For highly customized manufacturing models, using offline programming to generate processing trajectories for a small number of workpieces and manually adjusting trajectories (which requires 3-8-h per workpiece) is a time-consuming process. The trajectories are also difficult to implement. Therefore, this study proposed an automatic deburring trajectory generation technique to achieve automatic adjustments and reduce operation time. During burr detection, curve fitting is performed using a computer-aided design (CAD) model of the target workpiece, and the curve information is obtained using linear contour scanning sensors, from which boundary contour curve equations are derived. The equations are used to detect burr distribution and generate processing point information, rapidly correcting processing trajectories to adapt to workpiece dimensional deviations and compensate for dynamic trajectory errors, thereby substantially enhancing the manufacturing flexibility of automatic deburring systems. Many studies related to burr detection and online trajectory generation methods have been conducted. Most of these studies have applied pattern matching [7; 8; 9; 10; 11; 12], force-sensing [13; 14], stripe detection [15; 16], edge recognition [17; 18], and other [19; 20; 21] methods. When pattern matching is performed, the workpiece must be completely scanned to generate the processing trajectory because trajectory tracking for dynamic absolute error correction cannot be achieved during processing. In a force-sensing operation, force correction based on the processing situation can be conducted only after the processing trajectory is generated; therefore, the processing operation cannot be performed accurately when the workpiece size deviation is large (e.g., in the case of castings). Although the processing trajectory can be generated during processing in stripe detection and edge recognition, these methods can be used for burr detection only on edges and not curved surface areas (e.g., parting lines, gates, and risers) or irregular and discontinuous areas. Accordingly, existing trajectory generation methods cannot be effectively applied for burr removal after various processing procedures are performed. To address the aforementioned problems, an automated method was developed in this study for burr detection and deburring trajectory generation. This method enables online burr detection and deburring trajectory point extraction; that is, the dynamic accuracy of robotic arms and the workpiece size deviation can be compensated through burr tracking. Thus, the developed method solves the drawbacks of (and requires less processing time than do) current burr detection and online trajectory generation methods. Although many burr detection and online trajectory generation method-related studies have been conducted, most have used methods involving pattern matching [3; 4; 5; 6; 7; 8], force-sensing [9; 10], stripe detection [11; 12], edge recognition [13; 14], and closed-loop compensation (with 3D measurement equipment) [15]. These methods generally can be applied only to detect burrs on edges and cannot be used to detect burrs on curved surface areas such as parting lines, gates, and risers or burrs that are irregular and discontinuous. To address this shortcoming, this study introduced a burr detection and automatic deburring trajectory generation method that enables burr trajectory tracking to solve the problems of low robotic arm dynamic accuracy and workpiece dimensional deviations. This paper is organized as follows. Section 1 introduces the motivation and contribution of this study and the shortcomings of existing methods. Section 2 describes the proposed process for online 3D trajectory generation in deburring processing. This process involves extracting workpiece section contours and obtaining boundary equations by using laser contour scanning sensors to generate the deburring trajectory. Section 3 presents the results obtained in this study. Finally, Section 4 provides the conclusions of this study.