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

Model predictive control for slurry pipeline transportation of a cutter suction dredger

Cutter Suction Dredgers (CSDs) are a special type of ships designed for construction and maintenance projects of ocean and offshore engineering. During the dredging operation, CSDs can excavate nearly all kinds of soil on the sea bed, and then the dredged materials with coarse particles need to be sucked up by a slurry pump and transported to a disposal area through a long-distance pipeline. In order to avoid sedimentation of slurry in pipeline transportation, the flow rate must be maintained within a reasonable range. Otherwise, the pipeline can be blocked when the slurry density is too high. In this paper, we present a Model Predictive Control (MPC) approach to manipulate the flow rate of slurry in pipeline transportation for a CSD. To demonstrate the advantages of our proposed approach, we also implement three Proportional–Integral–Derivative (PID) controllers (i.e., conventional PID, Fuzzy-PID, and LQR-PID) to make a direct comparison. Moreover, in order to evaluate the effectiveness of our proposed approach in real scenarios, we have, in particular, built a slurry pipeline transportation platform. Both the simulation and experimental results show that our proposed MPC approach is more effective than other PID controllers in controlling the flow rate in the slurry pipeline transportation problem. The proposed approach can provide a guideline for the automated control of the slurry pump for a CSD

Distributed adaptive leader–follower and leaderless consensus control of a class of strict-feedback nonlinear systems : a unified approach

Author's accepted manuscript.Available from 16/05/2022.acceptedVersio

Adaptive control of uncertain nonlinear systems with quantized input signal

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Localisation-safe reinforcement learning for mapless navigation

Most reinforcement learning (RL)-based works for mapless point goal navigation tasks assume the availability of the robot ground-truth poses, which is unrealistic for real world applications. In this work, we remove such an assumption and deploy observation-based localisation algorithms, such as Lidar-based or visual odometry, for robot self-pose estimation. These algorithms, despite having widely achieved promising performance and being robust to various harsh environments, may fail to track robot locations under many scenarios, where observations perceived along robot trajectories are insufficient or ambiguous. Hence, using such localisation algorithms will introduce new unstudied problems for mapless navigation tasks. This work will propose a new RL-based algorithm, with which robots learn to navigate in a way that prevents localisation failures or getting trapped in local minimum regions. This ability can be learned by deploying two techniques suggested in this work: a reward metric to decide punishment on behaviours resulting in localisation failures; and a reconfigured state representation that consists of current observation and history trajectory information to transfer the problem from a partially observable Markov decision process (POMDP) to a Markov Decision Process (MDP) model to avoid local minimum

Reinforcement learning-based mapless navigation with fail-safe localisation

Mapless navigation is the capability of a robot to navigate without knowing the map. Previous works assume the availability of accurate self-localisation, which is, however, usually unrealistic. In our work, we deploy simultaneous localisation and mapping (SLAM)-based self-localisation for mapless navigation. SLAM performance is prone to the quality of perceived features of the surroundings. This work presents a Reinforcement Learning (RL)-based mapless navigation algorithm, aiming to improve the robustness of robot localisation by encouraging the robot to learn to be aware of the quality of its surrounding features and avoid feature-poor environment, where localisation is less reliable. Particle filter (PF) is deployed for pose estimation in our work, although, in principle, any localisation algorithm should work with this framework. The aim of the work is two-fold: to train a robot to learn 1) to avoid collisions and also 2) to identify paths that optimise PF-based localisation, such that the robot will be unlikely to fail to localise itself, hence fail-safe SLAM. A simulation environment is tested in this work with different maps and randomised training conditions. The trained policy has demonstrated superior performance compared with standard mapless navigation without this optimised policy

Data-driven offline learning approach for excavating control of cutter suction dredgers

Cutter suction dredgers (CSDs) play a very important role in the construction of ports, waterways and navigational channels. Currently, most of CSDs are mainly manipulated by human operators, and a large amount of instrument data needs to be monitored in real time in case of unforeseen accidents. In order to reduce the heavy workload of the operators, we propose a data-driven offline learning approach, named Preprocessing-Prediction-Learning Control (PPLC), for obtaining the optimal control policy of the excavating operation of CSDs. The proposed framework consists of three modules, i.e., a data preprocessing module, a dynamics prediction module realized by a Convolutional Neural Network (CNN), and a deep reinforcement learning based control module. The first module is responsible for filtering out irrelevant variables through correlation analysis and dimensionality reduction of raw data. The second module works as a state transition function that provides the dynamics prediction of the excavating operation of a CSD. To realize the learning control, the third module employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to control the swing speed during the excavating operation. The simulation results show that the proposed framework can provide an effective and reliable solution to the automated excavating control of a CSD

Transcription Factor Crosstalk and Regulatory Networks in Hypopharyngeal Squamous Cell Carcinoma

licenses/by-nc-nd/3.0/). Reproduction is permitted for personal, noncommercial use, provided that the article is in whole, unmodified, and properly cited. Received: 2014.03.24; Accepted: 2014.04.18; Published: 2014.06.16 To date, no effective therapeutic treatments have been developed for hypopharyngeal squamous cell carcinoma (HPSCC), a disease that has a five-year survival rate of approximately 31 % because of its late diagnosis and aggressive nature. Despite recent improvements in diagnostic methods, there are no effective measures to prevent or detect HPSCC in an early stage. The goal of the current study was to identify molecular biomarkers and networks that can facilitate the speedy identification of HPSCC patients who could benefit from individualized treatment. Isobaric tags for relative and absolute quantification (iTRAQ) labeling was employed with two-dimensional liquid chromatography-tandem mass spectrometry to identify quantitatively the differentially expressed proteins among three types of HPSCC disease stages. The iTRAQ results were evaluated by literature searches and western blot analysis. For example, FUBP1, one of 412 proteins with significantl

Fully Photonic Integrated Wearable Optical Interrogator

Wearable technology constitutes a pioneering and leading innovation and a market development platform worldwide for technologies worn close to the body. Wearable optical fiber sensors have the most value for advanced multiparameter sensing in digital health monitoring systems. We demonstrated the first example of a fully integrated optical interrogator. By integrating all the optical components on a silicon photonic chip, we realized a stable, miniaturized and low-cost optical interrogator for the continuous, dynamic, and long-term acquisition of human physiological signals. The interrogator was integrated in a wristband, enabling the detection of body temperature and heart sounds. Our study paves the way for the development of watch-sized integrated wearable optical interrogators with potential applications in health monitoring and can be directly exploited for the customized design of ultraminiaturized optical interrogator systems.H.L. acknowledges the support from the Tianjin Talent Special Support Program. J.D.P.G. acknowledges the support from the Serra Hunter Program, the ICREA Academia Program, and the Tianjin Distinguished University Professor Program. This work was supported by the National Natural Science Foundation of China (no. 61675154), the Tianjin Key Research and Development Program (no. 19YFZCSY00180), the Tianjin Major Project for Civil-Military Integration of Science and Technology (no. 18ZXJMTG00260), the Tianjin Science and Technology Program (no. 20YDTPJC01380), and the Tianjin Municipal Special Foundation for Key Cultivation of China (no. XB202007)