co-operative and swarm robots with their applications.pptx

Cooperative and swarm robots are groups of robots that work together to accomplish complex tasks by coordinating their actions and leveraging collective intelligence, drawing inspiration from natural systems like ant colonies or bee swarms; their applications range from environmental monitoring and search and rescue operations to manufacturing and healthcare

1. SHARAD INSTITUTE OF TECHNOLOGY, COLLEGE OF ENGINEERING, YADRAV- ICHALKARANJI (Kolhapur) An ‘A’ Grade Institute Accredited by NAAC NBA Accredited Programmes Prepared by Mr. Ashish A. Desai Assistant Professor Co-operative and Swarm Robots

2. Introduction Cooperative and swarm robotics involve multiple robots working together to achieve common goal. Cooperative Robots collaborate through communication, sharing information, and coordinating movements to complete tasks efficiently. In industrial settings, they optimize production by handling specific tasks on an assembly line. Swarm Robots mimic natural swarm behaviors , relying on decentralized control to exhibit collective intelligence. They adapt dynamically to environments and are used in applications like environmental monitoring, search and rescue, and exploration of hazardous areas.

3. Cooperative Robots Definition : Robots that collaborate, communicate, and coordinate to perform tasks efficiently. Key Features: Centralized or decentralized control. Task allocation and synchronization. Applications: Industrial automation (e.g., assembly lines). Multi-robot exploration and mapping. Medical and assistive robotics.

4. Swarm Robot Definition: Large groups of simple robots working collectively using decentralized control. Key Features: Scalability and redundancy. Simple local interactions lead to complex collective behavior . Applications: Environmental monitoring. Search and rescue missions. Precision agriculture.

5. Cooperative vs. Swarm Robots

6. Cooperative Manipulation:- Cooperative manipulation involves multiple robots working together to handle objects or perform tasks requiring coordination. By synchronizing movements and sharing information, robots enhance their capabilities to manage complex or heavy objects efficiently.

7. This image depicts a human-robot cooperative manipulation system , where a robotic arm ( SoftHand ) assists a human using EMG sensors and optical markers to track muscle activity and movement. A hybrid force/impedance controller processes feedback from the myoelectric and task frame interfaces , ensuring synchronized motion and adaptive force application. The system enables intuitive and precise collaboration for applications in industrial assembly, rehabilitation, and assistive robotics . Cooperative Manipulation:-

8. Review on Multi Robot system in Agriculture

9. Challenges in Cooperative Manipulation Communication & Coordination – Ensuring seamless real-time data exchange between robots for synchronized movements. Uncertainty & Variability – Handling unpredictable environments, object variations, and external disturbances. Control Complexity – Designing adaptive control algorithms for precise force and motion coordination. Human-Robot Interaction – Ensuring intuitive, safe, and efficient collaboration in shared workspaces. Sensing & Feedback – Accurate perception using sensors for object detection, force estimation, and motion tracking. Computational Load – Managing high processing demands for real-time decision-making and control execution. Scalability – Expanding multi-robot cooperation without compromising performance and stability. Energy Efficiency – Optimizing power consumption for prolonged operations in mobile robotic systems. Would you like an expansion on any of these points?

10. Case Study: Collaborative Robots Enhancing Efficiency in Automotive Assembly Background : An automotive manufacturer integrated collaborative robots ( cobots ) into its assembly lines to improve efficiency, precision, and worker safety . Implementation : Cobots assisted workers in tasks like welding, painting, and component assembly. AI-driven sensors enabled real-time adjustments and safe human-robot interaction. Vision systems ensured quality control and defect detection . Results : 30% increase in production efficiency. Reduced worker fatigue and injury risks. Enhanced precision , leading to fewer defects and rework. Conclusion : Collaborative robots optimized operations, improving productivity and safety while maintaining high-quality standards in automotive manufacturing.

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