Modern robotic platforms for aerospace applications are complex systems characterized by many degrees of freedom. They are assigned with relatively simple tasks, defined at a high-level of abstraction. On the other hand, it is necessary that such tasks are optimized to save resources or increase the utilization of the expensive robotic assets. For such systems, globally-optimal redundancy resolution is a challenge, especially for those with more than one redundancy parameter. Among the leading techniques that are employed to solve the redundant inverse kinematics problem, approaches based on dynamic programming execute more efficiently by breaking the complex problem into various simpler subproblems and demonstrate superior flexibility in the management of constraints and objective functions.