Designing a wheeled quadruped robot for research (R) involves combining the mobility of wheels with the stability and adaptability of a quadrupedal platform. Below is a structured approach to building such a robot:--- 1. Key Design Considerations- Purpose: Research in locomotion (hybrid wheel-leg movement), terrain adaptability, or payload delivery.- Mobility: Wheels for speed on flat terrain, legs for rough/uneven surfaces.- Actuation: Motors (DC, servo, or BLDC) for wheels + actuators (servos, linear actuators) for legs.- Control: Gait coordination (trot, walk) + wheel synchronization.- Power: High-capacity batteries (LiPo) for extended operation.- Sensors: IMU, encoders, cameras, LiDAR, or force sensors for feedback.--- 2. Mechanical Design Option 1: Wheels Attached to Legs- Design: Each leg ends with a powered wheel (e.g., MIT's "Whegs" concept).- Advantages: Simple control, fast rolling, and step-climbing ability.- Example: - Legs with 2–3 DOF (hip + knee) + wheel at the foot. - Wheels can be passive (free-rolling) or active (driven by motor). Option 2: Retractable Wheels- Design: Wheels fold into the body when not in use (e.g., ETH Zurich's ANYmal with wheels).- Advantages: Pure wheeled mode for efficiency, legs for obstacles.- Challenge: Complex mechanism to deploy/stow wheels. Option 3: Hybrid Spoked Wheels- Design: Wheels with spokes that act as legs (e.g., Boston Dynamics' "Handle" but quadrupedal).- Advantages: No mode switching; spokes adapt to terrain.--- 3. Actuation & Electronics- Motors: - Wheels: DC gear motors or BLDC motors with encoders. - Legs: High-torque servos (e.g., Dynamixel) or linear actuators.- Controller: - ESP32 or STM32 for low-level control. - Raspberry Pi/NVIDIA Jetson for high-level planning (ROS/ROS2).- Sensors: - IMU (MPU6050) for balance. - ToF sensors or ultrasonic sensors for obstacle detection. - Force-sensitive resistors (FSR) on feet for ground contact.--- 4. Software & Control- Gait Engine: Finite-state machine for hybrid locomotion (e.g., trot + rolling). - ROS Integration: Use ROS Control for motor feedback and Gazebo for simulation. - Algorithms: - Inverse kinematics for leg positioning. - PID control for wheel speed synchronization. - Terrain adaptation using sensor fusion.--- 5. Example Research Applications1. Multi-modal Locomotion: Study energy efficiency of wheels vs. legs. 2. Obstacle Negotiation: Compare wheeled traversal vs. stepping over gaps. 3. Payload Transport: Test stability when carrying loads on uneven terrain. --- 6. Open-Source Platforms to Build On- SpotMicro (add wheels to legs). - Stanford Doggo (modify for hybrid motion). - Open Dynamic Robot Initiative (low-cost torque-controlled legs).--- 7. Challenges to Address- Weight Distribution: Wheels may raise the center of gravity. - Complex Control: Switching between wheeled and legged modes smoothly. - Power Consumption: Activating both systems drains batteries faster.--- Next Steps1. Simulate: Test designs in PyBullet or Webots. 2. Prototype: Start with a 3D-printed small-scale model. 3. Iterate: Optimize for speed, stability, and energy use. Would you like help with specific aspects (e.g., CAD, motor selection, or ROS code)?