Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userChallenges in the Safety-Security Co-Assurance of Collaborative Industrial Robots
453
0
0.0
(
0
)
Added by
Mario Gleirscher
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
The coordinated assurance of interrelated critical properties, such as system safety and cyber-security, is one of the toughest challenges in critical systems engineering. In this chapter, we summarise approaches to the coordinated assurance of safety and security. Then, we highlight the state of the art and recent challenges in human-robot collaboration in manufacturing both from a safety and security perspective. We conclude with a list of procedural and technological issues to be tackled in the coordinated assurance of collaborative industrial robots.
rate research
Read More
Human-robot collaborations have been recognized as an essential component for future factories. It remains challenging to properly design the behavior of those co-robots. Those robots operate in dynamic uncertain environment with limited computation capacity. The design objective is to maximize their task efficiency while guaranteeing safety. This paper discusses a set of design principles of a safe and efficient robot collaboration system (SERoCS) for the next generation co-robots, which consists of robust cognition algorithms for environment monitoring, efficient task planning algorithms for reference generations, and safe motion planning and control algorithms for safe human-robot interactions. The proposed SERoCS will address the design challenges and significantly expand the skill sets of the co-robots to allow them to work safely and efficiently with their human counterparts. The development of SERoCS will create a significant advancement toward adoption of co-robots in various industries. The experiments validate the effectiveness of SERoCS.
Human-robot interactions have been recognized to be a key element of future industrial collaborative robots (co-robots). Unlike traditional robots that work in structured and deterministic environments, co-robots need to operate in highly unstructured and stochastic environments. To ensure that co-robots operate efficiently and safely in dynamic uncertain environments, this paper introduces the robot safe interaction system. In order to address the uncertainties during human-robot interactions, a unique parallel planning and control architecture is proposed, which has a long term global planner to ensure efficiency of robot behavior, and a short term local planner to ensure real time safety under uncertainties. In order for the robot to respond immediately to environmental changes, fast algorithms are used for real-time computation, i.e., the convex feasible set algorithm for the long term optimization, and the safe set algorithm for the short term optimization. Several test platforms are introduced for safe evaluation of the developed system in the early phase of deployment. The effectiveness and the efficiency of the proposed method have been verified in experiment with an industrial robot manipulator.
The objective of this paper is to present a systematic review of existing sensor-based control methodologies for applications that involve direct interaction between humans and robots, in the form of either physical collaboration or safe coexistence. To this end, we first introduce the basic formulation of the sensor-servo problem, then present the most common approaches: vision-based, touch-based, audio-based, and distance-based control. Afterwards, we discuss and formalize the methods that integrate heterogeneous sensors at the control level. The surveyed body of literature is classified according to the type of sensor, to the way multiple measurements are combined, and to the target objectives and applications. Finally, we discuss open problems, potential applications, and future research directions.
The application of robotic solutions to small-batch production is challenging: economical constraints tend to dramatically limit the time for setting up new batches. Organizing robot tasks into modular software components, called skills, and allowing the assignment of multiple concurrent tasks to a single robot is potentially game-changing. However, due to cycle time constraints, it may be necessary for a skill to take over without waiting on another to terminate, and the available literature lacks a systematic approach in this case. In the present article, we fill the gap by (a) establishing the specifications of skills that can be sequenced with partial executions, (b) proposing an implementation based on the combination of finite-state machines and behavior trees, and (c) demonstrating the benefits of such skills through extensive trials in the environment of ARIAC (Agile Robotics for Industrial Automation Competition).
Achieving human-like motion in robots has been a fundamental goal in many areas of robotics research. Inverse kinematic (IK) solvers have been explored as a solution to provide kinematic structures with anthropomorphic movements. In particular, numeric solvers based on geometry, such as FABRIK, have shown potential for producing human-like motion at a low computational cost. Nevertheless, these methods have shown limitations when solving for robot kinematic constraints. This work proposes a framework inspired by FABRIK for human pose imitation in real-time. The goal is to mitigate the problems of the original algorithm while retaining the resulting humanlike fluidity and low cost. We first propose a human constraint model for pose imitation. Then, we present a pose imitation algorithm (PIC), and its soft version (PICs) that can successfully imitate human poses using the proposed constraint system. PIC was tested on two collaborative robots (Baxter and YuMi). Fifty human demonstrations were collected for a bi-manual assembly and an incision task. Then, two performance metrics were obtained for both robots: pose accuracy with respect to the human and the percentage of environment occlusion/obstruction. The performance of PIC and PICs was compared against the numerical solver baseline (FABRIK). The proposed algorithms achieve a higher pose accuracy than FABRIK for both tasks (25%-FABRIK, 53%-PICs, 58%-PICs). In addition, PIC and its soft version achieve a lower percentage of occlusion during incision (10%-FABRIK, 4%-PICs, 9%-PICs). These results indicate that the PIC method can reproduce human poses and achieve key desired effects of human imitation.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
