and Stable Multi-step Sparse Reward Reinforcement Learning

Although Deep Reinforcement Learning (DRL) has been popular in many disciplines including robotics, state-of-the-art DRL algorithms still struggle to learn long-horizon, multi-step and sparse reward tasks, such as stacking several blocks given only a task-completion reward signal. To improve learning efficiency for such tasks, this paper proposes a DRL exploration technique, termed A^2, which integrates two components inspired by human experiences: Abstract demonstrations and Adaptive exploration. A^2 starts by decomposing a complex task into subtasks, and then provides the correct orders of subtasks to learn. During training, the agent explores the environment adaptively, acting more deterministically for well-mastered subtasks and more stochastically for ill-learnt subtasks. Ablation and comparative experiments are conducted on several grid-world tasks and three robotic manipulation tasks. We demonstrate that A^2 can aid popular DRL algorithms (DQN, DDPG, and SAC) to learn more efficiently and stably in these environments.Comment: Accepted by The 27th IEEE International Conference on Automation and Computing (ICAC2022