Develop a deep multimodal AI system for indoor navigation in Webots.
Budget: $250 – $750 CAD
I'm seeking an expert in robot navigation and Deep multimodal AI using the Webots simulator (Pioneer 3 DX Robot) for the following:
Key Requirements:
- Integration of multiple sensors.
- Implementation of Deep Reinforcement Learning (e.g., Deep Q-Networks).
- Attention mechanisms are used to filter sensor data.
- Robust navigation in a dynamic indoor environment.
Expected Deliverables:
- A complete Webots project file.
- Documented source code with explanations.
- Performance analysis and validation.
- A report or paper explaining the methodology.
Testing and Simulation:
- The robot should navigate using a combination of cameras, LIDAR, and ultrasonic sensors.
- The simulation should emulate the complexities of an actual office space.
Ideal Skills:
- Proficiency in Webots simulation
- Strong background in robot indoor navigation and multimodal systems
- Experience with LIDAR, Ultrasonic sensors, and camera integration.
Please include examples of similar projects you've worked on in your bid.
Key Requirements:
- Integration of multiple sensors.
- Implementation of Deep Reinforcement Learning (e.g., Deep Q-Networks).
- Attention mechanisms are used to filter sensor data.
- Robust navigation in a dynamic indoor environment.
Expected Deliverables:
- A complete Webots project file.
- Documented source code with explanations.
- Performance analysis and validation.
- A report or paper explaining the methodology.
Testing and Simulation:
- The robot should navigate using a combination of cameras, LIDAR, and ultrasonic sensors.
- The simulation should emulate the complexities of an actual office space.
Ideal Skills:
- Proficiency in Webots simulation
- Strong background in robot indoor navigation and multimodal systems
- Experience with LIDAR, Ultrasonic sensors, and camera integration.
Please include examples of similar projects you've worked on in your bid.