A pathway from foundations to application
This self-directed pathway organises robotics into four connected stages. It is a subject exploration resource rather than an accredited programme specification. Work through the stages at a pace that allows time for reading, practice and reflection.
1. Kinematics
Focus on kinematics as a way to understand machines that sense, plan and act in physical environments. Build a concise glossary, identify the central models and explain one idea without relying on specialist vocabulary.
Learning activity: select a published example related to kinematics. Identify the question, evidence and conclusion. Reconstruct the reasoning and note one assumption that could change the result.
Portfolio evidence: write a short critical explanation of how kinematics connects with embedded systems. Include an annotated diagram, worked example or source comparison where appropriate.
2. Embedded systems
Focus on embedded systems as a way to understand machines that sense, plan and act in physical environments. Build a concise glossary, identify the central models and explain one idea without relying on specialist vocabulary.
Learning activity: select a published example related to embedded systems. Identify the question, evidence and conclusion. Reconstruct the reasoning and note one assumption that could change the result.
Portfolio evidence: write a short critical explanation of how embedded systems connects with computer vision. Include an annotated diagram, worked example or source comparison where appropriate.
3. Computer vision
Focus on computer vision as a way to understand machines that sense, plan and act in physical environments. Build a concise glossary, identify the central models and explain one idea without relying on specialist vocabulary.
Learning activity: select a published example related to computer vision. Identify the question, evidence and conclusion. Reconstruct the reasoning and note one assumption that could change the result.
Portfolio evidence: write a short critical explanation of how computer vision connects with motion planning. Include an annotated diagram, worked example or source comparison where appropriate.
4. Motion planning
Focus on motion planning as a way to understand machines that sense, plan and act in physical environments. Build a concise glossary, identify the central models and explain one idea without relying on specialist vocabulary.
Learning activity: select a published example related to motion planning. Identify the question, evidence and conclusion. Reconstruct the reasoning and note one assumption that could change the result.
Portfolio evidence: write a short critical explanation of how motion planning connects with kinematics. Include an annotated diagram, worked example or source comparison where appropriate.