PRISM: Pose Registration for Integrated Semantic Mapping

The MARTHA project objectives are the control and the management of a fleet of autonomous mobile robots for transshipment tasks in harbors, airports and marshalling yards. One of the most challenging and key problems of the MARTHA project is multi-robot cooperation. A general concept for the control of a large fleet of autonomous mobile robots has been developed, implemented and validated in the framework of the MARTHA project. This is the first study in the autonomous mobile robot field to add multi-robot cooperation capabilities to such a large fleet of robots.

We present and discuss the use of a generic scheme for multi-robot cooperation called the for managing a large fleet of autonomous mobile robots. Each robot, autonomously and incrementally builds and executes its own plans taking into account the multi-robot context obtained by collecting the current plans and goals of the other robots. We describe the overall system architecture and discuss the properties of our cooperative scheme. We show how the plan-merging paradigm (PMP) can be used in a hierarchical manner and how it fills the gap between centralized planning and distributed execution. We finally illustrate this scheme through an implemented system which allows a fleet of autonomous mobile robots to perform load transfer tasks in a route network environment. The central activity is limited to task allocation and important gains are obtained in system flexibility and robustness to execution contingencies. Simulations (using up to 30 robots) as well as experiments with real robots (3) are presented and discussed.

Controlling a fleet of autonomous mobile robots (AMR) is a complex problem of optimization. Many approached have been conducted for solving this problem. They range from heuristics, which usually do not find an optimum, to mathematical models, which are limited due to their high computational effort. Machine Learning (ML) methods offer another potential trajectory for solving such complex problems. The focus of this brief survey is on Reinforcement Learning (RL) as a particular type of ML. Due to the reward-based optimization, RL offers a good basis for the control of fleets of AMR. In the context of this survey, different control approaches are investigated and the aspects of fleet control of AMR with respect to RL are evaluated. As a result, six fundamental key problems should be put on the current research agenda to enable a broader application in industry: (1) overcoming the “sim-to-real gap”, (2) increasing the robustness of algorithms, (3) improving data efficiency, (4) integrating different fields of application, (5) enabling heterogeneous fleets with different types of AMR and (6) handling of deadlocks.

Industrial maintenance is undergoing a process of digitalization and automation through the use of Industry 4.0 technologies. An example is the employment of digital twins which allow virtualizing assets such as a fleet of Autonomous Mobile Robots (AMR) . In this paper, we analyze two local navigation algorithms for AMRs navigation in a digitized environment: Dynamic Window Approximation (DWA) and Time Elastic Band (TEB) . For this purpose, a digital twin is implemented in the ROS ecosystem. In this environment, a combined mission of a fleet of three AMRs is simulated where they navigate together in order to compare the behavior of the algorithms in dynamic industrial environments. As a result, the trajectories of each AMR that are key for production cost planning are analyzed. Finally, we introduce in this work a web server to interact with the developed digital twin facilitating its use.

This paper presents an implemented system which allows one to run a fleet of autonomous mobile robots in a route network with a very limited centralized activity. The robots are provided with all the necessary ingredients for planning and executing navigation missions in a multi-robot context. Multi-robot cooperation is based on a generic paradigm called plan-merging paradigm, where robots incrementally merge their plans into a set of already coordinated plans. The robot architecture is derived from the generic architecture developed at LAAS. A 3D graphic environment system allows one to display a complete system composed of a dozen (or more) robots, each running on an independent workstation. An example is presented together with numerical results on the system behavior.

A fleet of autonomous mobile robots are used in the hospital for the delivery service. To increase the efficiency of using multiple robots, an appropriate task allocation algorithm is required. This paper explains the task allocation procedure for multiple robots and introduces two multi-task allocation (MT) algorithms to be applied in the hospital delivery service. The algorithms are tested via simulation, and the performance are compared according to the criteria. The result shows that proposing combinatorial search algorithm with limited capacity performed better among others.

Systems composed of a fleet of autonomous mobile robots are among the most complex control systems. This control complexity is at a high level especially when those robots navigate in hazardous and dynamic environments such as chemical analysis laboratories. These systems include different dangerous and harmful products (toxic, flammable, explosive...) with different quantity. In order to perform its mission on a regular basis, this multi-robot system can be controlled according to multiple architectures. We propose, firstly, to apply the System Theoretic Process Analysis (STPA) on two selected control architectures, namely distributed and hybrid architectures in order to obtain a set of loss scenarios for each kind of architecture. For further assessment, the Analytic Hierarchy Process (AHP) is used to choose the best architecture. The proposed approach provides a risk analysis and a more practical comparison between the two control architectures of a mobile multi-robot system and facilitates decision-making, even in complex situations.

A 56 node (with 16 megabytes per processor) Intel Paragon was purchased in part under this grant and installed at rice University. Before acceptance, Rice and INTEL reached an agreement for trading in 16 nodes for additional memory; 32 megabytes for each of the 40 nodes. In August 1995, the machine was moved to The University of Texas at Austin (UT Austin) with Wheeler and members of the Center for Subsurface Modeling. The author gives a brief list of accomplishments by members of the Center for Subsurface Modeling at UT Austin.

Robotics is a scientific discipline which includes the operation and development of robots and other fields of computer science, as well as electrical and mechanical engineering and high-performance modular control systems. This paper reports the first results of a research project that uses a previously developed MES - Modular Electronic System for autonomous mobile robots and for developing robots to use the MES in other scientific applications. This approach offers the ability to build a scalable and flexible control system with only a few modules. A novel feature of our approach is its modular integration of electronics with the ability to multitask, which can be adapted for particular applications. We demonstrate the robustness of this approach in controlling an indoor mobile robot for the EUROBOT 2009 and 2010.

The construction of a smart autonomous mobile robot system that can operate in an anonymous environment can be difficult. Building an autonomous mobile robot requires collecting data from sensors and cameras in order to recognize the surrounding environment, get knowledge of existing landmarks, and figure out its position and orientation. Many research studies have been carried out in recent years providing algorithms for achieving autonomous motion of mobile robot systems and object detection. The most common platform utilized for robot motion design is ROS (Robot Operating System) and for object detection YOLO (You Only Look Once) is a common choice. Additionally, the autonomous system needs motion planning in a systematic way to avoid obstacles by using LIDAR or other sensing methods generating the feedback signals required for control of motion around occupied spaces. One of the most effective and versatile feedback systems for mobile robot control can be constructed using visual feedback, which relies on a high-resolution camera that processes the image in real-time and the result is fed back to the control system for decision making. Visual feedback can be employed alone without sensors for feedback control of autonomous mobile robots, and it can be used for multiple tasks like path planning, obstacle avoidance, object detection, object recognition in addition to motion control. In this research, results of design and construction of an autonomous mobile rescue robot for finding victims through video data are presented. It is aimed to find victims via applying video as a data collection and feedback generation tool in the autonomous mobile robot control system. The visual feedback and YOLO v3 are used not only for finding victims but also for motion control. Our autonomous mobile robot is designed to move smoothly to the victim's direction and avoids obstacles by using ROS Global Planner. The robot is tested in multiple scenarios in different locations, various obstacle and victim numbers, and satisfactory results with good performances are obtained.

Every year there will be thousands of students enrolling into universities. Before the starting of every semester, the head of department in all schools have a very important job which is deciding the numbers of classes/slots to be opened for every course and submit them to the Academic Affairs (HEA) office. The number of classes/slots for each course is determined based on the number of students enrolled for the course. Knowing the accurate number of classes/slots to be opened could help in allocating suitable lecture halls for each class/slot. This would then ensure the lecture halls are used to the fullest and the students would have a comfortable learning environment. The traditional method of counting the number of classes/slots manually course by course is rather troublesome and time consuming. Therefore, a web-based School of Computing (SOC) Course Slots Distribution System is developed to help solve the problem. The system aims to distribute the classes/slots for each course in BSc in Information Technology and BSc in Computer Science according to the number of students enrolled for the course. The system is developed using HTML, PHP and CSS. The system was then tested by the representatives of SOC management and evaluated based on user acceptance using an online survey platform – Google Form. The result of the evaluation suggested that the SOC Course Slots Distribution System is usable to assist the head of department of every school in Universiti Utara Malaysia (UUM) to calculate the number of classes/slots to be opened for each course efficiently. The respondents also suggested for the system to have lab allocation as well for further improvement. With the help of this system, the workload of head of departments will greatly decrease.

Recent studies in science and engineering education support that inductive learning activities encouraging active student involvement may improve students’ motivation, development of soft skills and academic performance, compared to traditional lectures. Until recently, several technology-enhanced learning environments have been proposed to facilitate such activities in classrooms. However, these commonly depend on dedicated hardware devices, such as clickers or tablet PCs. Contrastingly, smartphones are being massively adopted by society as these become increasingly powerful and inexpensive. Even so, the use of smartphones as learning tools in lecture halls has still not been widely adopted. In this paper we present CollPhoto, a paper-plus-smartphone environment that supports face-to-face problem solving activities in the classroom. CollPhoto provides the instructor with instant visibility of students’ work, and facilitates him/her conducting discussions, based on a selection of students’ responses. We report on the design and initial validation of CollPhoto in the context of two computer science courses.KeywordsSTEM educationface-to-face problem solvingpaper-plussmartphone environment

<title>Abstract</title> Digital twin (DT) technology is transforming industrial processes by enabling proactive decisions, minimizing human intervention, and improving automation coordination. This study explores how DTs can optimize production throughput by jointly modeling production processes, factory layouts, and heterogeneous robotic fleets with specialized roles such as transportation and manipulation. The proposed DT framework realistically simulates robotic operation and coordination, workflow dynamics, and spatial constraints across both brownfield and greenfield layouts. In brownfield settings, constrained by legacy infrastructure, the DT identifies optimal fleet size and reveals scalability limits and diminishing returns due to congestion, emphasizing the need for lean automation. In greenfield environments, designed for automation, the DT enables joint optimization of spatial layout, robotic fleet size, and functional role balance. The DT also determines that bottlenecks may arise not only from physical constraints but also from imbalances within the robotic fleet, such as insufficient manipulation capacity relative to transport capability. In a representative case study, our proposed framework suggests that scaling robotic fleets up in brownfield layouts yields minimal gains. In contrast, scaling robotic fleets up to a balanced configuration in greenfield layouts results in up to 3.5x gains in production throughput, without reducing individual efficiency of the robots.

Farmers are under growing pressure to intensify production to feed a growing population while managing environmental impact. Robotics has the potential to address these challenges by replacing large complex farm machinery with fleets of small autonomous robots. This article presents our research toward the goal of developing teams of autonomous robots that perform typical farm coverage operations. Making a large fleet of autonomous robots economical requires the use of inexpensive sensors, such as cameras for localization and obstacle avoidance. To this end, we describe a vision-based obstacle detection system that continually adapts to environmental and illumination variations and a visionassisted localization system that can guide a robot along crop rows with a complex appearance. Large fleets of robots will become time-consuming to monitor, control, and resupply. To reduce this burden, we describe a vision-based docking system for autonomously refilling liquid supplies and an interface for controlling multiple robots.

Dynamic pickup and delivery problem for autonomous delivery robots in an airport terminal