Object Tracking with a Multiagent Robot System and a Stereo Vision Camera

AbstractWhen working with a robot in terms of object manipulation the essential information is relative position between robot's tool center point (TCP) and the object of interest. This paper proposes a method of frame relative displacement and describes a working multiagent robot application that can be used for tracking, tooling or handling operations with the use of stereo vision in unstructured laboratory environment. Robot system is composed of two Fanuc robot arms, one of which carries a stereo vision camera system and the other which is guided in relation to object of interest. The latter robot has a marker that is used for navigation between the robot and the object of interest. Image processing, marker detection, 3-D coordinates extraction, coordinate system transformations, offset coordinates calculation and communication are handled using c++ multithread program and TCP/IP protocol

Tuning of Parameters for Robotic Contouring Based on the Evaluation of Force Deviation

The application of industrial robots with advanced sensor systems in unstructured environments is continuously becoming wider. A widely used type of advanced sensor systems is the force-torque sensor. Force-torque sensors are typically used for applications such as robot grinding, sanding, polishing, and deburring, where a constant force is exerted upon a workpiece. In this research, control parameters for exerting a constant force along a predefined path are evaluated in laboratory conditions. The experimental setup with the contouring force feedback is composed of a Fanuc LRMate six-degree-of-freedom industrial robot with an integrated force-torque sensor. Control parameters of the Contouring function within the Fanuc robot controller are tuned in four contouring experiments. The experiments conducted in this research are: i) flat beam, ii) flat beam with a rigid support, iii) wave shaped compliant plate, and iv) compliant flat plate. During the experiments, contouring parameters were altered in order to collect the feedback on the values of the force to be used for the evaluation of the force deviation. A fitness function for the evaluation of the force deviation and the tuning of the control parameters is presented. The fitness function enables a selection of initial control parameters which minimize the force deviation during the robot contouring process

Solving Pallet loading Problem with Real-World Constraints

Efficient cargo packing and transport unit stacking play a vital role in enhancing logistics efficiency and reducing costs in the field of logistics. This article focuses on the challenging problem of loading transport units onto pallets, which belongs to the class of NP-hard problems. We propose a novel method for solving the pallet loading problem using a branch and bound algorithm, where there is a loading order of transport units. The derived algorithm considers only a heuristically favourable subset of possible positions of the transport units, which has a positive effect on computability. Furthermore, it is ensured that the pallet configuration meets real-world constraints, such as the stability of the position of transport units under the influence of transport inertial forces and gravity.Comment: 8 pages, 1 figure, project report pape

VIRTUAL SURFACE FOR HUMAN-ROBOT INTERACTION

As cooperation between robots and humans becomes increasingly important for new robotic applications, human-robot interaction (HRI) becomes a significant area of research. This paper presents a novel approach to HRI based on the use of a virtual surface. The presented system consists of a virtual surface and a robot manipulator capable of tactile interaction. Multimedia content of the virtual surface and the option to manually guide the manipulator through space provide an intuitive means of interaction between the robot and the operator. The paper proposes shared workspaces for humans and robots to simplify and improve human-robot collaboration when performing various tasks utilizing a developed interaction model

AUTONOMOUS ROBOT LEARNING MODEL BASED ON VISUAL INTERPRETATION OF SPATIAL STRUCTURES

The main concept of the presented research is an autonomous robot learning model for which a novel ARTgrid neural network architecture for the classification of spatial structures is used. The motivation scenario includes incremental unsupervised learning which is mainly based on discrete spatial structure changes recognized by the robot vision system. The learning policy problem is presented as a classification problem for which the adaptive resonance theory (ART) concept is implemented. The methodology and architecture of the autonomous robot learning model with preliminary results are presented. A computer simulation was performed with four input sets containing 22, 45, 73, and 111 random spatial structures. The ARTgrid shows a fairly high (>85%) match score when applied with already learned patterns after the first learning cycle, and a score of >95% after the second cycle. Regarding the category proliferation, the results are compared with a more predictive modified cluster centre seeking algorithm

Calibration of an industrial robot using a stereo vision system

AbstractIndustrial robots have very good repeatability but still lack good absolute accuracy. The main reason is difference between the ideal robot kinematic model integrated in the robot controller and actual robot parameters. A method for identifying certain parameters of the robot model has been proposed. A noncontact method using a stereovision system attached to the robot arm is utilized for providing measurements of calibration points in space. Points are represented as spheres which localized by the stereo vision system project a circle in two image capture planes independent of the viewing angle. Spatial coordinates of each sphere center are acquired in different robot configurations. From these readings errors of robot absolute positioning are measured. The standard Denavit-Hartenberg (DH) notation is used when the modified model parameters containing joint encoder offset values are directly input to the robot controller. Calibration experiments carried out on a KUKA KR 6 R900 industrial robot show improved accuracy results. The maximum positioning error around calibration points was decreased from 3.63mm prior to calibration, to 1.29mm after the calibration procedure

Robot assisted 3D point cloud object registration

AbstractIn this paper we describe a method for registration of 3D point clouds that represent objects of interest. A stereovision system is used to capture point clouds of a static environment, robot arm and an unknown object. By moving the robot arm in the environment the proposed system defines known occupied zones and is able to identify the robot arm. In order to identify a complete point cloud presentation of the robot gripper it is rotated in front of a stereovision camera and its geometry is captured from different angles. Iterative closest point algorithm is used to determine a rigid transformation between every new robot pose so the original point cloud can be appended with the transformed one. When the robot is holding a new object the registration procedure is repeated and known elements (environment, robot arm and gripper) are removed so that the object can be identified

Influence of the localization strategy on the accuracy of a neurosurgical robot system

Precise navigation of surgical instruments is one of the most important features of autonomous surgical robots. In this paper, we introduce a concept of robot localization strategy and analyse its influence on the overall application error of a robot system for frameless stereotactic neurosurgery named RONNA. Localization strategies utilize specific angles at which the robot can approach a target point, orientations, and types of movement during the procedure of physical space fiducial marker localization and positioning to the target points. The localization strategies developed in this study are a neutral orientation strategy (NOS), an orientation correction strategy (OCS) and a joint displacement minimization strategy (JDMS). To evaluate the robot positioning performance with the localization strategies applied, we performed laboratory phantom measurements using a different number of fiducial markers in the registration procedure. When three, four, and five fiducial markers were used, the application error for the NOS was 1.571±0.256 mm, 1.397±0.283 mm, and 1.327±0.274 mm, and for the OCS, it was 0.429±0.133 mm, 0.284±0.068mm, and 0.260±0.076 mm, respectively. The application error for the JDMS was 0.493±0.176 mm with four and 0.369±0.160 mm with five fiducial markers used

Position planning for collaborating robots and its application in neurosurgery

Primjena robotskih manipulatora u medicini danas je vrlo aktualno područje istraživanja. Unatoč tome još uvijek postoji velik broj problema koji se javljaju kod pripreme većine robotiziranih operacijskih postupaka. Jedan od glavnih je pozicioniranje robota u odnosu na pacijenta. Kod postavljanja robota u odnosu na unaprijed poznate radne točke potrebno je osigurati efikasnu poziciju robota iz koje se sve zadane kretnje mogu izvršiti bez kinematskih problema i kolizija. U radu je predstavljena metoda za planiranje prostornog razmještaja robota prikladna za primjenu u neurokirurgiji. Razvijena metoda počiva na višeciljnoj optimizaciji funkcije cilja koja je sastavljena od kriterija koji objedinjuju prostornu upravljivost robota sa izbjegavanjem kolizija. Primjena razvijene metode validirana je na dvoručnom sustavu robota.Applications of robot manipulators in surgery are nowadays a very actual field of research. Still, there are a number of technical problems when setting and preparing robotical systems for various operation procedures. One of them is the robot-patient placement. When placing robots in respect to known target working positions it is crucial to assure feasible positioning where all required motions can be executed with no kinematic or collision problems. A planning method for robot placement suitable for neurosurgical operations is presented in this paper. The planning method is based on a multi-objective cost function which is composed of criteria that balance dexterity properties with a novel collision avoiding parameter. Use of the planning approach is implemented and validated on a dual arm robot setup