The research goal of this study is to investigate the use of sensor feedback for robot path planning and optimization. Robots are ideal automation tools for handling highly repetitive manufacturing tasks with unparalleled accuracy and agility. Manufacturing tasks that can be handled by robots include polishing, material handling, assembly, inspection, etc. The study uses ABB Robotstudio simulation software and an existing collaborative robot equipped with a conventionally available 3D machine vision sensor. Regular digital cameras capture two-dimensional images. 3D vision sensors can capture three-dimensional images with the depth perception. The research findings are expected to enhance the domain knowledge and make industrial robots more collaborative and intelligent with advanced sensor feedback. The approach involves two scholarly tasks in achieving the research goal.The first task is to investigate robot path planning for a painting application with the use of the ABB Robotstudio simulation software. The aim of the path planning is to cover the entire surface of a flat panel evenly. For the simulation purposes, a flat panel may contain arbitrary pockets and/or holes which shouldn’t be painted. The simulation uses line sensors that assimilate proximity sensors for detecting the geometry of the flat panel so that the robot path can be optimized in accordance with the workpiece geometry. The second task is to investigate path planning methods along with the machine vision feedback for the same robotic painting application. A UR-10e robot and a SICK ranger camera are used for the second task. A Canny edge detection algorithm is studied in conjunction with identifying the workpiece geometry.