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We propose a novel solution to the problem of inverse kinematics for redundant robotic manipulators for the purposes of goal selection for path planning. We unify the calculation of the goal configuration with searching for a path in order to avoid the uncertainties inherent to selecting goal configurations which may be unreachable because they currently lie in components of the free configuration space disconnected from the initial configuration. We adopt workspace heuristic functions that implicitly define goal regions of the configuration space and guide the extension of rapidly-exploring random trees (RRTs), which are used to search for these regions. The algorithm has successfully been used to efficiently plan reaching and grasping motions for a humanoid robot equipped with redundant manipulator arms