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The performance usually decreases if the considered object lacks texture and if the background is heavily cluttered. In the works listed above, learning algorithms based on classical ML methods and deep-learning e. However, current solutions are either heavily tailored to a specific application, requiring specific engineering during deployment, or their generality makes them too slow or imprecise to fulfill the tight time-constraints of industrial applications.

While deep learning holds the potential to both improve accuracy i. Domain adaptation and domain randomization i. Usually, in traditional mobile robot manipulation use-cases, the navigation and manipulation capabilities of a robot can be exploited to let the robot gather data about objects autonomously. This can involve, for instance, observing an object of interest from multiple viewpoints in order to allow a better object model estimation, or even in-hand modeling.

In the case of perception for mobile robots and autonomous robot vehicles, such options are not available; thus, its perception systems have to be trained offline. The development of advanced perception for full autonomous driving has been a subject of interest since the s, having a period of strong development due to the DARPA Challenges , , and and the European ELROB challenges since , and more recently, it has regained considerable interest from automotive and robotics industries and academia.

Research in self-driving cars, also referred as autonomous robot-cars, is closely related to mobile robotics and many important works in this field have been published in well-known conferences and journals devoted to robotics. In addition to the sensors e.

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The rationale is to improve robustness and safety by providing complementary information to the perception system, for example: the position and identification of a given object or obstacle on the road could be reported e. The EU FP7 Strands project [ 52 ] is formed by a consortium of six universities and two industrial partners. The aim of the project is to develop the next generation of intelligent mobile robots, capable of operating alongside humans for extended periods of time. While research into mobile robotic technology has been very active over the last few decades, robotic systems that can operate robustly, for extended periods of time, in human-populated environments remain a rarity.

Strands aims to fill this gap and to provide robots that are intelligent, robust, and can provide useful functions in real-world security and care scenarios. A task scheduling mechanism dictates when the robot should visit which waypoints, depending on the tasks the robot has to accomplish on any given day. The perception system consists, at the lowest level, of a module which builds local metric maps at the waypoints visited by the robot.

These local maps are updated over time, as the robot revisits the same locations in the environment, and they are further used to segment out the dynamic objects from the static scene. The dynamic segmentations are used as cues for higher level behaviors, such as triggering a data acquisition and object modeling step, whereby the robot navigates around the detected object to collect additional data which are fused into a canonical model of the object [ 53 ]. The data can further be used to generate a textured mesh through which a convolutional neural network can be trained which can successfully recognize the object in future observations [ 31 , 32 ].

The dynamics detected in the environment can be used to detect patterns, either through spectral analysis i. In addition to the detection and modeling of objects, the Strands perception system also focuses on the detection of people.

Robot systems reliability and safety: a review

Robust perception algorithms that can operate reliably for extended periods of time are one of the cornerstones of the Strands system. However, any algorithm deployed on the robot has to be not only robust, but also able to scale as the robot makes more observations and collects more information about the world. One of the key parts that would enable the successful operation of such a robotic system is a perception stack that is able to continuously integrate observations about the world, extract relevant parts as well as build models that understand and are able to predict what the environment will look like in the future.

Advanced robots operating in complex and dynamic environments require intelligent perception algorithms to navigate collision-free, analyze scenes, recognize relevant objects, and manipulate them. Nowadays, the perception of mobile manipulation systems often fails if the context changes due to a variation, e. Then, a robotic expert is needed who needs to adjust the parameters of the perception algorithm and the utilized sensor or even select a better method or sensor. Thus, a high-level cognitive ability that is required for operating alongside humans is to continuously improve performance based on introspection.

This adaptability to changing situations requires different aspects of machine learning, e. Then, a ground truth annotation tool can be used by the user to mark satisfying results, or correct unsatisfying ones, where the suggestions and interactive capabilities of the system reduced the cognitive load of this often complicated task especially when it comes to 6 DoF pose annotations , as shown in user studies involving computer vision expert and nonexpert users alike.

These annotations are then used by a Bayesian optimization framework to tune the off-the-shelf pipeline to the specific scenarios the robot encounters, thereby incrementally improving the performance of the system. The project did not focus only on perception, but on other key technologies for mobile manipulation as well. Bayesian optimization and other techniques were used to adapt the navigation, manipulation, and grasping capabilities independently of each other and the perception ones.

However, the combinatorial complexity of the joint parameter space of all the involved steps was too much even for such intelligent meta-learners. Such functionalities go beyond the usual focus of robotics research groups, while academics focusing on user experience typically do not have the means to develop radically new robots.

In such, and similar cases, robotic assistants that can be deployed and booked flexibly can possibly help alleviate some of the problem. The SPENCER consortium integrated the developed technologies onto a robot platform whose task consists in picking up short-transfer time passenger groups at their gate of arrival, identifying them with an onboard boarding pass reader, guiding them to the Schengen barrier and instructing them to use the priority track [ 59 ]. Additionally, the platform was equipped with a KLM information kiosk and provides services to passengers in need of help.

In crowded environments such as airports, generating short and safe paths for mobile robots is still difficult.

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Thus, social scene understanding and long-term prediction of human motion in crowds is not sufficiently solved but highly relevant for all robots that need to quickly navigate in human environments, possibly under temporal constraints. Classical path planning approaches often result in an overconstrained or overly cautious robot that either fails to produce a feasible and safe path in the crowd, or plans a large and suboptimal detour to avoid people in the scene.

In this context, it is important to say that level 5 cars i. We can say that the perception system is in charge of all tasks related to object and event detection and response OEDR.

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Within a connected and cooperative environment, connected cars would leverage and complement onboard sensor data by using information from vehicular communication systems i. The recent surge and interest in deep-learning methods for perception has greatly improved performance in a variety of tasks such as object detection, recognition, semantic segmentation, etc. The tool can import and export safety configurations to some devices. The devices that cannot be accessed by the tool are configured manually by the instructions provided by the software tool.

The tool also supports configuration of MS Kinect cameras of system described in previous chapter. The complexity of the configuration can be imagined by calculating possible combinations of the dynamic safety system. For example, the safety system can apply four safety speeds, four robot allowed areas and 16 safety areas for each laser scanner e.

All the combinations need to be configured and there can be thousands of combinations here The large amount of cases allows a person to walk safely near the robot. If the amount of combinations were smaller, then, in some cases, the robot must stop from a longer distance to ensure safety. Actually, this is exchange between complexity with laborious configuration and safety distance with possibility to go near the robot. The amount of devices is often the same, since the safety sensors must cover the robot work area, anyway.

The difference is on the work demanded on the configuration and testing. It is difficult to configure dynamic safety system reliably manually. Therefore, the safety configurator tool is required, but there is still a lot of testing to ensure that the model at the configuration tool is realised correctly to the real robot system. Nearly all mistakes result in too slow speed or long safety distance, since most of the wires are duplicated and discrepancy causes protective stop, and many conditions need to be fulfilled before an unsafe situation can occur i.

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However, there is a possibility of undetected dangerous fault in the realisation of the configuration. One problem related to configuration is that collaborative robot cells are modified relatively often and reconfiguration should not be an exhausting process. It can be a time-consuming process to consider, each time a system is changed, new risks and related safety measures [ 16 ].

Transparent Role Assignment and Task Allocation in Human Robot Collaboration

Currently, the safety devices and robot safety controller use predefined discrete safety areas and speeds, but not a seamless, smoothly changing performance according to the safe distance equation. In the future, it may be possible to configure the safety system just by applying simple rule for robots to keep the stopping distance adequate all the time.

Intelligent Robotic Perception Systems

This could reduce many faults related to configuration and also reduce the need for considering new safety measures for new risks. Currently, the dynamic safety system for robots is too expensive for many possible collaborative industrial robot applications, because the interface between robot and safety controller, sensors and safety sensors is complex from the safety point of view.

In the future, safety and robot controllers should be unified and dynamic safety system should be able to support configuration and validation tools. The advantage of the dynamic safety system is that it can provide safety without fences, production almost without protective stops and human-robot collaboration, in cases, where the industrial robot can do the hard or monotonous work and humans can observe, make decisions and do flexible tasks.

We do not know yet all the possibilities for human-robot collaboration, since the accepted described in standards concept is relatively new and still evolving e.

Frontiers | Toward Formal Models and Languages for Verifiable Multi-Robot Systems | Robotics and AI

Especially, the development of robot safety controllers could provide new possibilities to robot safety systems. Safety is currently limiting factor for human-robot collaboration, since designers would like to have immediate responses for safety functions and simultaneously perfect reliability. Unfortunately, novel sensors do not detect all they should and human hand can move a long way before robot brakes realise stopping.

Apparently, economy is one enabling factor in emerging technologies. Large number of successful applications would decrease the application costs design, material and safety and furthermore make the application more attractive. The dynamic safety system enables use of industrial robots in collaborative tasks by applying human - robot separation strategy. Non-safe technology is applied when a person is approaching the robot and safe technology when robot needs to be stopped for safety purposes. The dynamic safety system for robots is a versatile and complex system.

Since it is versatile, several sensors can be applied in the system and safety areas, robot allowed speed and positions can be configured according to the system. For simple systems safety PLC is not always required, but robot safety controller and safety sensors are, in order to have reliable information about the robot and persons. The dynamic safety system shows that, currently, safety systems for collaborative heavy industrial robots are complex. The complexity can be a safety issue and the validation process is laborious.

These factors require more development in the future. Apparently, many strategies will be applied to ensure safe collaboration of robots and human beings. The main funder of the project is Business Finland Oy. In addition, seven companies at the current project and six more at the previous project have supported the development of the dynamic safety system together with VTT Technical Research Centre of Finland. Malm T. Intelligent sensor controlling the danger zone of an industrial robot.

Cui G. University of Ontario Institute of Technology. Minerva, Canada.

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Halme R-J, Lanz M. Review of vision-based safety systems for human-robot collaboration. Published by: Elsevier B. Michalosa G. CIRPe - Understanding the life cycle implications of manufacturing.