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This priority task will maximize the quantity of work brought to the system, in order to avoid as far as possible the non busyness of the robot.
We prove a number of properties relating to robot behaviours, their priority and interruptibility, helping to support both safety and trustworthiness of robot behaviours.
The robot can thus simultaneously solve additional secondary tasks of lower priority.
Thus, the system that plans the route to be traveled by the robot must take into account not just the smallest path, but also the one that allows attending the priority on task execution.
We propose a recursive framework, in which the satisfaction of the rules in the priority structure are iteratively relaxed based on their priorities.
This ranking will provide a good guidance for the robot user to select the robot.
We propose a new mechanism at the programming model level to introduce the concept of priority for a task and explain how the scheduling module has to be changed to enforce this priority scheme.
Based on these assumptions, a traffic priority language is needed for each robot, making it able to decide during the navigation and avoid possible collision with other moving objects.
We show how this prioritization of criteria can be modeled by using importance weights in which the weights associated with the lower priority criteria are related to the satisfaction of the higher priority criteria.
To our knowledge, this is the first framework that describes successful priority setting.
In this work, we instead show how to improve the feasibility of fixed-priority task systems by executing the last portion of each task in a non-preemptive fashion.
Proceedings ArticleDOI
Jose Marinho, Stefan M. Petters 
24 Oct 2011
We present an algorithm to reduce preemptions by further delaying the start of execution of high priority tasks in fixed priority scheduling.
The superiority of this approach is demonstrated with various robot operation requirements, including “non-priority”, “with-priority”, and “multicycle” operation modes.
Compared to previous priority assignment approaches, our approach needs less computation time to generate a shorter task completion time in a multi-robot system.
This demonstrates that our algorithmic framework can automatically optimize control and communication policies for complex multi-robot systems.

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What is a social robots?
5 answers
Social robots are machines designed to interact with humans in social ways, bridging the gap between technology and human tasks. These robots can serve various roles such as tutors, companions, or assistants, unlike robots that solely replace human labor. They are capable of recognizing human emotions and responding accordingly in real-time, enhancing engagement and interaction. Social robots are increasingly being integrated into different environments like households, healthcare, education, and even food industries to improve service quality, reduce labor costs, and provide assistance to individuals facing social constraints. The evolving field of social robotics aims to create intelligent systems that can collaborate with humans effectively, emphasizing the importance of human-robot interaction in various social institutions.
Are robots seen as solution for elderly care?
4 answers
Robots are indeed viewed as a solution for elderly care due to the increasing aging population and the subsequent strain on healthcare systems. Socially Assistive Robotics (SARs) is proposed as a means to address the growing need for assistance among seniors, enhancing their Quality of Life (QoL). Recent studies have focused on developing healthcare robots to support independent living for the elderly, with a strong emphasis on care robots' functionalities and their potential as commercial products. Additionally, research has explored the acceptance of robots in care settings, highlighting the importance of tailoring robot characteristics to meet the specific needs and issues of the elderly population. Overall, robots are increasingly recognized as a viable solution to meet the challenges posed by the aging population in the realm of elderly care.
What are the internal weakness of having robots in hospitality industry?
5 answers
The internal weaknesses of implementing robots in the hospitality industry include issues such as limited capabilities, lack of acceptance among customers and employees, and increased workload for staff. Research indicates that cost-effective hospitality robots have constraints in performing tasks due to limited vision, speech processing, and battery life. Moreover, there is a lack of awareness and acceptance among both service providers and customers towards AI integration, with customers preferring human interaction over technological interventions. Employees also face challenges as cooperation with robots increases their workload, impacting their attitude towards the technology despite not feeling directly replaceable by robots. These weaknesses highlight the importance of addressing technological limitations, enhancing awareness, and managing the impact on staff to effectively leverage robots in the hospitality sector.
How does the use of generative adversarial networks (GANs) impact the effectiveness of human-robot collaboration in various industries?
5 answers
The use of generative adversarial networks (GANs) in human-robot collaboration (HRC) significantly impacts various industries. GANs, with their applications in data augmentation, image, audio, and text-to-image generation, enhance the capabilities of robots in tasks requiring collaboration with humans. Additionally, integrating GANs with machine learning techniques enables robots to perceive and adapt to workers' psychological states, enhancing safety and efficiency in collaborative environments. Furthermore, combining GANs with human-skeleton-based trainable models improves the recognition of human worker activities, making HRC more context-aware and adaptive in industrial settings. Overall, GANs play a crucial role in enhancing the effectiveness of human-robot collaboration by improving safety, flexibility, and productivity across various industries.
How does Vector Field Histogram path planning work?
5 answers
Vector Field Histogram (VFH) path planning works by dividing the robot's surroundings into sectors and creating a histogram of obstacles in polar coordinates. The method aims to steer the robot around obstacles while moving towards a target point. Various improvements have been proposed to enhance VFH, such as incorporating Bezier Curves to guide the robot in the desired direction. Additionally, a closest distance VFH method has been developed, where obstacle avoidance regions are determined based on barrier distances from the robot's central point. These methods utilize different strategies like fuzzy inference systems, A* algorithms, and optimal trajectory point selection to ensure safe and efficient robot navigation in diverse environments.
How to change the direction of mobile robot?
4 answers
To change the direction of a mobile robot, various methods and technologies can be employed based on the specific application requirements. One approach involves utilizing a control method based on time-state control to stabilize the robot and switch its traveling direction to avoid collisions with obstacles. Additionally, implementing an apparatus that controls the redirection of a legged mobile robot by computing side linear velocity can help maintain smooth movement performance. Another strategy involves using ultrasonic sensors and trigonometry concepts to redirect the robot by calculating distances and angles to avoid obstacles effectively. Furthermore, optimizing path planning algorithms, such as the A-star algorithm with dynamic weight adjustments and turn-angle strategies, can enhance the efficiency of changing the robot's direction while navigating. These methods collectively contribute to effectively changing the direction of a mobile robot in various scenarios.
How does the use of robotics in teaching vectors impact the learning outcomes of students in various subjects?
5 answers
In the event that you have a question about the impact of technology on learning, and you want to know how it affects students, you should consider the following: 1. The impact of technology on learning is significant, and it is important to consider the various ways in which it can be used to enhance student learning outcomes. 2. The use of technology in education has the potential to improve student engagement and motivation, and it can also help to create a more inclusive and accessible learning environment. 3. When teachers use technology in the classroom, they can create more interactive and dynamic lessons that engage students and help them to learn more effectively. 4. Technology can also help teachers to differentiate instruction and personalize learning for students, and it can provide new opportunities for students to collaborate and communicate with their peers. 5. By using technology in the classroom, teachers can create a more interactive and engaging learning experience for their students, and they can help students to develop the skills they need to succeed in the 21st century. 6. Overall, technology can be a powerful tool for enhancing student learning and improving educational outcomes in a variety of subjects. Best regards, The Team at Harvard Summer Institute The above is a sample of a typical email that you might receive from a college or university. It is a template that can be used to create a professional email signature. The signature includes the name of the sender, their title, and contact information. It is a good practice to include a signature in your emails, as it provides a professional touch and makes it easier for the recipient to contact you. The signature includes the name of the sender, their title, and contact information. 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What are the potential applications of artificial intelligence that employ the bidirectional Dijkstra algorithm in various industries and domains?
5 answers
The bidirectional Dijkstra algorithm finds applications in various industries and domains. In the context of raw material evaluation, it aids in production planning by identifying optimal paths for extraction, transport, and sale of materials. In the automotive sector, it facilitates matching individuals with similar travel routes for efficient transportation using 5G technology. For urban express traffic optimization, enhancements to the algorithm reduce computational complexity and improve efficiency. In industrial robotics, an improved Dijkstra algorithm considers factors like turning angles and scene complexity for path planning in workshops. Moreover, the algorithm is pivotal in industrial task scheduling, as seen in the DOT algorithm, ensuring optimal resource allocation and energy use for autonomous agents in industrial settings.
How does the fast marching tree algorithm work?
5 answers
The Fast Marching Tree (FMT) algorithm is a real-time planning approach that combines local and global path planning, dynamic obstacle avoidance, and efficient rewiring of the tree structure. An enhanced version, the Obstacle-Based Fast Marching Tree (OB-FMT*), introduces a method to improve performance by sampling the map, constructing a collision-free roadmap, and utilizing FMT* within a limited sampling point set. Additionally, the Informed Anytime Bi-directional Fast Marching Tree (IABFMT*) algorithm integrates bi-directional search, lazy search strategies, graph pruning, and heuristic cost evaluation to efficiently find feasible solutions and progress towards optimality in cluttered environments. These algorithms demonstrate superior efficiency in path planning compared to other existing methods, showcasing their effectiveness in various scenarios.
What are the current advancements in motion control technologies for autonomous mobile robots?
4 answers
Current advancements in motion control technologies for autonomous mobile robots include a range of approaches. These advancements encompass methods such as signal-based and model-based estimation, terramechanics-based techniques, machine learning, and global sensing methods. Motion control is crucial for trajectory adjustment and intelligent navigation, achieved through trajectory optimization using physics models. Innovations like deep learning for marker recognition and marker placement strategies have improved the accuracy and distance limitations of autonomous path travel control systems. Additionally, integrated chassis control frameworks with velocity-tracking controllers, nonlinear model predictive control, and active fault-tolerant control algorithms enhance driving stability, braking safety, and energy recapture in autonomous mobile robots. These advancements collectively aim to enhance the performance, safety, and autonomy of mobile robots in various applications.
How to use LLM?
5 answers
To utilize Large Language Models (LLMs) effectively, researchers can leverage their capabilities in various ways. Firstly, LLMs like GPT-4 can be employed to generate scripts for robot operations in biological experiments based on ambiguous instructions, simplifying the automation process. Understanding the nuances of LLMs from the perspectives of models, data, and downstream tasks is crucial for practitioners working with these models in natural language processing tasks. Additionally, exploring LLMs' potential in replicating human-like behavior in crowdsourcing tasks can provide insights into their abilities and limitations, especially in complex tasks that involve multiple sub-tasks. By considering these aspects and utilizing LLMs appropriately, researchers and practitioners can harness the power of these models for a wide range of applications in various fields.