A task-priority based framework for multiple tasks in highly redundant robots

A task-priority based framework for multiple tasks of highly redundant robots was derived using the Lagrangian multiplier method. The framework was proved to prioritize a generic number of tasks without algorithmic problems - so called an algorithmic singularity and an algorithmic error. The computational efficiency of the framework excels other conventional task-priority strategies. The efficiency and efficacy of the framework was demonstrated theoretically and experimentally through comparative study.