Machine Learning Meets Advanced Robotic Manipulation

Automated industries lead to high quality production, lower manufacturing cost and better utilization of human resources. Robotic manipulator arms have major role in the automation process. However, for complex manipulation tasks, hard coding efficient and safe trajectories is challenging and time consuming. Machine learning methods have the potential to learn such controllers based on expert demonstrations. Despite promising advances, better approaches must be developed to improve safety, reliability, and efficiency of ML methods in both training and deployment phases. This survey aims to review cutting edge technologies and recent trends on ML methods applied to real-world manipulation tasks. After reviewing the related background on ML, the rest of the paper is devoted to ML applications in different domains such as industry, healthcare, agriculture, space, military, and search and rescue. The paper is closed with important research directions for future works

Kinematics modeling of six degrees of freedom humanoid robot arm using improved damped least squares for visual grasping

The robotic arm has functioned as an arm in the humanoid robot and is generally used to perform grasping tasks. Accordingly, kinematics modeling both forward and inverse kinematics is required to calculate the end-effector position in the cartesian space before performing grasping activities. This research presents the kinematics modeling of six degrees of freedom (6-DOF) robotic arm of the T-FLoW humanoid robot for the grasping mechanism of visual grasping systems on the robot operating system (ROS) platform and CoppeliaSim. Kinematic singularity is a common problem in the inverse kinematics model of robots, but. However, other problems are mechanical limitations and computational time. The work uses the homogeneous transformation matrix (HTM) based on the Euler system of the robot for the forward kinematics and demonstrates the capability of an improved damped least squares (I-DLS) method for the inverse kinematics. The I-DLS method was obtained by improving the original DLS method with the joint limits and clamping techniques. The I-DLS performs better than the original DLS during the experiments yet increases the calculation iteration by 10.95%, with a maximum error position between the end-effector and target positions in path planning of 0.1 cm

Fuzzy optimisation based symbolic grounding for service robots

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophySymbolic grounding is a bridge between task level planning and actual robot sensing and actuation. Uncertainties raised by unstructured environments make a bottleneck for integrating traditional artificial intelligence with service robotics. In this research, a fuzzy optimisation based symbolic grounding approach is presented. This approach can handle uncertainties and helps service robots to determine the most comfortable base region for grasping objects in a fetch and carry task. Novel techniques are applied to establish fuzzy objective function, to model fuzzy constraints and to perform fuzzy optimisation. The approach does not have the short comings of others’ work and the computation time is dramatically reduced in compare with other methods. The advantages of the proposed fuzzy optimisation based approach are evidenced by experiments that were undertaken in Care-O-bot 3 (COB 3) and Robot Operating System (ROS) platforms

Review of the techniques used in motor‐cognitive human‐robot skill transfer

Abstract A conventional robot programming method extensively limits the reusability of skills in the developmental aspect. Engineers programme a robot in a targeted manner for the realisation of predefined skills. The low reusability of general‐purpose robot skills is mainly reflected in inability in novel and complex scenarios. Skill transfer aims to transfer human skills to general‐purpose manipulators or mobile robots to replicate human‐like behaviours. Skill transfer methods that are commonly used at present, such as learning from demonstrated (LfD) or imitation learning, endow the robot with the expert's low‐level motor and high‐level decision‐making ability, so that skills can be reproduced and generalised according to perceived context. The improvement of robot cognition usually relates to an improvement in the autonomous high‐level decision‐making ability. Based on the idea of establishing a generic or specialised robot skill library, robots are expected to autonomously reason about the needs for using skills and plan compound movements according to sensory input. In recent years, in this area, many successful studies have demonstrated their effectiveness. Herein, a detailed review is provided on the transferring techniques of skills, applications, advancements, and limitations, especially in the LfD. Future research directions are also suggested

CROPS : high tech agricultural robots

In the EU-funded CROPS (Clever Robots for Crops) project high tech robots are developed for site-specific spraying and selective harvesting of fruit and fruit vegetables. The harvesting robots are being designed to harvest high-value crops such as greenhouse vegetables, fruits in orchards and grapes for premium wines. The CROPS robots are also developed for canopy spraying in orchards and for precision target spraying in grape vines to reduce the use of pesticides. A CROPS robot will be able to detect the fruit, sense its ripeness, then move to grasp and gently detach only the ripe fruit. For crop protection the canopy sprayer can detect contours of trees in an orchard and consequently only spraying on the trees and the precision target sprayer can detect diseases on leaves of vine grapes and only spray pesticides on the affected spots of the leaves. In the CROPS project also attention is paid to reliable detection and classification of objects and obstacles for autonomous navigation in a safe way in plantations and forests. For the several applications within the CROPS project platforms were developed. Sensing systems and appropriate vision algorithms for the platforms have been developed. For the software platform the Robot Operating System (ROS) is used. A 9 degrees of freedom (DOF) manipulator was designed and built and tested for sweet-pepper harvesting, apple harvesting and in close range spraying. The 9-DOF manipulator is modular, since the joint configuration can be adapted to the applications, e.g. 6 DOF for the close range spraying. For the different applications different end-effectors were designed and tested. The main results of the CROPS project will be the applications, the so-called demonstrators For sweet pepper a platform that can move in between the crop rows on the common greenhouse rail system which also serves as heating pipes was built and equipped with a sensing and lightning system, the manipulator and end-effectors. The complete system was tested and showed to growers in a lab situation. The apple harvesting platform is based on a current mechanical grape harvester. In discussion with growers so-called 'walls of fruit trees' have been designed which bring robots closer to the practice. This system, equipped with a sensing system the CROPS manipulator and a special end-effector, has been successfully tested in an orchard. A canopy-optimised sprayer has been designed as a trailed sprayer with a centrifugal blower. The system has been successfully tested in an orchard with a significant reduction of pesticide use. For close range target spraying the spraying robot in a greenhouse experiment with grape vines reduced the pesticide consumption with 84%

Robots learn to behave: improving human-robot collaboration in flexible manufacturing applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Safe navigation and human-robot interaction in assistant robotic applications

L'abstract è presente nell'allegato / the abstract is in the attachmen