Toward autonomous timber construction using distributed robotic perception system in coordination with a tower crane

A laser-technology-based lifting-path tracking system for a robotic tower crane

<span lang="EN-US">A humanoid robot called BarelangFC was designed to take part in the Kontes Robot Indonesia (KRI) competition, in the robot coordination division. In this division, each robot is expected to recognize its opponents and to pass the ball towards a team member to establish coordination between the robots. In order to achieve this team coordination, a fast and accurate system is needed to detect and estimate the other robot’s position in real time. Moreover, each robot has to estimate its team members’ locations based on its camera reading, so that the ball can be passed without error. This research proposes a Tiny-YOLO deep learning method to detect the location of a team member robot and presents a real-time coordination system using a ZED camera. To establish the coordinate system, the distance between the robots was estimated using a trigonometric equation to ensure that the robot was able to pass the ball towards another robot. To verify our method, real-time experiments was carried out using an NVDIA Jetson NX Xavier, and the results showed that the robot could estimate the distance correctly before passing the ball toward another robot.</span>

Autonomous mobile robot soccer is a very popular field of research. The complexity of robot designs that include perception systems, processing systems, drive systems, inter-subsystem coordination systems, and intelligence in game strategies, make the development of soccer robots even more exciting and challenging. This paper discussed the design results of the coordination system of soccer robot. The coordination system is responsible for integrating information from the perception system into game strategies. Then the game strategy processing generates information to the locomotion system, dribbler, and kicker. The coordination system between subsystems is implemented using behavior-based intelligent systems. This selection is based on intelligent behavior-based system design designed by bottom-up, starting from simple behavior. So it allows researchers to develop it in a sustainable manner. The implementation of this system is using Finite State Machine (FSM) with the help of fuzzy logic as the basis of decision making. This soccer robot coordination system has been implemented on a robot platform named Devara. From the test results in the field in the game, the robot can recognize the ball, move towards the ball, dribble, and kick the ball towards the goal. The average time needed by the robot in the game to find the ball placed in several positions, then dribble and score the goal is 13.2 seconds.

In the implementation of building construction, especially in the context of functionality of campus buildings, punctuality is an important thing that must be strictly followed because it concerns the start of lecture schedules that cannot be delayed. Therefore, it is necessary to have precise time management of building construction during all the implementation. It’s cannot be separated in building construction between the innovation of work methods and improvisation of quality control which are two processes that need to be implementation in an effort to achieve efficiency and. One of the innovations in building construction, is installation of a concrete precast wall as a cover for the facade of the Binus campus building. Concrete precast wall is made of a good material, as an effort to reduce solar heat entering the room. Furthermore, all existing constraints must be mapped properly as support implementation concrete precast wall. One of antecedents of implementation concrete precast wall in building construction is time management of machine construction tools when use tower crane. Researcher knows the issues of building construction are limitations time using tower crane efficiently, working tools, weather conditions, time of material application, labelling system, quality control mechanism and installation system. In other side, those strengths, weaknesses, opportunities, threats, that can be used as an approach and analysis, start from resource mapping, work requirements, material quality control and coordination systems between sub-supporters. The success of this work depends on the application and coordination calculation time, inspection and quality procedure is main of a consistent methodology from manufacturing in the workshop to installation in the field. In addition, efficiency and effectiveness can provide good quality, saving time and costs.

Control systems for autonomous robots are concurrent, distributed, embedded, real-time and data intensive software systems. A real-world robot control system is composed of tens of software components. For each component providing robotic functionality, tens of different implementations may be available. The difficult challenge in robotic system engineering consists in selecting a coherent set of components, which provide the functionality required by the application requirements, taking into account their mutual dependencies. This challenge is exacerbated by the fact that robotics system integrators and application developers are usually not specifically trained in software engineering. In various application domains, software product line (SPL) development has proven to be the most effective approach to face this kind of challenges. In a previous paper [D. Brugali and N. Hochgeschwender, Managing the functional variability of robotic perception systems, in First IEEE Int. Conf. Robotic Computing, 2017, pp. 277–283.] we have presented a model-based approach to the development of SPL for robotic perception systems, which integrates two modeling technologies developed by the authors: The HyperFlex toolkit [L. Gherardi and D. Brugali, Modeling and reusing robotic software architectures: The HyperFlex toolchain, in IEEE Int. Conf. Robotics and Automation, 2014, pp. 6414–6420.] and the Robot Perception Specification Language (RPSL) [N. Hochgeschwender, S. Schneider, H. Voos and G. K. Kraetzschmar, Declarative specification of robot perception architectures, in 4th Int. Conf. Simulation, Modeling, and Programming for Autonomous Robots, 2014, pp. 291–302.]. This paper extends our previous work by illustrating the entire development process of an SPL for robot perception systems with a real case study.

Robot system used in building construction sites can efficiently reduce construction time and increase safety by replacing human in dangerous operations. This paper describes the development of a robotic manipulator control algorithm which provides stable and efficient gripping of a pendulum-like object. The pendulum object mimics the construction materials hanging from tower crane, such as steel beam and column. The robot should be able to handle the heavy objects in order to be used in the building construction sites. This control algorithm requires dynamic modeling of the hanging object. To simplify the analysis, the dynamic modeling was limited to the 2 dimensional pendulum movements in x-y plane. In order to achieve the stable and efficient gripping, a shock isolator is designed using a pre-acting control.

In this paper, a novel reinforcement learning mission supervisor (RLMS) with memory is proposed for human-multi-robot coordination systems (HMRCS). The existing HMRCS are known to suffer from long decision waiting time and large mission error caused by repeated human intervention, restricting the autonomy of multi-robot systems. The proposed supervisor elaborately integrates deep-Q-network (DQN) and long-short-term memory (LSTM) knowledge base within the null-space based behavioral control (NSBC) framework, so as to achieve optimal adjustment strategy of the behavioral priority in the presence of mission conflicts, and to reduce the frequency of human intervention. In particular, the proposed RLMS with memory first memorize human intervention history when robot systems are not confident in decision making when encountering emergencies, and then reload the history information when encountering the same situation that have been tackled by human previously. Simulation demonstrates the effectiveness of proposed RLMS with memory.KeywordsHuman-multi-robot coordination systemsReinforcement learningNull-space based behavioral controlMission supervisor

Robot coordination and control systems for remote teleoperation applications are by necessity implemented on distributed computers. Modeling and performance analysis of these distributed robotic systems is difficult, but important for economic system design. Performance analysis methods originally developed for conventional distributed computer systems are often unsatisfactory for evaluating real-time systems. The paper introduces a formal model of distributed robotic control systems; and a performance analysis method, based on scheduling theory, which can handle concurrent hard-real-time response specifications. Use of the method is illustrated by a case of remote teleoperation which assesses the effect of communication delays and the allocation of robot control functions on control system hardware requirements.

Sensory data and AI/ML techniques are crucial to several robotics applications, which is why perception in robots is a hot topic. Some of these applications include: object recognition, scene understanding, environment representation, activity identification, semantic location classification, object modeling, and pedestrian/human detection. Robotic perception, as used in this article, is the collection of machine learning (ML) techniques and methods that allow robots to process sensory data and form conclusions and perform actions accordingly. It is clear that recent development in the field of ML, mostly deep learning methodologies, have led to improvements in robotic perception systems, which in turn make it possible to realize applications and activities that were previously unimaginable. These recent advancements in complex robotic tasks, human-robot interaction, decision-making, and intelligent thought are in part due to the fast development and widespread usage of ML algorithms. This article provides a survey of real-world and state of the art applications of intelligent perception systems in robots.

Self-replicating robots represent a new area for prospective advancement in robotics. A self-replicating robot can identify when additional robots are needed to solve a problem or meet user needs, and create them in response to this identified need. This allows robotic systems to respond to changing (or non-predicted) mission needs. Being able to modify the physical system component provides an additional tool for optimizing robotic system performance. This paper begins the process of developing a command and coordination system that makes decisions with the consideration of replication, repair, and retooling capabilities. A high-level algorithm is proposed and qualitatively assessed.

The integration of advanced robotics into manufacturing production lines promises unprecedented flexibility and efficiency. However, a significant barrier to widespread adoption is the inherent incompatibility between the complex requirements of optimal robotic trajectory planning and the traditional programming paradigms prevalent in industrial automation, such as Ladder Logic and Structured Text (ST) for Programmable Logic Controllers (PLCs). Existing workforces, mainly composed of electricians and technicians, often lack the advanced mathematical and object-oriented programming skills necessary for optimized robot control. This paper introduces a middleware framework that leverages Large Language Models (LLMs) to enable intuitive human-robot interactions in robotic trajectory planning. By bridging the communication gap between industrial workforces and robotic systems, this framework democratizes access to sophisticated robotic capabilities, making advanced robotics more accessible to a broader range of users. Our approach introduces an innovative, formatted configuration file for comprehensive system description, an iterative LLM Retrieval-Augmented Generation (RAG) coordination system that orchestrates an external optimization-based trajectory planner and incorporates human feedback, and a comprehensive Continuous Integration/Continuous Deployment (CI/CD) testing architecture for validation. This methodology aims to empower industrial operators to effectively utilize advanced robotics by abstracting technical complexities and fostering intuitive human-AI interactions, thereby accelerating innovation and enhancing productivity in the manufacturing sector.

Chemical communication is a ubiquitous process in nature, and it has sparked interest in the development of electric-sense-based robotic perception systems with chemical components. Here, a novel liquid crystal polymer is introduced that combines the transferring, receiving, and sensing of chemical signals, providing a new principle to achieve chemical communication in robotic systems. This approach allows for the transfer of cargo between two polymer coatings, and the transfer can be monitored through an electrical signal. Additionally, cascade transfer can be achieved through this approach, as the transfer of cargo is not limited to only two coatings, but can continue from the second to a third coating. Furthermore, the two coatings can be infused with different reagents, and upon exchange, a reaction takes place to generate the desired species. The novel method of chemical communication that is developed presents a notable improvement in embodied perception. This advancement facilitates human-robot and robot-robot interactions and enhances the ability of robots to efficiently and accurately perform complex tasks in their environment.

We present a method for computing exact reachable sets for deep neural networks with rectified linear unit (ReLU) activation. Our method is well-suited for use in rigorous safety analysis of robotic perception and control systems with deep neural network components. Our algorithm can compute both forward and backward reachable sets for a ReLU network iterated over multiple time steps, as would be found in a perception-action loop in a robotic system. Our algorithm is unique in that it builds the reachable sets by incrementally enumerating polyhedral cells in the input space, rather than iterating layer-by-layer through the network as in other methods. If an unsafe cell is found, our algorithm can return this result without completing the full reachability computation, thus giving an anytime property that accelerates safety verification. In addition, our method requires less memory during execution compared to existing methods where memory can be a limiting factor. We demonstrate our algorithm on safety verification of the ACAS Xu aircraft advisory system. We find unsafe actions many times faster than the fastest existing method and certify no unsafe actions exist in about twice the time of the existing method. We also compute forward and backward reachable sets for a learned model of pendulum dynamics over a 50 time step horizon in 87s on a laptop computer. Algorithm source code: https://github.com/StanfordMSL/Neural-Network-Reach.

Human-robot collaboration is transforming healthcare, particularly in surgical environments. Robotic surgery systems, embodied by advanced AI, are pivotal in augmenting human expertise across specialties such as gynecology and laparoscopic surgery. However, critical gaps remain in understanding how knowledge, agency, and ownership shape these collaborations. We address these gaps through semi-structured interviews with eleven healthcare professionals from diverse surgical roles. Our findings reveal that while robotic systems enhance precision and efficiency, they also generate tensions related to professional autonomy, control, and responsibility. Participants expressed ambivalent views, simultaneously demonstrating trust in the technology and strategic disengagement to preserve human authority. Concepts such as avatarization, the perception of robots as extensions of the self, and strategic ignorance emerged as key mechanisms through which professionals manage this evolving relationship. These dynamics point to the need for rethinking human–robot roles as fluid and co-constructed rather than fixed or hierarchical. We also emphasize the possible use of robotic systems to promote inclusivity and accessibility in healthcare while identifying structural barriers such as high costs, dependence on proprietary technology, and uneven organizational readiness. Our research enhances theoretical frameworks on human–robot interaction, providing practical and conceptual insights for the creation of equitable, sustainable, and context-sensitive robotic healthcare systems.

We describe our experience with using several CORBA products to interconnect the software modules of a fairly complex system for file caching coordination from a tertiary storage. The application area that the system was designed for is High Energy and Nuclear Physics (HENP). In this application area, the volume of data reaches hundreds of terabytes per year and therefore it is impractical to store them on disk systems. Rather the data are organized into large files and stored on robotic tape systems that are managed by some Mass Storage System (MSS). The role of the Storage Access Coordination System (STACS) that we developed is to manage the caching of files from the MSS to a large disk cache that is shared by multiple HENP analysis programs. The system design involved multiple components developed by different people at different sites and the modules could potentially be distributed as well. In this paper we describe the architecture and implementation of STACS, emphasizing the inter‐module communication requirements. We describe the use of CORBA interfaces between system components, and our experience with using multi‐threaded CORBA and using hundreds of concurrent CORBA connections. STACS development was recently completed and is being incorporated in an operational environment that started to produce data in the summer of 2000 [1]. Copyright © 2001 John Wiley & Sons, Ltd.