Automatic Tool Path Planning Strategy Based on Feature Extraction of 3D Models

Path planning is essential for mobile robots to operate efficiently in uncertain environments. As the number of robots increases, centralised approaches struggle to provide feasible solutions within real-time constraints. To address this, an adaptive hybrid strategy-based decentralised path planning algorithm is proposed to solve the real-time path planning problem through decentralised computing. First, various path planning strategies are introduced, including a neural computing planning strategy, a dynamic search planning strategy, and a cluster coordination planning strategy. Then, an intelligent strategy selection mechanism with an adaptive strategy adjustment factor is designed, allowing each robot to dynamically select optimal planning strategies based on their current state and ensuring the planned paths exhibit greater flexibility and adaptability. Finally, the results of the ablation experiment indicate that all three strategies effectively enhance the navigation capabilities of robots in a decentralised mode. The algorithm comparison experiment demonstrates that the proposed algorithm achieves a higher task completion rate and a lower detour percentage in various environments. The decision response experiment shows that our approach has an average decision-making time of approximately 60 ms, which meets the real-time requirements for decentralised path planning of mobile robots in most scenarios.

Difficult-to-machine material thin-walled parts with curved surface are widely used in industrial applications, and the shape accuracy is a basic requirement for ensuring the usability. Due to the low rigidity of the thin-walled curved surface parts, the cutting force becomes a sensitive factor for the machining deformation. In addition, high speed milling, that has an obvious attribute of small cutting force comparing with the traditional one, provides an effective way to process the thin-walled curved surface parts made by difficult-to-machine materials like titanium alloy. Moreover, the rigidity of the thin-walled curved surface parts is constantly changing along with the machining process, which leads to a more complex machining deformation when choosing different tool paths and affects the machining quality. To reduce the machining deformation, a proper cutting parameters combination which influences the machining deformation directly is obtained based on the established cutting force model, and then a deformation control strategy by planning tool path is put forward. At the same time, an efficient compensation method based on modifying cutter location point is proposed. Taking TC4 thin-walled arc-shaped parts as an example, experimental studies indicate that the largest deformation values reduce to 49 μm after compensation. Compared with the former 104 μm, the deformation degree decreases by 52.88 % when the thickness of the thin wall is 200 μm. The research provides an effective approach to reduce the machining deformation induced error for difficult-to-machine material thin-walled parts with curved surface.

By analysis of the validity of tool path, tool path planning is divided into two processes, the choice of curves on surface and the design of effective and rational path arrangement. On this basis, a spiral topology toolpath for continuous cutfing is developed in order to reduce the number of interruptions and rapid traversals during high-speed machining (HSM) process. Based on the isoparametric toolpath, the contour-parallel isoparametric path is firstly created, and then a spiral path is generated as a kind of diagonal curve between the isoparametric offset curves. Experimental results demonstrate that the proposed method is straightforward and avoids the self-intersection of the toolpath, especially suitable for HSM.

ABSTRACTWhile 5-axis CNC machines improve the manufacturing productivity, the tool-path programming demands human expertise and tremendous time investment to generate collision-free optimal tool trajectories. To address the challenge of multi-axis CNC programming, this work presents a new discrete volume based geometry representation that naturally enables highly parallel geometry processing. Then it formulates and discusses a methodology for tool path planning algorithms designed around the formulated geometric representation for 5-axis CNC milling machines and ball end tools. This paper is the first of two papers that deals with finding the XYZ locations of ball end mill centers that govern the tool trajectories. The experimental 5-axis machining using the resulting tool paths demonstrates that the designed algorithms are capable of producing collision- and gouge- free tool paths with a desired surface quality for a given target geometry by using the selected ball end tools.

This paper proposes a novel computation method for tool path planning in 5-axis flank milling of ruled surfaces. This method converts the path planning (a geometry problem) into a curve matching task (a mathematical programming problem). Discrete dynamic programming techniques are applied to obtain the optimal matching with machining error as the objective function. Each matching line corresponds to a cutter location and the tool orientation at the location. An approximating method based on z-buffer is developed for a quick estimation of the error. A set of parameters is allowed to vary in the optimization, thus generating the optimal tool paths in different conditions. They reveal useful insights into design of the tool motion pattern with respect to the surface geometry. The simulation result of machining different surfaces validates the proposed method. This work provides an effective systematic approach to precise error control in 5-axis flank milling.

Deterministic optics fabrication using sub-aperture tools has been vital for manufacturing precision optical surfaces. The fabrication process requires the tool influence function and the tool path to calculate the dwell time that guides the tool to bring surface quality within tight design tolerances. Widely used spiral and raster paths may leave excess waviness from the tool path, and the unavoidable constant removal layer is added to obtain positive dwell time. This waviness can be removed by either using smaller tools sequentially or randomizing the tool path. However, the existing tool-path solutions can hardly adapt to different surface aperture shapes and localized surface errors. Process efficiency and accuracy are also not well considered in tool-path planning. We propose an innovative zonal Random Adaptive Path (RAP) to solve these problems in this study. Firstly, RAP can be flexibly adapted to different surface aperture shapes by introducing part boundary. Secondly, an average threshold strategy is used in the RAP planning to improve efficiency, enabling the surface errors to be selectively corrected. Finally, the threshold is performed in several passes within one processing cycle, each with its RAP, until the desired residual is achieved. The performance of the proposed RAP is studied by comparing it with the conventional tool paths. The results demonstrated that RAP takes the least processing time and achieves the best surface quality, which verifies the effectiveness of RAP in deterministic optics fabrication.

Tool path generation is a fundamental problem in 5-axis CNC machining, which consists of tool orientation planning and cutter-contact (CC) point planning. The planning strategy highly depends on the type of tool cutters. For ball-end cutters, the tool orientation and CC point location can be planned separately; while for flat end cutters, the two are highly dependent on each other. This paper generates a smooth tool path of workpiece surfaces for flat end mills from two stages: Computing smooth tool orientations on the surface without gouging and collisions and then designing the CC point path. By solving the tool posture optimization problem the authors achieve both the path smoothness and the machining efficiency. Experimental results are provided to show the effectiveness of the method.

Multi-axis machining of a complex surface is not only efficient but also reduces workpiece set-up errors. However, engineers usually rely on experience when determining set-up orientation and often cannot finish complex surfaces in one set-up. In this paper, a rational evaluation method for workpiece orientation and configuration of machine tool before tool path planning is proposed to increase the efficiency and reliability of process planning. By incorporating the visibility cone into workspace analysis, a software module is constructed in this research for optimum workpiece orientation and configuration of machine tool. First, the configurations of three-, four- and five-axis machine tools were synthesised, and the workspace was then analysed using form-shaping function. Both position vector and normal vector of each point along the u and v directions in the machining surface were employed to calculate the visibility range as the point of the visibility cone. In 3-axis machining, all normal vectors of the surface were projected on a unit sphere. All normal vectors were ensured to be within the visibility range of the ball-mill cutter as the workpiece set-up orientation changed. In 4-axis machining, the intersection between the visibility cone of each point and the workspace was obtained by changing the set-up orientation. In 5-axis machining, according to different workspaces of 5-axis machine tools, the visibility range of the machining surface was determined to yield the intersection between the visibility cone and the workspace by changing the workpiece orientation. The proposed method for evaluating manufacturability has been demonstrated to generate optimum workpiece set-up orientation and the suitable configuration of machine tools. Tool interference with workpiece surface can be avoided, which can help improve the planning of tool path.

With the increase of on-orbit maintenance and support requirements, the application of a space manipulator is becoming more promising. In actual operation, the strong coupling of the free-floating space robot itself and the unknown disturbance of the contact target caused a major challenge to the robot base posture control. Traditional Reaction Null Space (RNS) motion planning and control methods require the construction of precise dynamic models, which is impossible in reality. In order to solve this problem, this paper proposes a new Adaptive Reaction Null Space (ARNS) path planning and control strategy for the contact of free-floating space robots with unknown targets. The ARNS path planning strategy is constructed by the Variable Forgetting Factor Recursive Least Squares (VFF–RLS) algorithm. At the same time, a robust adaptive control strategy based on the Strategy Self-Adaption Differential Evolution–Extreme Learning Machine (SSADE–ELM) algorithm is proposed to track the dynamic changes of the planned path. The algorithm enables us to intelligently learn and compensate for the unknown disturbance. Then, this paper constructs a robust controller to compensate model uncertainty. A striking feature of the proposed strategy is that it does not require an accurate system model or any information about unknown attributes. This design can dynamically implement RNS path tracking performance. Finally, through simulation and experiment, the proposed algorithm is compared with the existing methods to prove its effectiveness and superiority.

The study deals with automatic path planning method for cutting operation using an industrial robot. In general, studies of motion planning for industrial robots are divided in two approaches, considering a robot configuration or a tool configuration. However, in case of cutting operation using an ultrasonic cutter, generation of optimal tool path becomes difficult, because it is required consideration of continuous tool posture, wide movable range of robot, and high tool feed speed. In this study, we propose the use of an original local configuration space that generated by two angles that determine tool posture at each cutting point, and generate tool path using an A-star search algorism based on the evaluations of interference, movable range and mobility of robot that varied according to relationship of tool feed speed and angular speed of each joint. As a result, it is found that the proposed path generation method is more effective than conventional methods.

Introduction: With the development of computer technology and data modeling, the use of point cloud models to generate tool paths is particularly important for improving productivity and accuracy.Methods: This study proposes a new method that first preprocesses the point cloud data using four-point denoising and octree methods to improve processing efficiency. Subsequently, roughing tool paths were analyzed using the layer slicing method and finishing paths using the residual height method.Results and Discussion: The experimental results show that the layer slicing method has a minimum error close to 10% on the roughing path generation and the computation time is reduced to 35 s, while the residual height method has an error rate of 10.17% on the finishing path and the computation time is only 11.82 s, which reflects a high trajectory smoothness and accuracy. The above results show that the study not only optimizes the tool path generation process and improves the machining efficiency and accuracy, but also demonstrates the potential application of point cloud models in the machining of complex parts.Conclusion: The novel tool roughing and finishing methods provide more reliable path planning for actual machining operations, and future research will be devoted to further improving the performance of the data processing algorithms and exploring more efficient path planning strategies to facilitate automated production.

This paper reports on the development of a collision capable omni-driven platform called Omnipuck with an accompanying reflection model capturing uncertainty present after collision events. Path planning and navigation strategies focus almost entirely on avoiding collisions, but this platform was designed to specifically benefit by colliding with obstacles in the environment. Collisions are modeled as partially reflected diffusions characterizing boundary interaction behaviors including uncertainty with experimentally validated parameters. Experiments highlighting convergence time demonstrate the potential benefits of using boundaries to partially recover impact energy in planning. The results herein lay the foundation for path planning and navigation strategies that exploit collisions.

A robotic simulation and off-line programming package has been developed for use on Silicon Graphics workstations that has the capability of using several types commercially available virtual reality (VR) hardware devices. The software includes a library of 4, 5, and 6 degree-of-freedom (DOF) industrial robots with closed form inverse kinematics and translators that convert graphical simulation commands into the device control language that can be transferred to the robot control hardware. Forward kinematics, inverse kinematics, and joint space path planning methods used in the software development are discussed. The performance of three types of commercially available 6-DOF input devices are compared for use in interactive off-line programming. Stereo vision and external 6-DOF positioning devices were added to enhance programming efficiency and offer more realistic access to the virtual workcell environment.

This paper addresses the task assignment and path planning problem of multiple UAVs. As one of the strategies for a SEAD(Suppressions of Enemy Air Defense) mission, an efficiency strategy for assignment and path planning of homogeneous UAVs is developed. There are many path planning methods (e.g. potential function method or probabilistic roadmap method) to avoid obstacles, but the Voronoi diagram is used for the path planning with avoiding the “No fly zones” in this study. Since several candidate-paths are needed to solve the problem of timing constraints, the flyable paths in the shape of net is generated by using Voronoi diagram and multiple paths are found as the candidate paths from each UAV to each target. The objective function is determined as accumulated sum of the required time to finish the tasks on each target and the optimization is achieved by using genetic algorithm with appropriate representation of chromosome and genetic operators.

Spray forming is an emerging manufacturing process. The automated tool planning for this process is a nontrivial problem, especially for geometry-complicated parts consisting of multiple freeform surfaces. Existing tool planning approaches are not able to deal with this kind of compound surfaces. This paper proposes a tool path planning approach which considers the tool motion performance and the thickness uniformity. There are two steps in this approach. The first step partitions the part surface into flat patches based on its topology and normal directions. The second step determines the tool movement patterns and the sweeping directions for each flat patch. Based on that, optimal tool paths can be calculated. Experimental tests are carried out on automotive body parts and the results validate the proposed approach.