Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 28 hours
Course Outline
Introduction to Multi-Robot Systems
- Overview of coordination and control architectures for multi-robot setups
- Industrial, research, and autonomous system applications
- Distinguishing between centralized and decentralized system architectures
Foundations of Swarm Intelligence
- Core principles of collective intelligence and self-organization
- Biological models: insights from ants, bees, and flocks
- Emergent behaviors and system robustness in swarm contexts
Communication and Coordination Mechanisms
- Models and protocols for inter-robot communication
- Consensus algorithms and distributed agreement processes
- Strategies for task allocation and resource sharing
Control and Formation Approaches
- Leader-follower, behavior-based, and virtual structure control methods
- Algorithms for flocking, coverage, and pursuit–evasion scenarios
- Maintaining formations amidst noisy communication conditions
Swarm Optimization Techniques
- Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)
- Utilization in path planning and dynamic task assignment
- Hybrid methods integrating machine learning with swarm heuristics
Simulation and Practical Implementation
- Constructing multi-robot simulations using ROS 2 and Gazebo
- Developing swarm behaviors via Python or C++
- Debugging and analyzing emergent system dynamics
Advanced Concepts in Swarm Robotics
- Scalability, fault tolerance, and resilience in communication
- Integrating machine learning for adaptive coordination
- Human-swarm interaction and supervisory control mechanisms
Practical Project: Designing and Simulating a Swarm Coordination System
- Establishing objectives and constraints for multi-robot missions
- Implementing specific swarm coordination algorithms
- Assessing performance metrics and system robustness
Conclusion and Recommended Next Steps
Requirements
- A solid command of robotics fundamentals
- Proficiency in Python programming and the ROS framework
- Working knowledge of algorithms used for motion planning and control
Target Audience
- Robotics researchers specializing in distributed and cooperative systems
- System architects developing large-scale multi-agent robotic solutions
- Advanced developers working on autonomous coordination and swarm algorithms
Testimonials (2)
Supply of the materials (virtual machine) to get straight into the excersises, and the explanation of the Ros2 core. Why things work a certain way.
Arjan Bakema
Course - Autonomous Navigation & SLAM with ROS 2
its knowledge and utilization of AI for Robotics in the Future.
