RubbleBug

Circuit
CAD of leg
Inverse Kinematics
Working on our project
GIF
Leg moving
final hexapod!!

Inspiration

Disasters can happen at any time. An earthquake or flood can leave a neighborhood in ruins. When a catastrophe like this happens, you need a hexapod to search for things in the ruin and reach those small, narrow, hard-to-reach areas during search and rescue operations.

What it does

Our hexapod is a six-legged robot intended for use in disaster scenarios. Once fully operational, it would help first responders quickly scan hazardous zones without putting human lives at additional risk.

How we built it

We started by creating detailed 3D models of the robot’s legs—tibia, femur, and coxa—using OnShape. We also designed a central body plate to house our microcontrollers. After finalizing these CAD designs, we 3D-printed each component. Then, we moved on to the electronics. We built and tested circuits following our schematic, wiring three servos to each Arduino for a total of 18 servos across six legs. In the Arduino IDE, we wrote the control code in C++, focusing heavily on inverse kinematics to calculate the precise angles needed for each leg’s movement.

Challenges we ran into

After getting one leg fully operational, it was pretty straightforward to get two working at the same time. However, when wiring up the third leg, we faced a major roadblock: our Arduino IDE crashed when we tried to upload code for the rest of the legs. Despite trying multiple computers, USB ports, and reinstalls, we couldn’t get the remaining four legs to function. This unexpected technical issue ate into our limited build time and halted progress on achieving a fully walking hexapod.

Accomplishments that we're proud of

Detailed 3D Models: We successfully designed and printed complex leg components, ensuring a robust mechanical structure. Inverse Kinematics Implementation: We managed to solve the math behind precise servo angles, allowing our single leg to move smoothly to specified coordinates. Functional Prototypes: Getting two legs to work confirmed that our approach to leg control and structure was valid before the technical issues arose.

What we learned

Time Management & Planning: 3D printing takes longer than expected, and planning for potential errors is crucial. Inverse Kinematics: Diving deep into the math behind leg motion sharpened our problem-solving and coding skills. Troubleshooting Tools: When software fails, having alternative approaches and backups is key to avoiding stalls.