ROS 2 Configuration for Delta Robot Arm Kinematic Motion and Stereo Camera Visualization

The Delta robot is one of the robot types that is used in agriculture and industrial application. However, before the complex physical development of the robot, a simulation needs to be developed to ensure the perfect functionality of the design. Therefore, this paper presented a development of simulation for a parallel delta robot using a Robot Operating System 2 (ROS 2) environment and stereo camera visualization.  The contribution of this research is to present the development details and the proposed solution to solve issues encountered during the development. The development of script in the format of eXtensible Markup Language (XML), Unified Robot Description Format (URDF), and Simulation Description Format (SDF) are presented for describing a robot's physical structure, allowing a robotic system to be depicted in a tree structure, and defining the delta robot arm, which is made up of closed-loop kinematic chain linkage that will be simulated in Gazebo. For the results, several Gazebo plugin libraries are compared and tested for the wheels motion control, stereo camera visualization, and delta robot arm kinematic motion. From the experiment, the best method is inverse kinematic motion the method is selected and used in the simulation. The selected method resulted in an average percentage error of 3.92%, 3.72%, and 2.92%, respectively for each joint

Miniaturized modular manipulator design for high precision assembly and manipulation tasks

In this paper, design and control issues for the development of miniaturized manipulators which are aimed to be used in high precision assembly and manipulation tasks are presented. The developed manipulators are size adapted devices, miniaturized versions of conventional robots based on well-known kinematic structures. 3 degrees of freedom (DOF) delta robot and a 2 DOF pantograph mechanism enhanced with a rotational axis at the tip and a Z axis actuating the whole mechanism are given as examples of study. These parallel mechanisms are designed and developed to be used in modular assembly systems for the realization of high precision assembly and manipulation tasks. In that sense, modularity is addressed as an important design consideration. The design procedures are given in details in order to provide solutions for miniaturization and experimental results are given to show the achieved performances

Lightweight design and encoderless control of a miniature direct drive linear delta robot

This paper presents the design, integration and experimental validation of a miniature light-weight delta robot targeted to be used for a variety of applications including the pick-place operations, high speed precise positioning and haptic implementations. The improvements brought by the new design contain; the use of a novel light-weight joint type replacing the conventional and heavy bearing structures and realization of encoderless position measurement algorithm based on hall effect sensor outputs of direct drive linear motors. The description of mechanical, electrical and software based improvements are followed by the derivation of a sliding mode controller to handle tracking of planar closed curves represented by elliptic fourier descriptors (EFDs). The new robot is tested in experiments and the validity of the improvements are verified for practical implementation

Dynamics of the Orthoglide parallel robot

Recursive matrix relations for kinematics and dynamics of the Orthoglide parallel robot having three concurrent prismatic actuators are established in this paper. These are arranged according to the Cartesian coordinate system with fixed orientation, which means that the actuating directions are normal to each other. Three identical legs connecting to the moving platform are located on three planes being perpendicular to each other too. Knowing the position and the translation motion of the platform, we develop the inverse kinematics problem and determine the position, velocity and acceleration of each element of the robot. Further, the principle of virtual work is used in the inverse dynamic problem. Some matrix equations offer iterative expressions and graphs for the input forces and the powers of the three actuators

High precision motion control of parallel robots with imperfections and manufacturing tolerances

This work attempts to achieve precise motion control using parallel robots with manufacturing tolerances and inaccuracies by migrating the measurements from their joint space to task space in order to decrease control system’s sensitivity to any kinematical uncertainty rather than calibrating the parallel plant. The problem of dynamical model uncertainties and its effect on the derivation of the control law is also addressed in this work through disturbance estimation and compensation. Eventually, both task space measurement and disturbance estimation are combined to formulate a control framework that is unsensitive to either kinematical and dynamical system uncertainties

Methods for autonomous wristband placement with a search-and-rescue aerial manipulator

A new robotic system for Search And Rescue (SAR) operations based on the automatic wristband placement on the victims’ arm, which may provide identification, beaconing and remote sensor readings for continuous health monitoring. This paper focuses on the development of the automatic target localization and the device placement using an unmanned aerial manipulator. The automatic wrist detection and localization system uses an RGB-D camera and a convolutional neural network based on the region faster method (Faster R-CNN). A lightweight parallel delta manipulator with a large workspace has been built, and a new design of a wristband in the form of a passive detachable gripper, is presented, which under contact, automatically attaches to the human, while disengages from the manipulator. A new trajectory planning method has been used to minimize the torques caused by the external forces during contact, which cause attitude perturbations. Experiments have been done to evaluate the machine learning method for detection and location, and for the assessment of the performance of the trajectory planning method. The results show how the VGG-16 neural network provides a detection accuracy of 67.99%. Moreover, simulation experiments have been done to show that the new trajectories minimize the perturbations to the aerial platform.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Modeling and simulation of a Stewart platform type parallel structure robot

The kinematics and dynamics of a Stewart Platform type parallel structure robot (NASA's Dynamic Docking Test System) were modeled using the method of kinematic influence coefficients (KIC) and isomorphic transformations of system dependence from one set of generalized coordinates to another. By specifying the end-effector (platform) time trajectory, the required generalized input forces which would theoretically yield the desired motion were determined. It was found that the relationship between the platform motion and the actuators motion was nonlinear. In addition, the contribution to the total generalized forces, required at the actuators, from the acceleration related terms were found to be more significant than the velocity related terms. Hence, the curve representing the total required actuator force generally resembled the curve for the acceleration related force. Another observation revealed that the acceleration related effective inertia matrix I sub dd had the tendency to decouple, with the elements on the main diagonal of I sub dd being larger than the off-diagonal elements, while the velocity related inertia power array P sub ddd did not show such tendency. This tendency results in the acceleration related force curve of a given actuator resembling the acceleration profile of that particular actuator. Furthermore, it was indicated that the effective inertia matrix for the legs is more decoupled than that for the platform. These observations provide essential information for further research to develop an effective control strategy for real-time control of the Dynamic Docking Test System

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor

Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building efficient neural network based architectures for control of fast and agile robots. In this paper, we present a spiking neural network architecture that uses sensory feedback to control rotational velocity of a robotic vehicle. When the velocity reaches the target value, the mapping from the target velocity of the vehicle to the correct motor command, both represented in the spiking neural network on the neuromorphic device, is autonomously stored on the device using on-chip plastic synaptic weights. We validate the controller using a wheel motor of a miniature mobile vehicle and inertia measurement unit as the sensory feedback and demonstrate online learning of a simple 'inverse model' in a two-layer spiking neural network on the neuromorphic chip. The prototype neuromorphic device that features 256 spiking neurons allows us to realise a simple proof of concept architecture for the purely neuromorphic motor control and learning. The architecture can be easily scaled-up if a larger neuromorphic device is available.Comment: 6+1 pages, 4 figures, will appear in one of the Robotics conference