Synopsis
Explore how a Jacobian-transpose servo algorithm enables precise Cartesian control in a multiprocessing robot system.
This work outlines the architecture, algorithms, and practical demonstrations of implementing a Cartesian PD controller with gravity compensation. It covers the control levels, task decomposition, and how software processes can be distributed across processors to run complex robot tasks without relying on full inverse kinematics.
The material emphasizes real-world applications, including stable contact with stiff environments and catching a rolling ping-pong ball. It also explains how gains and planning strategies affect motion, accuracy, and safety in a robotics context. Readers will see how end effector forces relate to Cartesian errors and how dynamic behavior is managed in a multi-process control scheme.- How Cartesian control is achieved without explicit inverse kinematics
- How the system is structured for multiprocessing and processor distribution
- Two practical demonstrations: stable contact motion and ball catching
- Gain scheduling and trajectory options for general-purpose motions
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