Enhancement of Industrial Logistic Systems with Semantic 3D Representations for Mobile Manipulators

et al. 2020

Sensors

Self Cite

Carrying out the task of the exploration of a scene by an autonomous robot entails a set of complex skills, such as the ability to create and update a representation of the scene, the knowledge of the regions of the scene which are yet unexplored, the ability to estimate the most efficient point of view from the perspective of an explorer agent and, finally, the ability to physically move the system to the selected Next Best View (NBV). This paper proposes an autonomous exploration system that makes use of a dual OcTree representation to encode the regions in the scene which are occupied, free, and unknown. The NBV is estimated through a discrete approach that samples and evaluates a set of view hypotheses that are created by a conditioned random process which ensures that the views have some chance of adding novel information to the scene. The algorithm uses ray-casting defined according to the characteristics of the RGB-D sensor, and a mechanism that sorts the voxels to be tested in a way that considerably speeds up the assessment. The sampled view that is estimated to provide the largest amount of novel information is selected, and the system moves to that location, where a new exploration step begins. The exploration session is terminated when there are no more unknown regions in the scene or when those that exist cannot be observed by the system. The experimental setup consisted of a robotic manipulator with an RGB-D sensor assembled on its end-effector, all managed by a Robot Operating System (ROS) based architecture. The manipulator provides movement, while the sensor collects information about the scene. Experimental results span over three test scenarios designed to evaluate the performance of the proposed system. In particular, the exploration performance of the proposed system is compared against that of human subjects. Results show that the proposed approach is able to carry out the exploration of a scene, even when it starts from scratch, building up knowledge as the exploration progresses. Furthermore, in these experiments, the system was able to complete the exploration of the scene in less time when compared to human subjects.

Section: Test Scenario 3: Industrial Applicationmentioning

confidence: 99%

Autonomous Scene Exploration for Robotics: A Conditional Random View-Sampling and Evaluation Using a Voxel-Sorting Mechanism for Efficient Ray Casting

Santos

Oliveira

et al. 2020

Sensors

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“…In the prospective Factory of the Future, data from the physical factory shop floor will be captured and stored in a synchronized and centralized virtual representation. 9,10 As a direct consequence of the information analytics conducted on the captured data, networked industrial systems will be capable of more efficient, collaborative and resilient performance. 11 In addition, the interaction with the physical reality will not be restricted to sensing capabilities, but will, in fact, also incorporate active actuation by embedded software devices in the production process.…”

Section: Related Workmentioning

confidence: 99%

ROBIN: An open-source middleware for plug‘n’produce of Cyber-Physical Systems

International Journal of Advanced Robotic Systems

Ribeiro

Domingos

et al. 2020

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Motivated by the Fourth Industrial Revolution, there is an ever-increasing need to integrated Cyber-Physical Systems in industrial production environments. To address the demand for flexible robotics in contemporary industrial environments and the necessity to integrate robots and automation equipment in an efficient manner, an effective, bidirectional, reliable and structured data interchange mechanism is required. As an answer to these requirements, this article presents ROBIN, an open-source middleware for achieving interoperability between the Robot Operating System and CODESYS, a softPLC that can run on embedded devices and that supports a variety of fieldbuses and industrial network protocols. The referred middleware was successfully applied and tested in various industrial applications such as battery management systems, motion, robotic manipulator and safety hardware control, and horizontal integration between a mobile manipulator and a conveyor system.

“…1, this robotic system has a distributed architecture. In the server side implementation, there are two components, the Production Manager (PM) and the Advanced Plant Model (APM) [8], while on the robot side of the architecture, there are the skills and the Task Manager (TM). The APM keeps a near realtime model of the production environment.…”

Section: Case Study Descriptionmentioning

confidence: 99%

Applying Software Static Analysis to ROS: The Case Study of the FASTEN European Project

Neto

Advances in Intelligent Systems and Computing

Sousa

et al. 2019

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Modern industry is shifting towards flexible, advanced robotic systems in order to meet the increasing demand for custom-made products with low manufacturing costs, and to promote a collaborative environment for humans and robots. As a consequence of this industrial revolution, some traditional, mechanical-and hardware-based safety mechanisms are discarded in favor of a safer, more dependable robot software. This work presents a case study of assessing and improving the internal quality of a European research mobile manipulator, operating in a real industrial environment, using modern static analysis tools geared for robotic software. Following an iterative approach, we managed to fix about 90% of the reported issues, resulting in code that is easier to use and maintain.