Automatic motion of manipulator using sampling based motion planning algorithms - application in service robotics

The thesis presents new approaches for autonomous motion execution of a robotic arm. The calculation of the motion is called motion planning and requires the computation of robot arm's path. The text covers the calculation of the path and several algorithms have been therefore implemented and tested in several real scenarios. The work focuses on sampling based planners, which means that the path is created by connecting explicitly random generated points in the free space. The algorithms can be divided into three categories: those that are working in configuration space(C-Space)(C- Space is the set of all possible joint angles of a robotic arm) , the mixed approaches using both Cartesian and C-Space and those that are using only the Cartesian space. Although Cartesian space seems more appropriate, due to dimensionality, this work illustrates that the C-Space planners can achieve comparable or better results. Initially an enhanced approach for efficient collision detection in C-Space, used by the planners, is presented. Afterwards the N dimensional cuboid region, notated as Rq, is defined. The Rq configures the C-Space so that the sampling is done close to a selected, called center, cell. The approach is enhanced by the decomposition of the Cartesian space into cells. A cell is selected appropriately if: (a) is closer to the target position and (b) lies inside the constraints. Inverse kinematics(IK) are applied to calculate a centre configuration used later by the Rq. The CellBiRRT is proposed and combines all the features. Continuously mixed approaches that do not require goal configuration or an analytic solution of IK are presented. Rq regions as well as Cells are also integrated in these approaches. A Cartesian sampling based planner using quaternions for linear interpolation is also proposed and tested. The common feature of the so far algorithms is the feasibility which is normally against the optimality. Therefore an additional part of this work deals with the optimality of the path. An enhanced approach of CellBiRRT, called CellBiRRT*, is developed and promises to compute shorter paths in a reasonable time. An on-line method using both CellBiRRT and CellBiRRT* is proposed where the path of the robot arm is improved and recalculated even if sudden changes in the environment are detected. Benchmarking with the state of the art algorithms show the good performance of the proposed approaches. The good performance makes the algorithms suitable for real time applications. In this work several applications are described: Manipulative skills, an approach for an semi-autonomous control of the robot arm and a motion planning library. The motion planning library provides the necessary interface for easy use and further development of the motion planning algorithms. It can be used as the part connecting the manipulative skill designing and the motion of a robotic arm

Saliency-based approaches for multidimensional explainability of deep networks

In deep learning, visualization techniques extract the salient patterns exploited by deep networks to perform a task (e.g. image classification) focusing on single images. These methods allow a better understanding of these complex models, empowering the identification of the most informative parts of the input data. Beyond the deep network understanding, visual saliency is useful for many quantitative reasons and applications, both in the 2D and 3D domains, such as the analysis of the generalization capabilities of a classifier and autonomous navigation. In this thesis, we describe an approach to cope with the interpretability problem of a convolutional neural network and propose our ideas on how to exploit the visualization for applications like image classification and active object recognition. After a brief overview on common visualization methods producing attention/saliency maps, we will address two separate points: firstly, we will describe how visual saliency can be effectively used in the 2D domain (e.g. RGB images) to boost image classification performances: as a matter of fact, visual summaries, i.e. a compact representation of an ensemble of saliency maps, can be used to improve the classification accuracy of a network through summary-driven specializations. Then, we will present a 3D active recognition system that allows to consider different views of a target object, overcoming the single-view hypothesis of classical object recognition, making the classification problem much easier in principle. Here we adopt such attention maps in a quantitative fashion, by building a 3D dense saliency volume which fuses together saliency maps obtained from different viewpoints, obtaining a continuous proxy on which parts of an object are more discriminative for a given classifier. Finally, we will show how to inject this representations in a real world application, so that an agent (e.g. robot) can move knowing the capabilities of its classifier