Revolutionizing Robotics: Passivity Based Control and Estimation in Networked Communications

In the ever-evolving field of robotics, advances in communication systems play a crucial role in enabling robots to work cooperatively, share information, and perform complex tasks efficiently. Networked robotics communication refers to the exchange of data and instructions between multiple robots through a network, enabling them to coordinate their actions and achieve common goals.

Passivity-based control and estimation techniques have emerged as a groundbreaking solution to enhance the performance and stability of networked robotics communication systems. This article explores the concepts, benefits, and applications of passivity-based control and estimation in networked robotics communications.

Understanding Passivity-Based Control

Passivity is a fundamental property of dynamical systems that ensures energy preservation and stability. In the context of robotics, passivity-based control focuses on preserving the energy balance within a networked system, thereby improving its stability and performance. By utilizing the concept of passivity, robots can communicate with each other in a synchronized manner while efficiently managing their energy resources.

Passivity-Based Control and Estimation in Networked Robotics (Communications and Control Engineering)

by Marcos Lacerda(2015th Edition, Kindle Edition)

4.8 out of 5

Language	:	English
File size	:	24753 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Word Wise	:	Enabled
Print length	:	639 pages
Screen Reader	:	Supported

FREE

Passivity-based control techniques employ mathematical models to describe the behavior of robots and their interactions in a networked communication system. These models help in analyzing and optimizing the energy flow, ensuring that the overall system remains stable and efficient.

The Role of Estimation in Networked Robotics Communications

Estimation plays a vital role in networked robotics communications by providing robots with accurate information about their environment, the positions of other robots, and the necessary parameters for effective collaboration. Estimation techniques allow robots to sense, perceive, and respond to changes in their surroundings, enhancing their decision-making capabilities.

Passivity-based estimation techniques leverage the concept of energy preservation to estimate unknown variables or states within a networked robotic system. These techniques employ advanced algorithms and mathematical models to estimate robot positions, velocities, forces, and other critical parameters.

Benefits of Passivity Based Control and Estimation

The integration of passivity-based control and estimation in networked robotics communications offers several significant benefits:

• Improved Stability: Passivity-based control ensures the stability of networked communication systems, reducing the chances of signal loss, delays, or erratic behavior.
• Enhanced Efficiency: By managing the energy flow within the communication network, passivity-based control optimizes the usage of available resources, resulting in improved energy efficiency and longer battery life for robots.
• Effective Collaboration: Passivity-based control and estimation techniques enable robots to work collaboratively and share information seamlessly, enhancing their cooperation and coordination for complex tasks.
• Adaptive Responses: Passivity-based estimation allows robots to adapt to changes in the environment or network conditions by continuously updating their estimates and predictions.
• Robustness: Passivity-based control and estimation provide robustness to networked robotics communications, making the system resilient to failures, disturbances, or uncertainties.

Applications of Passivity Based Control and Estimation

The integration of passivity-based control and estimation techniques in networked robotics communications has opened up exciting possibilities in various domains:

• Warehouse Automation: Passivity-based control and estimation enable efficient coordination between robots in automated warehouses, leading to optimized material handling and order fulfillment.
• Search and Rescue Operations: Networked robots with passivity-based control and estimation capabilities can efficiently search an area for survivors, exchange critical information, and enhance the effectiveness of rescue missions.
• Manufacturing and Assembly: Passivity-based communication techniques ensure smooth cooperation between robots in industrial assembly lines, reducing errors and enhancing productivity.
• Medical Robotics: Passivity-based control and estimation techniques are crucial for the precise coordination of surgical robots, ensuring safe and accurate procedures during surgeries.
• Exploration and Mapping: Networked robots equipped with passivity-based control and estimation can collaboratively explore unknown environments, map terrain, and collect valuable data for scientific research or disaster management.

Passivity-based control and estimation techniques have revolutionized networked robotics communications by enhancing stability, efficiency, and collaboration between robots. Through the preservation of energy and the utilization of advanced estimation algorithms, passivity-based approaches have opened up opportunities in various sectors, including warehousing, search and rescue, manufacturing, medicine, and exploration. The integration of passivity-based control and estimation marks a significant milestone in advancing the capabilities and performance of networked robotics communication systems.

Passivity-Based Control and Estimation in Networked Robotics (Communications and Control Engineering)

by Marcos Lacerda(2015th Edition, Kindle Edition)

4.8 out of 5

Language	:	English
File size	:	24753 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Word Wise	:	Enabled
Print length	:	639 pages
Screen Reader	:	Supported

FREE

Highlighting the control of networked robotic systems, this book synthesizes a unified passivity-based approach to an emerging cross-disciplinary subject. Thanks to this unified approach, readers can access various state-of-the-art research fields by studying only the background foundations associated with passivity. In addition to the theoretical results and techniques, the authors provide experimental case studies on testbeds of robotic systems  including networked haptic devices, visual robotic systems,  robotic network systems and visual sensor network systems.

The text begins with an to passivity and passivity-based control together with the other foundations needed in this book. The main body of the book consists of three parts. The first examines how passivity can be utilized for bilateral teleoperation and demonstrates the inherent robustness of the passivity-based controller against communication delays. The second part emphasizes passivity’s usefulness for visual feedback control and estimation. Convergence is rigorously proved even when other passive components are interconnected. The passivity approach is also differentiated from other methodologies. The third part presents the unified passivity-based control-design methodology for multi-agent systems. This scheme is shown to be either immediately applicable or easily extendable to the solution of various motion coordination problems including 3-D attitude/pose synchronization, flocking control and cooperative motion estimation.

Academic researchers and practitioners working in systems and control and/or robotics will appreciate the potential of the elegant and novel approach to the control of networked robots presented here. The limited background required and the case-study work described also make the text appropriate for and, it is hoped, inspiring to students.