Detection,localisation and tracking of pallets using machine learning techniques and 2D range data

The problem of autonomous transportation in industrial scenarios is receiving a renewed interest due to the way it can revolutionise internal logistics, especially in unstructured environments. This paper presents a novel architecture allowing a robot to detect, localise, and track (possibly multiple) pallets using machine learning techniques based on an on-board 2D laser rangefinder only. The architecture is composed of two main components: the first stage is a pallet detector employing a Faster Region-Based Convolutional Neural Network (Faster R-CNN) detector cascaded with a CNN-based classifier; the second stage is a Kalman filter for localising and tracking detected pallets, which we also use to defer commitment to a pallet detected in the first stage until sufficient confidence has been acquired via a sequential data acquisition process. For fine-tuning the CNNs, the architecture has been systematically evaluated using a real-world dataset containing 340 labelled 2D scans, which have been made freely available in an online repository. Detection performance has been assessed on the basis of the average accuracy over k-fold cross-validation, and it scored 99.58% in our tests. Concerning pallet localisation and tracking, experiments have been performed in a scenario where the robot is approaching the pallet to fork. Although data have been originally acquired by considering only one pallet as per specification of the use case we consider, artificial data have been generated as well to mimic the presence of multiple pallets in the robot workspace. Our experimental results confirm that the system is capable of identifying, localising and tracking pallets with a high success rate while being robust to false positives.

     

Mobility analysis of the typical gait of a radial symmetrical six-legged robot

Radial symmetrical hexapod robots have attracted the attention of the research community because of their flexibility. There is nonetheless still much to study on their kinematics, dynamics and locomotion. In this paper, initially, full body kinematics of a radial symmetrical six-legged robot with statically stable movements are reviewed. The kinematics analysis is made on cooperated swing legs over supporting legs. Using the robot screw theory and exponential product equations, the velocities and accelerations referring to the object reference frame of each robot part are presented in a compact form. This makes it easy to calculate kinetic energy and so to build the dynamics model using the Lagrangian method. Many ways of walking of six-legged robots have been introduced in specialized literature. However, mobility comparison is still open to research. Two main aspects of mobility are analyzed in detail in this paper. The first one concerns the mobility of three statically stable ways of walking (the insect-wave gait, mammal-kick gait and mixed gait) with the same duty factor on the same radial symmetrical hexapod robot. The stability, energy efficiency, turning flexibility, and terrain or environment adaptability among those gaits have been compared. The mixed gait presents important advantages over the other two, while those two are useful for some special terrain conditions where the mixed gait is limited. The second aspect that has been analyzed focuses on the mobility of the body. The body height, measured from the body bottom to the supporting surface, and the stride optimization factors are proposed according to the obstacles’ configuration and the energy optimization. The results of our study can be used for the intelligent locomotion control of some articulated multi-legged robots for walking statically-stably on a complicated surface.Most of our analyses have been successfully verified on the prototype which has been designed by Politecnico di Milano (POLIMI) and Beijing University of Astronautics and Aeronautics (BUAA) and developed by POLIMI in 2007.     

Circular backpropagation networks embed vector quantization.

This letter proves the equivalence between vector quantization (VQ) classifiers and circular backpropagation (CBP) networks. The calibrated prototypes for a VQ schema can be plugged in a CBP feedforward structure having the same number of hidden neurons and featuring the same mapping. The letter describes how to exploit such equivalence by using VQ prototypes to perform a meaningful initialization for BP optimization. The approach effectiveness was tested considering a real classification problem (NIST handwritten digits).     

Adaptive internal representation in circular back-propagation networks

Surface- and prototype-based models are often regarded as alternative paradigms to represent internal knowledge in trained neural networks. This paper analyses a network model (Circular Back-Propagation) that overcomes such dualism by choosing the best-fitting representation adaptively. The model involves a straightforward modification to classical feed-forward structures to let neurons implement hyperspherical boundaries; as a result, it exhibits a notable representation power, and benefits from the simplicity and effectiveness of classical back-propagation training. Artificial testbeds support the model definition by demonstrating its basic properties; an application to a real, complex problem in the clinical field shows the practical advantages of the approach.     

An efficient technique for implementing an image-compression neural algorithm on concurrent multiprocessor architectures

The paper describes a parallel implementation of a neural algorithm performing vector quantization for very low bit-rate video compression on toroidal-mesh multiprocessor systems. The neural model considered is a plastic version of the Neural Gas algorithm, whose features are suitable for implementations on toroidal mesh topologies. The architecture adopted, and the data-allocation strategy, enhance the method's scaling properties and remarkable efficiency. The parallel approach is supported by a theoretical analysis of the efficiency of the overall structure. Experimental results on a significant testbed and the fit between predicted and measured values confirm the validity of the parallel approach.     

Worst case analysis of weight inaccuracy effects in multilayerperceptrons

We derive a method for the analysis of weight quantization effects in multilayer perceptrons based on the application of interval arithmetic. Differently from previous results, we find worst case bounds on the errors due to weight quantization, that are valid for every distribution of the input or weight values. Given a trained network, our method allows us to easily compute the minimum number of bits needed to encode its weights.     

月球探测(车)机器人技术的发展与展望

简述当前国内外探月研究进展及月球探测(车)机器人研究发展情况.基于月球的复杂环境特征,对比轮式运动与步行式运动月球探测(车)机器人的优势与缺陷,重点介绍了一种新型六轮腿式月球探测机器人方案.该方案具有高机动性,越障能力强,容错性好等特点,对该方案主要技术性能、本体结构、运动系统、轮腿切换机构工作原理等关键技术进行了探讨.     

Anomalous sound event detection: A survey of machine learning based methods and applications

With the development of multi-modal man-machine interaction, audio signal analysis is gaining importance in a field traditionally dominated by video. In particular, anomalous sound event detection offers novel options to improve audio-based man-machine interaction, in many useful applications such as surveillance systems, industrial fault detection and especially safety monitoring, either indoor or outdoor. Event detection from audio can fruitfully integrate visual information and can outperform it in some respects, thus representing a complementary perceptual modality. However, it also presents specific issues and challenges. In this paper, a comprehensive survey of anomalous sound event detection is presented, covering various aspects of the topic, ?.e.feature extraction methods, datasets, evaluation metrics, methods, applications, and some open challenges and improvement ideas that have been recently raised in the literature.

     

虚拟环境下机器人远程控制

结合虚拟环境技术与远程机器人控制技术，设计开发了一个基于虚拟环境的机器人远程控制系统，实现了通过识别语音控制命令，控制虚拟机器人动作，带动远程机器人完成语音内容相应的动作，并进行机器人动作的固体反馈。通过对真实机器人控制的系统运行，表明该控制系统的有效性。     

Circular backpropagation networks for classification

The class of mapping networks is a general family of tools to perform a wide variety of tasks. This paper presents a standardized, uniform representation for this class of networks, and introduces a simple modification of the multilayer perceptron with interesting practical properties, especially well suited to cope with pattern classification tasks. The proposed model unifies the two main representation paradigms found in the class of mapping networks for classification, namely, the surface-based and the prototype-based schemes, while retaining the advantage of being trainable by backpropagation. The enhancement in the representation properties and the generalization performance are assessed through results about the worst-case requirement in terms of hidden units and about the Vapnik-Chervonenkis dimension and cover capacity. The theoretical properties of the network also suggest that the proposed modification to the multilayer perceptron is in many senses optimal. A number of experimental verifications also confirm theoretical results about the model's increased performances, as compared with the multilayer perceptron and the Gaussian radial basis functions network.