K10 and maqueen Combo robot!

I was given funds to purchase a set of 8 robot rover combo’s. They use Unihiker K10’s attached to the edge connectors of Maqueen Robot v2’s. This combination is a great match because the newest types of ai interactions are brought to life in a solid robotics platform and the use of two DF Robot products, combined with DF robot graphic coding software (mind +) means that there is lots of support and documentation available on one website through tutorials and community support. 

This lesson plan also includes notes from a session where I delivered the robots to teachers at a technology conference. Teachers are not the intended audience of the plan but still “student specific” behaviours and challenges occurred and were noted. 

Lesson Plan:

AI in the classroom: voice controlled robotics as a guide to purposeful code revision

Grade 8-10 (one hour)

Tools: k10 robots on Maqueen robotic platform, computers with mind + installed for code revision, pre-made voice command code.

Format: AAAA lesson (activate, acquire, apply, and assess)

Focus:

Using the combined tools (which I call the Nova Cortex Rover Robot) to explore practical competitive use of voice command code to drive a robot to a target.

Objectives: 

Understand voice control in robots

Iterative code revision

Explore ai as a tool and a mechanism

Activate:

Discuss: 

Two layers of language in coding (voice prompts and the code to react to voice prompts)

AI as a robotics tool to elevate human robotics interfaces. (Going beyond the button-sensor model to explore the command-action model) 

I also demonstrated graphic coding with mind + at this point and showed how new code could be written for voice commands. 

Robot demo:

Show participants all the commands with the wake word “Jarvis”

-forward, backward, turn right, turn left, stop. 

Note: during the demo, sometimes actions would not fire as planned or the delay would lead to the robot moving too late to be perfect. I explored these flaws and highlighted that they are universal so no racer would have an advantage. 

Acquire

Participants become the racers. Each is given a pre-coded voice command rover. The targets (I used wood blocks) are distributed equal distances from the participant starting area. 

They use voice commands to race to the targets, competing to be first to arrive and also most targets reached.

Note: during the race, voice commands were effective for some teams but others forgot the wake word and unfortunately, some people were not able to articulate the commands because of accents or voice variations. I also observed some instances that could be called cheating (repositioning the robot by hand, and even voice commanding another team’s robot to throw them off course). 

Fairness and ai ethnic bias were topics in the discussion afterwards. 

Apply:

After discussing how the robot race went, participants were prompted to discuss how the commands could be improved. 

Note: One team suggested that different commands could be helpful so another team could not accidentally (or purposely) control their robot. Some teams wanted more specific coding to define turning and movement. We also discussed autonomous obstacle avoidance but I felt it was outside the scope of this lesson.

After discussion, teams were prompted to try to implement their code improvements. I supported them as they tried to write graphic code, using my code as a starting point. 

Assessment:

I used a reflection frame to allow participants to synthesize and present their learning. 

Questions in the frame:

How did your iteration improve the capabilities of the robot? 

Did AI move us from sensor-button to command-action robotic coding? 

How much impact could human error and human skill have on the race outcomes?

Rubric:

Students think about and discuss ai and robotics trends and challenges 1-4

1- the student did not engage in discussion or demonstrate contemplation about ai

4-the student actively engaged in conversation about ai and presented deeper thinking about trends and challenges 

Students predict code changes and their effectiveness to reaching a goal 1-4

1-the student did not refine the code

4-the student ‘s code effectively improved to robot’s functionality as predicted.

Students demonstrate a deeper understanding of language layers in robotics and coding 1-4

1-no demonstration of understanding how language layers impact robots

4-language layers are well understood and shown through new commands and code complexity to respond to these commands

Conclusion:

The toolkit that combines unihiker k10 as native language recognition, maqueen as a robotics platform, and code from mind+ made this into an engaging lesson that integrated ai and coding. 

Like many lessons, the interesting work and learning occurred in the places it broke down and in the reflections and conversations that followed.

The lesson touched on ai as a tool, robot and human interaction, coding and revision, language, and finally ethics and equity. It is an engaging lesson and an effective introduction to new ideas in language, robotics, ai, and coding. It also is a springboard to follow-up on autonomy and utility in robotics design, code, and engineering.