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.
Course Outline
Introduction to Multi-Robot Systems
- Overview of coordination and control architectures for multi-robot setups
- Practical applications in industry, research, and autonomous systems
- Analyzing the differences between centralized and decentralized systems
Fundamentals of Swarm Intelligence
- Key principles of collective intelligence and self-organization
- Bio-inspired models: ants, bees, and flocks
- Emergent behaviors and robustness characteristics in swarm systems
Communication and Coordination
- Models and protocols for inter-robot communication
- Consensus algorithms and mechanisms for distributed agreement
- Strategies for task allocation and resource sharing
Control and Formation Strategies
- Control methods: leader-follower, behavior-based, and virtual structure
- Algorithms for flocking, coverage, and pursuit–evasion
- Maintaining formation amidst noisy communication conditions
Swarm Optimization Algorithms
- Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)
- Use cases in path planning and dynamic task assignment
- Hybrid methods integrating learning with swarm heuristics
Simulation and Implementation
- Developing multi-robot simulations within ROS 2 and Gazebo
- Coding swarm behaviors using Python or C++
- Debugging processes and analyzing emergent dynamics
Advanced Topics in Swarm Robotics
- Addressing scalability, fault tolerance, and communication resilience
- Integrating machine learning for adaptive coordination
- Human-swarm interaction and supervisory control mechanisms
Practical Project: Designing and Simulating a Swarm Coordination System
- Setting objectives and constraints for multi-robot missions
- Developing and implementing swarm coordination algorithms
- Assessing performance metrics and system robustness
Summary and Future Directions
Requirements
- A solid grasp of robotics fundamentals
- Proficiency in Python programming and ROS
- Knowledge of algorithms related to 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 focused on autonomous coordination and swarm algorithms
28 Hours
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.