  Trajectory generation in confined environment is crucial for wide adoption of
intelligent robot manipulators. In this paper, we propose a novel motion
planning approach for redundant robot arms that uses a hybrid optimization
framework to search for optimal trajectories in both the configuration space
and null space, generating high-quality trajectories that satisfy task
constraints and collision avoidance constraints, while also avoiding local
optima for incremental planners. Our approach is evaluated in an onsite
polishing scenario with various robot and workpiece configurations,
demonstrating significant improvements in trajectory quality compared to
existing methods. The proposed approach has the potential for broad
applications in industrial tasks involving redundant robot arms.
