**1. Introduction**

Industrial robotics with advanced technology for the industrial automation field has received considerable attention in recent years [1–6]. Although the development and application of industrial robots are confined—to a certain extent—to the field of advanced manufacturing due to the lack of rigidity and precision of an industrial robot compared to a numerical control machine, the industrial robot, as a common and less expensive alternative to the latter, has great advantages and potentials especially in some applications that require low cutting forces, low precision requirements, large-sized complex shaped parts and/or multi-faces machining in one setup. Therefore, industrial robots were extensively invented and introduced into factories for robotic machining and other applications such as spray automation, spot welding and materials handling. Such robotic automation freed humans from heavy and tedious labor and such industries rapidly retooled their manufacturing lines into robot-integrated systems. Consequently, industrial robots offer a real gain of flexibility, modularity, and access for machining on production lines, and are viable solutions for enhanced productivity, quality, and safety. Industrial robotics are being widely adopted in the robotic machining applications and have become an area of significantly in-depth robotics research. More recent research on robotic machining and many diverse research domains have been investigated in recent years [7–14].

Several studies were concentrated on five-degree-of-freedom (five-DOF) hybrid robot manipulators for machining and fabrication applications. A five-DOF hybrid mechanism was developed, which included a synthesized two translational DOF and one rotational DOF (2T1R) parallel module [15]. A five-DOF hybrid mechanism which consists of a 3-DOF parallel platform and a X-Y table [16]. A parallel mechanism (3T1R) and a rotational table were integrated into a five-DOF hybrid robot manipulator [17]. A five-DOF hybrid mechanism was designed which includes a parallel manipulator (2T1R) with a rotational table [18]. A five-DOF hybrid mechanism including a 2-DOF redundant parallel manipulator was developed [19]. A five-DOF hybrid mechanism was investigated, consisting of a 3-DOF parallel manipulator with actuation redundancy and a 2-DOF worktable [20]. A five-DOF hybrid robot was introduced, which comprises of a parallel mechanism (1T2R) and two gantries [21]. Two five-DOF mechanisms were introduced, which are composed of a 3-DOF parallel mechanism connected in series with a 2-DOF wrist [22]. Some of the designs of these five-DOF hybrid robot manipulators are complex, since an application of the inferior-mobility robot manipulator demands a translatable table [15] or a rotatable one [16–18]. Also, the problem of achieving motion control is harder for a hybrid mechanism [19,20] with redundantly actuated joints than a non-redundantly actuated one. Moreover, a relatively small workspace for a robot manipulator is established to perform tasks [22].

Several research works are concentrated on the related technologies of robotic deburring. Practical mechanical deburring methods, including robotics for aluminum work parts and an overview of burr formation mechanism and morphology, were presented, and several deburring classifications were also proposed [23]. Also, a review of burr formation modeling and control, and factors governing milling burr formation were presented [24]. Robotic application of edge deburring with a controlled force progression pneumatic tool for a part of the manufacturing process of an aircraft engine detail was investigated [25]. A flexible robot-based cast iron deburring cell using a single-point laser displacement sensor was developed for small batch production of a robotized deburring task in a standard cast iron foundry scenario [26]. An adaptive robotic system using a custom-developed 3D laser-triangulation profilometer was developed for the robotic deburring of die-cast parts with position and orientation error correction [27]. Through discrete event simulation and 3D digital human modeling, a multi-purpose digital simulation approach was proposed for the sustainability enhancement of a real manufacturing cell of the aerospace industry, automated by robotic deburring [28].

The current research and applications of robotic deburring are mainly carried out by one or more traditional and commercial six-degree-of-freedom (six-DOF) serial industrial robot manipulators, which are not always suitable for the robotic deburring of complex shaped parts with highly dexterous orientations adjustment and efficient multi-faces deburring in one setup because of the defective nature of this kind of serial robot manipulator, e.g., the weakened structural rigidity of the extended robot manipulator when it is reaching far deburring position with a cantilever-beam-like structure, and the poor accessibility and small safety margins between robot manipulator and workpiece resulting from its difficulties in both obstacle avoidance adjustment and deburring orientation adjustment.

Several studies investigating tool path adaptation and process parameter control for robotic deburring were presented. A tool path adaptation for robotic deburring was presented in accordance with the registration using a custom 3D laser-triangulation profilometer to compensate the positioning errors of the currently processed workpiece, which is difficult to ensure repeatable clamping [27]. A tool path modification method based on a computer-aided design model and direct teaching was proposed to compensate for the position/orientation errors of the workpiece, in addition, impedance control was used to avoid applying an excessive contact force and a virtual wall was adopted to improve the force-control performance for the robotic deburring process [29]. An application of a human mimicking control strategy that mimics the human behavior during the manual deburring on the deburring of hard material items using an industrial robot was introduced [30]. By satisfying a set of constraints to properly perform the desired surface contact conditioning, a hybrid position/force control approach using task priority and sliding mode control was proposed for contact-driven robotic surface treatments such as deburring [31,32]. A set of optimal process parameter combination for robotic machining and the effect of process parameters such as spindle speed, feed rate and tool path strategies on the performance characteristics were investigated using the Taguchi–Grey relational approach and analysis of variance [33]. Edge robotic deburring with a controlled force progression pneumatic tool, as well as a methodology used to select and optimize the edge robotic deburring process, was presented [25]. A sliding mode control method based on radial basis function neural network was proposed for the deburring of industry robotic systems, without the requirements for strict constraints, an accurate model and exact parameters [34]. A fuzzy proportional–integral–derivative (PID) control method for deburring industrial robots was proposed and the PID controller parameters can be updated online at each sampling time to allow adaptive compensation for error and guarantee trajectory accuracy of the end-effector [35]. A vision-based approach, a Pythagorean hodograph quintic spline interpolator based on S curve acceleration/deceleration and an integrated process control structure consisting of an adaptive disturbance compensator, a sliding mode controller, and a friction compensator were investigated for force control and contour following in industrial applications such as deburring [36].

These studies of tool path adaptation and process parameter control for robotic deburring are usually conducted on one or more traditional and commercial six-DOF serial industrial robot manipulators. The tool path adaptation for robotic deburring implemented usually needs to supplement extra equipment or processes, e.g., using a vision system described in [27,36] or direct teaching [29]. Other studies on the process parameter control for robotic deburring have certain constraints, e.g., the deburring tool is an abrasive diamond disc in the control strategy for the process parameter control, and other deburring tools are not considered [30]; the designed control action and implementation are more intricate, such as [25,31,32,34,36]; the procedure of the proposed approach is more complicated, such as [33]; or detailed deburring process parameters such as robotic feed and spindle speed for the deburring industrial robot are not considered [35].

The deburring orientation adjustment of current industrial robots is usually not dexterous, especially at the far deburring position while simultaneously considering obstacle avoidance. There is an urgent need to develop a robot manipulator for robotic deburring of complex shaped parts with highly dexterous manipulation and very efficient five-face deburring in one setup. Moreover, there are very few related references for the tool path strategies which are implemented as proprietary software packages of robot manufacturers. It is particularly necessary to conduct the research on the tool path planning which is highly suitable for the deburring characteristics of the self-developed robot manipulator. Also, in order to perform an adaptive deburring process and finer deburring quality, it is very important to investigate an easy-to-implement and efficient process parameter control method with automatic-online errors correction.

The rest of this article is organized as follows. The structure of the robot manipulator and related research are described in Section 2. A robotic deburring tool path planning method and a robotic deburring process parameter control method are proposed for robotic deburring in Sections 3 and 4, respectively. A dexterous manipulation verification experiment is presented in Section 5. Two robotic deburring experiments, disc deburring experiment of an automobile hub and multifaceted edges deburring experiment of an automobile steering booster housing, are conducted in Section 6. Finally, conclusions are drawn in Section 7.
