Search or ask a question

Which is the first city of India using robot to control traffic?

Mobile robot navigation

Answers from top 5 papers

PDF

Open Access

More filters

Papers (5)	Insight
Open access•Proceedings Article•DOI A reactive robot architecture with planning on demand Ananth Ranganathan, Sven Koenig - Show less +1 more 03 Dec 2003 37 Citations	Our robot architecture differs from other robot architectures in that it gives planning progressively greater control of the robot if reactive navigation continues to fail, until planning controls the robot directly.
Proceedings Article•DOI An attitude estimation system for amphibious spherical robots Liwei Shi, Rui Xiao, Shuxiang Guo, Ping Guo, Shaowu Pan, Yanlin He - Show less +5 more 03 Sep 2015 12 Citations	It has manifested that the system is effective to realize the robot control and navigation.
Proceedings Article•DOI Need-based coordination for decentralized high-level robot control Kai Weng Wong, Hadas Kress-Gazit - Show less +1 more 01 Oct 2016 12 Citations	Our approach captures both the advantage of decentralized robot control and that of centralized robot control.
Proceedings Article•DOI Developing an efficient mobile robot control algorithm in the Webots simulation environment B. Magyar, Z. Forhecz, Péter Korondi - Show less +2 more 10 Dec 2003 9 Citations	The main contribution of this paper is to present an approach for mobile robot control which can be a basis for another step in the development of intelligent autonomous robots.
Proceedings Article•DOI Discovering New Trends in Web Robot Traffic Through Functional Classification Derek Doran, Swapna S. Gokhale - Show less +1 more 10 Jul 2008 19 Citations	The analysis results indicate how the classification scheme can provide insights into the robot traffic based on their functionality.

My columns

Related Questions

How to use timed petri nets for traffic light systems in india?5 answersTimed Petri Nets (TPNs) offer a robust framework for modeling and optimizing traffic light systems in urban areas like India. By utilizing TPNs, traffic flow at intersections can be efficiently regulated through dynamic control strategies. These models can help in managing green light times autonomously, adapting to varying traffic conditions throughout the day. Additionally, TPNs can be instrumental in preventing large-scale urban traffic congestion by rerouting vehicles and coordinating traffic lights and dynamic message signs effectively. Moreover, TPNs can be employed to address traffic congestion issues by implementing regulatory traffic light control systems, ensuring smoother traffic flow and preventing vehicles from entering congested zones. Overall, leveraging TPNs in India's traffic light systems can significantly enhance traffic management efficiency and reduce congestion, ultimately improving overall traffic flow and safety.

What are the challenges and opportunities of using artificial intelligence for traffic control?5 answersArtificial intelligence (AI) offers opportunities for improving traffic control by addressing the challenges of traffic congestion and inefficient signal management systems. AI-based traffic prediction methods, combined with emerging technologies such as GPS, cloud computing, and the Internet of Things (IoT), can provide real-time data and predictive solutions for traffic management. However, integrating these technologies and developing efficient algorithms pose challenges in terms of complexity, scalability, security, and privacy. By using AI and image processing, traffic density can be accurately calculated in real-time, allowing for better traffic management and signal control. This can lead to reduced congestion, improved travel times, and lower emissions. Overall, the use of AI in traffic control presents both challenges and opportunities for enhancing the efficiency and effectiveness of traffic management systems.

Does India have robotic soldiers?6 answers

How many AI robots are there in India?5 answers

Is war robots banned in India?5 answers

Which Indian airline first used robotic technology?7 answers

See what other people are reading

What are the current research topics regarding wooden construction?

Current research topics in wooden construction include exploring wood-based additive manufacturing (AM) for sustainable manufacturing practices, utilizing digital design tools and robot arm technology for modern timber construction, investigating the involvement of robots in timber building construction to enhance efficiency and accuracy, proposing wooden building construction methods using mobile robots to improve construction efficiency and reduce manual labor, and analyzing the energy performance of wood-based constructions to promote their ecological benefits and energy savings. These research areas focus on enhancing construction processes, reducing environmental impact, improving efficiency, and promoting the sustainability of wooden structures through innovative technologies and design approaches.How are wheel selective drives used to compensate for steering actuator failure?

Wheel selective drives are utilized to compensate for steering actuator failures by implementing fail-operation control strategies and fault-tolerant control architectures. These approaches aim to address issues such as rollover, drift, and loss of steering control in heavy-duty vehicles. The fail-operation control strategy involves using a linear quadratic regulator (LQR) to determine additional yaw moments based on vehicle state errors, estimating compensating resistance moments generated by failed wheels, and distributing optimal torque to normal wheels for control. On the other hand, fault-tolerant control architectures incorporate fault detection, fault isolation methods, and adaptive model predictive controllers to ensure system redundancy, stability, and prevent rollover. These methods have been validated through simulations, demonstrating their effectiveness in reducing lateral trajectory errors and maintaining vehicle velocity.How are wheel selective drives used to compensate for steering actuator failure on an articulated vehicle?

Wheel selective drives are utilized to compensate for steering actuator failure on articulated vehicles by distributing drive torques to individual wheels, ensuring steering control even if an actuator fails. This approach eliminates the need for a steering actuator. In scenarios where steering actuators fail, fault-tolerant control architectures are designed to isolate and estimate the faulty actuator, employing fault detection, diagnosis, and fault-tolerant control mechanisms. For vehicles like in-wheel motor drive (IWMD) and wheel-individual steer electric vehicles, a fail-operation control strategy is implemented to correct steering failures by allocating additional yaw moments and compensating resistance moments to normal wheels, enhancing vehicle trackability and control. Similarly, in two linked two-wheel drive (2WD) mobile robots, a multiple-model based failure compensation scheme is developed to ensure stability and tracking properties in case of actuator failures.How to compare different survey interfaces?

To compare different survey interfaces, researchers have conducted experiments using various methodologies. One study focused on natural language interfaces (NLI) for databases, categorizing them into keyword-, pattern-, parsing-, and grammar-based systems. Another research compared input methods for numeric data entry, finding that clicking is the fastest, followed by typing, while dragging is the slowest. Additionally, a study evaluated human-robot interfaces using handheld devices, highlighting the efficiency and user preferences for different interaction metaphors. These diverse studies emphasize the importance of considering factors like predictive validity, user experience, and efficiency when comparing survey interfaces for various research and practical applications.What are the current algorithms in robotics?

Current algorithms in robotics encompass a wide range of approaches, including traditional planning algorithms, machine learning techniques, reinforcement learning, and policy gradient methods. Traditional planning algorithms involve graph search, sampling-based methods, and interpolating curve algorithms, while machine learning algorithms include support vector machines, LSTM, Monte-Carlo tree search, and CNN. Reinforcement learning algorithms like Q learning, deep Q-learning network, and policy gradient methods such as actor-critic algorithms are also prominent in robotics research. These algorithms aim to enhance robot navigation, obstacle avoidance, trajectory planning, and autonomous decision-making, contributing to the development of more efficient and capable robotic systems.What is the Social and Emotional Well-Being framework?

The Social and Emotional Well-Being framework encompasses feelings, behavior, relationships, goals, and personal strengths, reflecting a state of constructive emotional management and positive social interactions. It is crucial to consider cultural variations, individual differences, and temperament when assessing well-being. Additionally, emotional well-being is viewed as a fluctuating continuum that impacts one's ability to engage in daily activities, practice self-care, and nurture relationships, with its absence linked to diminished relationship quality and capacity for love. The development of societal well-being frameworks aims to go beyond economic growth, integrating both objective and subjective measures of well-being from diverse disciplines to provide a holistic view of progress and social welfare.What is backtrack in mobile robot SLAM?

Backstepping in mobile robot SLAM refers to a control algorithm design technique used for trajectory tracking. This method focuses on developing robust output tracking controllers based on Lyapunov stability analysis to ensure accurate and stable tracking of trajectories in various conditions. Backstepping theory is relatively new in the field of mobile robot control, showing effectiveness in solving nonholonomic constraints and achieving satisfactory performance. The approach involves designing controllers that provide desired linear and angular velocities for the robot to follow a given trajectory, enhancing stability and accuracy in tracking errors. Overall, backstepping plays a crucial role in improving the trajectory tracking capabilities of mobile robots in SLAM applications.Issues with delivery robots

Delivery robots face several challenges highlighted in the research papers. One major issue is the difficulty in climbing curbs and stairs, especially for six-wheeled robots, due to inadequate mechanisms. Another challenge is ensuring secure last-mile delivery, which is addressed through AI-driven systems incorporating user authentication and identification modules for accurate and safe parcel delivery. Autonomous outdoor delivery robots encounter obstacles and environmental conditions, necessitating robust localization, safe navigation, and effective path planning strategies to navigate dynamic outdoor environments successfully. Moreover, energy supply during abnormal conditions like disasters is critical for uninterrupted robot operation, leading to the exploration of various power sources such as direct energy, solar energy, and flywheel storage energy for continuous delivery services.What are the uses of evolutionary algorithms in robotics ?

Evolutionary algorithms play a crucial role in robotics, particularly in areas such as robot locomotion, route planning, and controller evolution. These algorithms, inspired by natural evolution and genetics, are utilized to optimize robot behaviors and designs. In robot locomotion, evolutionary algorithms aid in evolving controllers that adapt to variable conditions, ensuring robust performance. Additionally, these algorithms are instrumental in mobile robot route planning, helping robots navigate environments efficiently by finding optimal paths while considering various constraints and obstacles. Moreover, evolutionary algorithms enable the automated design of robots, optimizing their structures to exhibit desired behaviors rapidly and effectively. Overall, evolutionary algorithms enhance the capabilities of robots in diverse applications, ranging from search and rescue to transportation and beyond.What has written "Arie Haenel"?

"Arie Haenel" has not been mentioned in the provided contexts. The contexts discuss topics such as autonomous controllers for cooperative robotics missions, a new camera/spectrograph system for astronomical observations, an airborne interferometer for simulating satellite-based infrared sounders, a mobile robot inspection system for radioactive waste drums, and an interferometer mounted on a research aircraft for meteorological purposes. Therefore, based on the information available in the contexts, there is no direct reference to any writing or contribution by Arie Haenel.What are the future trends in mobile robot design?

Future trends in mobile robot design include advancements in control mechanisms, perception technologies, geometric adaptability, and fault-tolerant control. These trends are driven by the need for adaptation to complex environments, stable communication, and robust control. Additionally, the integration of technologies such as real-time positioning, navigation, trajectory planning, target tracking, and multi-agent systems will play a crucial role in enhancing the flexibility, mobility, and overall performance of automatic mobile robots. Furthermore, the evolution of design characteristics, obstacle avoidance capabilities, and improved fault tolerance are key areas of focus for future research and development in mobile robot systems.