Wheel configurations and kinematics are crucial for mobile robots. Different setups like differential drive, Ackermann steering, and omnidirectional wheels offer unique advantages. Understanding forward and inverse kinematics helps determine robot position and required wheel velocities for desired motion.

Control systems are vital for robot motion, employing techniques like PID control and Model Predictive Control. Tracked robots offer unique performance benefits, especially in challenging terrains. Their mechanics involve track tension and grouser design, with mobility metrics like drawbar pull and tractive effort guiding their performance evaluation.

Wheel Configurations and Robot Kinematics

Wheel configurations for mobile robots

• Differential drive employs two independently driven wheels allowing simple design and control but limited stability on uneven terrain
• Ackermann steering utilizes car-like mechanism enabling smooth turning at high speeds with larger turning radius than differential drive
• Omnidirectional wheels facilitate holonomic movement in any direction through complex design and control at higher cost (mecanum wheels)
• Skid steering used in tracked vehicles and some wheeled robots performs well on rough terrain but less precise turning (bulldozers)

Kinematics of wheeled robots

• Forward kinematics determines robot position and orientation from wheel rotations using odometry equations
• Inverse kinematics calculates required wheel velocities for desired robot motion
• Velocity kinematics relates wheel velocities to robot linear and angular velocities
• Dynamic model accounts for mass, inertia, and forces acting on the robot using Newton-Euler equations or Lagrangian mechanics
• Wheel slip and friction models incorporate Coulomb friction model and Pacejka's Magic Formula for tire-ground interaction

Control Systems and Tracked Robots

Control systems for robot motion

• PID control utilizes Proportional, Integral, and Derivative terms with tuning methods (Ziegler-Nichols)
• Model Predictive Control optimizes control inputs over prediction horizon handling constraints explicitly
• Trajectory tracking implements path following algorithms and pure pursuit controller
• Obstacle avoidance employs potential field methods and Vector Field Histogram
• Localization and mapping incorporates Simultaneous Localization and Mapping and Kalman filtering for sensor fusion

Performance of tracked robots

• Tracked robot mechanics involve track tension, sag, and grouser design for traction
• Terrain interaction considers soil mechanics (terramechanics) and Bekker-Wong theory for soil-vehicle interaction
• Mobility metrics include drawbar pull, tractive effort, and motion resistance
• Turning mechanisms utilize skid steering for tracked vehicles with neutral turn capability
• Comparison with wheeled robots shows lower ground pressure for soft terrains, higher traction in loose soil or snow, but increased complexity and maintenance requirements