Local Navigation Among Movable Obstacles with Deep Reinforcement Learning

Autonomous robots would benefit a lot by gaining the ability to manipulate their environment to solve path planning tasks, known as the Navigation Among Movable Obstacle (NAMO) problem. In this paper, we present a deep reinforcement learning approach for solving NAMO locally, near narrow passages. We train parallel agents in physics simulation using an Advantage Actor-Critic based algorithm with a multi-modal neural network. We present an online policy that is able to push obstacles in a non-axial-aligned fashion, react to unexpected obstacle dynamics in real-time, and solve the local NAMO problem. Experimental validation in simulation shows that the presented approach generalises to unseen NAMO problems in unknown environments. We further demonstrate the implementation of the policy on a real quadrupedal robot, showing that the policy can deal with real-world sensor noises and uncertainties in unseen NAMO tasks.Comment: 7 pages, 7 figures, 4 table

Image segmentation in marine environments using convolutional LSTM for temporal context

Unmanned surface vehicles (USVs) carry a wealth of possible applications, many of which are limited by the vehicle's level of autonomy. The development of efficient and robust computer vision algorithms is a key factor in improving this, as they permit autonomous detection and thereby avoidance of obstacles. Recent developments in convolutional neural networks (CNNs), and the collection of increasingly diverse datasets, present opportunities for improved computer vision algorithms requiring less data and computational power. One area of potential improvement is the utilisation of temporal context from USV camera feeds in the form of sequential video frames to consistently identify obstacles in diverse marine environments under challenging conditions. This paper documents the implementation of this through long short-term memory (LSTM) cells in existing CNN structures and the exploration of parameters affecting their efficacy. It is found that LSTM cells are promising for achieving improved performance; however, there are weaknesses associated with network training procedures and datasets. Several novel network architectures are presented and compared using a state-of-the-art benchmarking method. It is shown that LSTM cells allow for better model performance with fewer training iterations, but that this advantage diminishes with additional training

ShorelineNet: an efficient deep learning approach for shoreline semantic segmentation for unmanned surface vehicles

This paper introduces a novel deep learning approach to semantic segmentation of the shoreline environments with a high frames-per-second (fps) performance, making the approach readily applicable to autonomous navigation for Unmanned Surface Vehicles (USV). The proposed ShorelineNet is an efficient deep neural network of high performance relying only on visual input. ShorelineNet uses monocular visual input to produce accurate shoreline separation and obstacle detection compared to the state-of-the-art, and achieves this with real-time performance. Experimental validation on a challenging multi-modal maritime obstacle detection dataset, the MODD2 dataset, achieves a much faster inference (25fps on an NVIDIA Tesla K80 and 6fps on a CPU) with respect to the recent state-of-the-art methods, while keeping the performance equally high (73.1% F-score). This makes ShorelineNet a robust and effective model to be used for reliable USV navigation that require real-time and high-performance semantic segmentation of maritime environments

Research on Assessment Methods for Urban Public Transport Development in China

In recent years, with the rapid increase in urban population, the urban travel demands in Chinese cities have been increasing dramatically. As a result, developing comprehensive urban transport systems becomes an inevitable choice to meet the growing urban travel demands. In urban transport systems, public transport plays the leading role to promote sustainable urban development. This paper aims to establish an assessment index system for the development level of urban public transport consisting of a target layer, a criterion layer, and an index layer. Review on existing literature shows that methods used in evaluating urban public transport structure are dominantly qualitative. To overcome this shortcoming, fuzzy mathematics method is used for describing qualitative issues quantitatively, and AHP (analytic hierarchy process) is used to quantify expert’s subjective judgment. The assessment model is established based on the fuzzy AHP. The weight of each index is determined through the AHP and the degree of membership of each index through the fuzzy assessment method to obtain the fuzzy synthetic assessment matrix. Finally, a case study is conducted to verify the rationality and practicability of the assessment system and the proposed assessment method

Numerical calculation of the resistance of catamarans at different distances between two hulls

For the design of high-speed catamarans, different distances between slices have obvious interference with the total resistance of the catamaran. In order to accurately predict the hydrodynamic characteristics of the catamaran and explore the interference of the chip spacing on the resistance prediction, this paper uses a combination of CFD calculations and empirical formulas to predict the ship model resistance under different chip spacings and calculate them. The result is compared with the empirical formula. The results of the ship model test and the results calculated by the empirical formula were used to verify the numerical calculation results. The results show that the resistance change trend is consistent, and the numerical calculation method is effective and feasible. Finally, the numerical calculation method is compared with the ship model test method, and the result is within the error range, which has certain reference value for the design and optimization of the catamaran model parameters

Simultaneous 3D temperature and velocity field measurements of micro-flow with laser-induced fluorescence and micro-digital holographic particle tracking velocimetry: Numerical study

International audienc

Image segmentation in marine environments using convolutional LSTM for temporal context

Unmanned surface vehicles (USVs) carry a wealth of possible applications, many of which are limited by the vehicle's level of autonomy. The development of efficient and robust computer vision algorithms is a key factor in improving this, as they permit autonomous detection and thereby avoidance of obstacles. Recent developments in convolutional neural networks (CNNs), and the collection of increasingly diverse datasets, present opportunities for improved computer vision algorithms requiring less data and computational power. One area of potential improvement is the utilisation of temporal context from USV camera feeds in the form of sequential video frames to consistently identify obstacles in diverse marine environments under challenging conditions. This paper documents the implementation of this through long short-term memory (LSTM) cells in existing CNN structures and the exploration of parameters affecting their efficacy. It is found that LSTM cells are promising for achieving improved performance; however, there are weaknesses associated with network training procedures and datasets. Several novel network architectures are presented and compared using a state-of-the-art benchmarking method. It is shown that LSTM cells allow for better model performance with fewer training iterations, but that this advantage diminishes with additional training

Direct particle depth displacement measurement in DHPTV using spatial correlation of focus metric curves

International audienc

Characterization of atomization and breakup of acoustically levitated drops with digital holography

WOS:000347300800005International audienceA digital holographic particle tracking velocimetry system is applied to quantitatively study the drop atomization induced by capillary waves, and the breakup caused by increased sound pressure levels. A wavelet-based algorithm is used for particle detection and autofocusing with a wide size range of 20 mu m-2 mm. To eliminate the influence of large particles on small particles, a two-step detection method is adopted. Large drops are first characterized and simulated by a diffraction-based model. Then the contributions of the drops are subtracted from the original hologram followed by the detection of small droplets. Finally, the velocity and size distribution of the secondary droplets are obtained from the experimental holograms. The results demonstrate the validity of the digital in-line holographic technique for the atomization and breakup study of acoustically levitated drops. (C) 2014 Optical Society of Americ