Learning Outcomes:

1. Assess complex kinematic structures using advanced geometric methods.

2. Apply analytical techniques to solve closed-loop kinematic chains with measurable precision.

3. Evaluate singular configurations through explicit mathematical metrics.

4. Optimize kinematic chain design based on iterative simulation feedback.

Learning Targets:

1. Identify and specify the necessary degrees of freedom required for executing complex robotic tasks.

2. Develop detailed CAD models to simulate mechanical designs and assess structural integrity under variable conditions.

3. Select and justify the use of specific materials and actuators based on load, stress, and performance requirements.

4. Integrate kinematic analysis with mechanical design through iterative project-based prototyping and testing.

Modules

Learning Targets:

1. Formulate algorithmic strategies for collision-free path planning using quantitative metrics.

2. Apply techniques such as probabilistic roadmaps and RRT in simulation and laboratory settings.

3. Evaluate the accuracy and efficiency of motion planning algorithms using performance benchmarks and iterative testing.

4. Optimize planning parameters based on hands-on project experiments to achieve smooth and predictable arm movements.

Modules

Learning Targets:

1. Select appropriate sensors for acquiring positional, force, and environmental data.

2. Interface and calibrate sensors with actuators using microcontrollers such as Arduino and Raspberry Pi in practical sessions.

3. Conduct project-based experiments to ensure accurate motion execution and feedback control.

4. Implement and test integration protocols for real-time data acquisition and processing in laboratory environments.

Modules

Learning Targets:

1. Develop and validate mathematical models representing the dynamics of robotic arms.

2. Implement PID control algorithms and adjust tuning parameters to minimize system error.

3. Monitor system responses using data logging tools and iteratively adjust PID gains for optimal performance.

4. Validate control strategies through both simulation software and hands-on experimental setups.

Modules

Learning Targets:

1. Implement image processing algorithms for object detection and recognition using libraries such as OpenCV.

2. Integrate camera systems with robotic arms to provide real-time visual feedback in project labs.

3. Evaluate vision-based control systems under varying lighting and environmental conditions through empirical testing.

4. Optimize computer vision algorithms by comparing simulation results with real-world experiments.

Modules

Learning Targets:

1. Set up and configure ROS environments for controlling and simulating robotic arms.

2. Develop and deploy firmware on Arduino for integrating multiple sensors and actuators.

3. Utilize Raspberry Pi for managing data processing tasks and interfacing with ROS in integrated projects.

4. Debug and troubleshoot cross-platform hardware-software integration issues in practical exercises.

Modules

Learning Targets:

1. Assess feasibility and safety requirements based on industry standards for heavy-load manipulation.

2. Design and simulate scenarios to test load capacity and stress distribution using advanced simulation tools.

3. Analyze case studies and current best practices from industrial and medical robotics projects.

4. Develop risk management protocols and error mitigation strategies in project-based environments.

Modules

Learning Targets:

1. Plan and document project requirements, including technical specifications and risk assessments.

2. Develop detailed design documents and CAD models that outline system architecture and components.

3. Implement hands-on prototyping by assembling sensors, actuators, and microcontrollers using platforms like Arduino and Raspberry Pi.

4. Conduct iterative testing and troubleshooting sessions to refine system performance and reliability.

5. Document and present the complete project lifecycle, including challenges, solutions, and lessons learned.

Modules

Learning Targets:

1. Lead and participate in cross-functional teams to develop integrated robotic solutions.

2. Utilize industry-standard project management tools such as Git and Jira to coordinate team efforts and version control.

3. Present technical findings and project outcomes through clear, data-driven presentations and technical reports.

4. Incorporate peer feedback and conduct iterative design reviews to enhance project performance and innovation.

Modules