Physical Interaction of Autonomous Robots in Complex Environments

Recent breakthroughs in the fields of computer vision and robotics are firmly changing the people perception about robots. The idea of robots that substitute humansisnowturningintorobotsthatcollaboratewiththem. Serviceroboticsconsidersrobotsaspersonalassistants. Itsafelyplacesrobotsindomesticenvironments in order to facilitate humans daily life. Industrial robotics is now reconsidering its basic idea of robot as a worker. Currently, the primary method to guarantee the personnels safety in industrial environments is the installation of physical barriers around the working area of robots. The development of new technologies and new algorithms in the sensor field and in the robotic one has led to a new generation of lightweight and collaborative robots. Therefore, industrial robotics leveraged the intrinsic properties of this kind of robots to generate a robot co-worker that is able to safely coexist, collaborate and interact inside its workspace with both personnels and objects. This Ph.D. dissertation focuses on the generation of a pipeline for fast object pose estimation and distance computation of moving objects,in both structured and unstructured environments,using RGB-D images. This pipeline outputs the command actions which let the robot complete its main task and fulfil the safety human-robot coexistence behaviour at once. The proposed pipeline is divided into an object segmentation part,a 6D.o.F. object pose estimation part and a real-time collision avoidance part for safe human-robot coexistence. Firstly, the segmentation module finds candidate object clusters out of RGB-D images of clutter scenes using a graph-based image segmentation technique. This segmentation technique generates a cluster of pixels for each object found in the image. The candidate object clusters are then fed as input to the 6 D.o.F. object pose estimation module. The latter is in charge of estimating both the translation and the orientation in 3D space of each candidate object clusters. The object pose is then employed by the robotic arm to compute a suitable grasping policy. The last module generates a force vector field of the environment surrounding the robot, the objects and the humans. This force vector field drives the robot toward its goal while any potential collision against objects and/or humans is safely avoided. This work has been carried out at Politecnico di Torino, in collaboration with Telecom Italia S.p.A

Parallelization Strategies for Markerless Human Motion Capture

Markerless Motion Capture (MMOCAP) is the problem of determining the pose of a person from images captured by one or several cameras simultaneously without using markers on the subject. Evaluation of the solutions is frequently the most time-consuming task, making most of the proposed methods inapplicable in real-time scenarios. This paper presents an efficient approach to parallelize the evaluation of the solutions in CPUs and GPUs. Our proposal is experimentally compared on six sequences of the HumanEva-I dataset using the CMAES algorithm. Multiple algorithm’s configurations were tested to analyze the best trade-off in regard to the accuracy and computing time. The proposed methods obtain speedups of 8× in multi-core CPUs, 30× in a single GPU and up to 110× using 4 GPU

Detection And Classification Of Buried Radioactive Materials

This dissertation develops new approaches for detection and classification of buried radioactive materials. Different spectral transformation methods are proposed to effectively suppress noise and to better distinguish signal features in the transformed space. The contributions of this dissertation are detailed as follows. 1) Propose an unsupervised method for buried radioactive material detection. In the experiments, the original Reed-Xiaoli (RX) algorithm performs similarly as the gross count (GC) method; however, the constrained energy minimization (CEM) method performs better if using feature vectors selected from the RX output. Thus, an unsupervised method is developed by combining the RX and CEM methods, which can efficiently suppress the background noise when applied to the dimensionality-reduced data from principle component analysis (PCA). 2) Propose an approach for buried target detection and classification, which applies spectral transformation followed by noisejusted PCA (NAPCA). To meet the requirement of practical survey mapping, we focus on the circumstance when sensor dwell time is very short. The results show that spectral transformation can alleviate the effects from spectral noisy variation and background clutters, while NAPCA, a better choice than PCA, can extract key features for the following detection and classification. 3) Propose a particle swarm optimization (PSO)-based system to automatically determine the optimal partition for spectral transformation. Two PSOs are incorporated in the system with the outer one being responsible for selecting the optimal number of bins and the inner one for optimal bin-widths. The experimental results demonstrate that using variable bin-widths is better than a fixed bin-width, and PSO can provide better results than the traditional Powell’s method. 4) Develop parallel implementation schemes for the PSO-based spectral partition algorithm. Both cluster and graphics processing units (GPU) implementation are designed. The computational burden of serial version has been greatly reduced. The experimental results also show that GPU algorithm has similar speedup as cluster-based algorithm

Diversity and frame invariance characteristics in Particle Swarm Optimization with and without digital pheromones

Academic problems for testing optimization methods are widely criticized for not being a good representative of real world problems. Due to the unavailability of publishable proprietary information from industries they collaborate with, researchers tend to simulate complex problems by using ‘n-dimensional’ multimodal and/or multi-objective academic test problems for evaluating optimization methods developed. Most of these benchmarking test problems can be decomposed and solved as ‘n’ 1-dimensional optimization problems, rendering them as ineffective representation of real-world problems. However, studies show that coordinate rotation of test problems through an arbitrary angle makes design variables dependent on each other and cannot easily be decomposed into simpler problem chunks. Test problems formulated with coordinate rotation therefore will represent a realistic test bed for evaluating the performance of an optimization routine. However with coordinate rotation, the complexity of the problems potentially increases from O(nn) to O(exp(n ln n)) imposing performance loss on the optimization method that solves the problem. In this paper, the authors attempted to investigate whether coordinate rotation affects the performance characteristics of the digital pheromone implementation of Particle Swarm Optimization (PSO). In particular, two characteristics - swarm diversity with different random number schemes for the velocity vector, and frame invariance with rotational problems are studied and reported. In other words, the authors intended to evaluate whether PSO with digital pheromones is truly capable of solving complex problems

Design and Implementation of Particle Systems for Meshfree Methods with High Performance

Particle systems, commonly associated with computer graphics, animation, and video games, are an essential component in the implementation of numerical methods ranging from the meshfree methods for computational fluid dynamics and related applications (e.g., smoothed particle hydrodynamics, SPH) to minimization methods for arbitrary problems (e.g., particle swarm optimization, PSO). These methods are frequently embarrassingly parallel in nature, making them a natural fit for implementation on massively parallel computational hardware such as modern graphics processing units (GPUs). However, naive implementations fail to fully exploit the capabilities of this hardware. We present practical solutions to the challenges faced in the efficient parallel implementation of these particle systems, with a focus on performance, robustness, and flexibility. The techniques are illustrated through GPUSPH, the first implementation of SPH to run completely on GPU, and currently supporting multi-GPU clusters, uniform precision independent of domain size, and multiple SPH formulations

Development of a calibration pipeline for a monocular-view structured illumination 3D sensor utilizing an array projector

Commercial off-the-shelf digital projection systems are commonly used in active structured illumination photogrammetry of macro-scale surfaces due to their relatively low cost, accessibility, and ease of use. They can be described as inverse pinhole modelled. The calibration pipeline of a 3D sensor utilizing pinhole devices in a projector-camera setup configuration is already well-established. Recently, there have been advances in creating projection systems offering projection speeds greater than that available from conventional off-the-shelf digital projectors. However, they cannot be calibrated using well established techniques based on the pinole assumption. They are chip-less and without projection lens. This work is based on the utilization of unconventional projection systems known as array projectors which contain not one but multiple projection channels that project a temporal sequence of illumination patterns. None of the channels implement a digital projection chip or a projection lens. To workaround the calibration problem, previous realizations of a 3D sensor based on an array projector required a stereo-camera setup. Triangulation took place between the two pinhole modelled cameras instead. However, a monocular setup is desired as a single camera configuration results in decreased cost, weight, and form-factor. This study presents a novel calibration pipeline that realizes a single camera setup. A generalized intrinsic calibration process without model assumptions was developed that directly samples the illumination frustum of each array projection channel. An extrinsic calibration process was then created that determines the pose of the single camera through a downhill simplex optimization initialized by particle swarm. Lastly, a method to store the intrinsic calibration with the aid of an easily realizable calibration jig was developed for re-use in arbitrary measurement camera positions so that intrinsic calibration does not have to be repeated

Desynchronization of simulation and optimization algorithms in HPC Environment

Need for scalability of an algorithm is essential, when one wants to utilize HPC infrastructure in an efficient and reasonable way. In such infrastructures, synchronization affects the efficiency of the parallel algorithms. However, one can consider introducing certain means of desynchronization in order to increase scalability. Allowing for omitting or delaying certain messages, can be easily accepted in the case of metaheuristics. Furthermore, some simulations can also follow this pattern and handle bigger environments. The paper presents a short survey of desynchronization idea, pointing out already obtained results or sketching out the future work focused on scaling the parallel and distributed computing or simulation algorithms leveraging desynchronization

Generalized Feedback Loop for Joint Hand-Object Pose Estimation

We propose an approach to estimating the 3D pose of a hand, possibly handling an object, given a depth image. We show that we can correct the mistakes made by a Convolutional Neural Network trained to predict an estimate of the 3D pose by using a feedback loop. The components of this feedback loop are also Deep Networks, optimized using training data. This approach can be generalized to a hand interacting with an object. Therefore, we jointly estimate the 3D pose of the hand and the 3D pose of the object. Our approach performs en-par with state-of-the-art methods for 3D hand pose estimation, and outperforms state-of-the-art methods for joint hand-object pose estimation when using depth images only. Also, our approach is efficient as our implementation runs in real-time on a single GPU.Comment: arXiv admin note: substantial text overlap with arXiv:1609.0969