Industrial manipulators often interact with large and complex objects for a variety of automation tasks. Finding a feasible path for the robot end-effector that ensures task success is often non-trivial due to considerations such as reachability, singularity avoidance, and collision avoidance. This dissertation presents an approach to expand the search space for feasible robot trajectories (and search for an optimal solution) around large and complex geometry by taking advantage of task redundancy for certain tasks without compromising task objectives. The effort builds on previous work enabling virtual fixture generation for complex shapes given CAD or scan data. While existing planners found in the literature have successfully implemented redundancy resolution techniques, the focus has mostly been on the execution of fully constrained trajectories by robots with Degrees of Freedom (DOF) greater than the number of task variables. This effort has successfully extended this discussion to address situations in which the nature of the task allows for relaxation of both geometric and non-geometric task variables to enable the exploration of a larger range of feasible solutions. The effort was developed into a trajectory planning library on the ROS (Robot Operating System) framework and tested by simulating an interaction of a six-axis industrial robot with an F-16 aircraft and a conformal Additive Manufacturing (AM) case study. The results show benefits such as increased task surface coverage with minimal robot base placements and greater user control over the trajectory generation since the user can easily identify problematic points in the trajectory and specify parameters to modify them in real-time