Path Generation Research Articles

Automated Weld Path Generation in Cluttered Environments using Segmentation and Iterative Closest Point Workpiece Localization

Abstract A significant amount of manufacturing is performed by small to medium enterprises (SMEs), but these manufacturers often have lower adoption rates of automation. The cost and complexity associated with traditional robot systems slows the adoption of robotic welding operations for SMEs. The recent increase in collaborative robot (cobot) welding systems is bridging the gap however, by reducing the complexity of installing, maintaining and training operators to perform weld operations with cobots. These systems add flexibility in range of operator use and ease of deployment. These systems however still rely on kinematic registration between the robot-mounted torch and workpiece on any open-loop weld. This requires precise placement of the workpiece prior to preforming a weld. This work will discuss a method for part identification and registration in a welding task as a step toward automated weld path generation. The method is based on lower-resolution 3D cameras (RGBD cameras) using a combination of color and depth information. This information is used to both identify the workpiece within a workspace that may have other, non-workpiece items, and then provide registration or localization information of the workpiece within a resolution that could allow follow-on near-position strategies to achieve final weld-path identification.

Efficient path planning for autonomous vehicles based on RRT* with variable probability strategy and artificial potential field approach.

With the rapid development of autonomous driving technology, path planning has gained significant attention as it holds great potential for improving safety. The Rapidly-exploring Random Tree star(RRT*) algorithm has attracted much attention because of its good adaptability and expansibility. However, how solving problems in the RRT* algorithm such as slow convergence time, significant search range randomness, and unpredictability is a challenge. Therefore, an RRT* enhancement algorithm combining variable probability goal-bias strategy and artificial potential field(APF) method(Improved A-RRT*) is proposed in this paper. Firstly, the variable probability goal-bias strategy is introduced in the sampling process to make random tree expand towards the target direction and improve the directional searchability of the random tree. Secondly, the potential field function in APF is improved to prevent falling into local optimum problems during path generation. Thirdly, improved APF is combined with RRT*, the target generates a gravitational field on random tree, and the obstacle generates a repulsive force on it, leading random tree to grow toward the target region. Finally, the proposed algorithm is compared with RRT* algorithm and its derivative algorithm. The experimental results demonstrate that the proposed algorithm has obvious optimizations in convergence speed and path quality.

Collaborative Optimization Framework for Coupled Power and Transportation Energy Systems Incorporating Integrated Demand Responses and Electric Vehicle Battery State-of-Charge

The growing adoption of electric vehicles (EVs) and advancements in dynamic wireless charging (DWC) technology have strengthened the interdependence between power distribution networks (PDNs) and electrified transportation networks (ETNs), leading to the emergence of coupled power and transportation energy systems (CPTESs). This development introduces new challenges, particularly as DWC technology shifts EV charging demand from residential plug-in charging to charging-while-driving during commuting hours, causing simultaneous congestion in both ETNs and PDNs during peak times. The present work addresses this issue by developing a collaborative optimization framework for CPTESs that incorporates integrated demand responses (IDRs) and EVs battery state-of-charge (SOC). In the ETN, a multiperiod traffic assignment model with time-shiftable traffic demands (MTA-TSTD) is established to optimize travelers’ routes and departure times while capturing traffic flow distribution. Meanwhile, effective path generation models with EVs battery SOC are proposed to optimize charging energy during driving and construct the effective path sets for MTA-TSTD. In the PDN, a multiperiod optimal power flow model with time-shiftable power demands (MOPF-TSPD) is formulated to schedule local generators and flexible power demands while calculating the power flow distribution. To enhance temporal and spatial coordination in CPTESs, a distributed coordinated operation model considering IDRs is proposed, aiming to optimize energy consumption, alleviate congestion, and ensure system safety. Finally, an adaptive effective path generation algorithm and an ETN–PDN interaction algorithm are devised to efficiently solve these models. Numerical results on two test systems validate the effectiveness of the proposed models and algorithms.

Boosting the Performance of Alias-Aware IFDS Analysis with CFL-Based Environment Transformers

The IFDS algorithm is pivotal in solving field-sensitive data-flow problems. However, its conventional use of access paths for field sensitivity leads to the generation of a large number of data-flow facts. This causes scalability challenges in larger programs, limiting its practical application in extensive codebases. In response, we propose a new field-sensitive technique that reinterprets the generation of access paths as a Context-Free Language (CFL) for field-sensitivity and formulates it as an IDE problem. This approach significantly reduces the number of data-flow facts generated and handled during the analysis, which is a major factor in performance degradation. To demonstrate the effectiveness of this approach, we developed a taint analysis tool, IDEDroid, in the IFDS/IDE framework. IDEDroid outperforms FlowDroid, an established IFDS-based taint analysis tool, in the analysis of 24 major Android apps while improving its precision (guaranteed theoretically). The speed improvement ranges from 2.1× to 2,368.4×, averaging at 222.0×, with precision gains reaching up to 20.0% (in terms of false positives reduced). This performance indicates that IDEDroid is substantially more effective in detecting information-flow leaks, making it a potentially superior tool for mobile app vetting in the market.

Steel Plate Cutting Path Optimisation Problem Based on Euler's Theorem and Greedy Algorithm

Steel plate cutting as one of the key technologies in the field of machining, planning the optimal path and reducing the air distance can improve the cutting efficiency of the steel plate, and improving the cutting efficiency is the most critical step in the machining process. In this paper, we analyze the main reasons for the generation of empty paths and make the assumption of cutting the whole part at once while cutting the part. On this basis, Euler's theorem and greedy algorithm are used to find the proximal point of the cutting path from the outer contour to the inner contour, and to plan the optimal path and the resulting shortest air distance for a given cutting of the outer contour of the serrated steel plate as well as the cutting of different shapes of parts in the inner part of the serrated steel plate. The resulting shortest air travel distance is 31.5536 by matlab.

An automatic generation approach of process model based on feature knowledge and geometric modeling

Traditional process planning applies knowledge to convert design information within the part model into process information. This information is then displayed by manually drawing two-dimensional (2D) process cards from multiple views, illustrating the geometric design and the process details. However, this method has revealed several issues, including poor efficiency, information misinterpretation, and the potential for manual drawing errors. This traditional approach hampers the integration of manufacturing information across engineering software. It exacerbates the digital divide between computer-aided design (CAD), computer-aided process planning (CAPP), and computer-aided manufacturing (CAM). To address these issues, this study proposes an automated approach for creating three-dimensional (3D) process models based on feature knowledge and geometric modeling. The 3-axis milling features are recognized as the machining objects from the boundary representation (B-Rep) part model. Geometric and parameter knowledge for modeling is derived from the machining step. In the proposed approach, two geometric modeling methods are investigated to construct feature state volumes (FSVs). The first method utilizes FSV modeling to simulate the shape of unmachined features before rough machining, while the second method utilizes FSV modeling to construct the shape of machined features before finishing machining. The case studies illustrate that the proposed approach can construct FSVs for various machining states and autonomously generate process models. In digital manufacturing, this approach assists process planners in intuitively evaluating the reasonableness of process routes. Additionally, it provides the essential driving geometry required for the autonomous generation of tool paths.

Non-planar 5-axis directional additive manufacturing of plastics: Machine, process, and tool path

Additive manufacturing (AM) of plastics is a rising research and application area for light weighting. The conventional way of AM is conducted layer-by-layer, whereas recently non-planar AM is proposed with several advantages such as better use of the material through decreased weight-to-strength ratio, reuse of support material and increased conformity for AM of complex shapes. As for all the tool path-based manufacturing processes, generation of the tool path, selection of process parameters and the machine tool configuration significantly affects the product quality. In this study, non-planar AM of plastics is handled in a holistic manner covering tool path, process, and machine perspectives. Tool path generation for non-planar and directional AM is discussed in accordance with the CAD-CAM software, where the tool path is generated using a novel approach which works with the bead directions obtained from FEM based mechanical analysis. The effects of critical process parameters on the geometrical conformity for non-planar AM are discussed through visual inspection. The proposed cluster-based tool path planning is successfully shown demonstrated from the geometrical perspective.

Freeform trajectory generation for fiber reinforced polymer composites additive manufacturing with variable-deposition-width

Continuous carbon fiber-reinforced polymers (CCFRPs) printing is a unique additive manufacturing (AM) technique that enhances design flexibility and enables on-demand production of lightweight, high-strength composite parts. However, current trajectory generation methods for CCFRPs-AM have limited control over fiber placement and flexible matrix path infill, particularly in complex regions, often using constant deposition widths that restrict path coverage and mechanical properties. To address these limitations, this paper presents a variable-deposition-width (VDW) trajectory generation method for CCFRPs-AM. The approach integrates a streamline-based technique for fiber paths with a Voronoi diagram-based method for matrix path generation, optimizing path placement based on 2D stress fields while accommodating user-defined fiber paths. Mechanical tests by us on fabricated open-hole composite components demonstrate that the proposed approach enhances the interfacial bonding and increases the maximal loading capacity under tensile stress.

Conductive paths generation using topology optimization for worst-case thermal design in space systems

Designing thermal systems for space platforms is a challenging task due to the wide variability in thermal conditions during the orbit and the strict constraints imposed by other subsystems. Several strategies have been developed to address these challenges while minimizing the thermal control subsystem’s signature, encompassing different algorithms optimize components positions in the platform. When relocation is not possible, thermal couplings between components can be modified to enhance heat transfer and achieve more uniform temperature distribution. To design optimal thermal paths in 2D space structures, in this paper a topology optimization framework based on the Heaviside Projection Method is introduced, using the Lumped Parameter Method (LPM) and taking the radiation heat transfer into account. The algorithm’s performance is tested on a Printed Circuit Board (PCB) by designing an optimal copper layer to meet specific thermal requirements under both extreme hot and cold conditions. Results show a significant improvement in thermal compliance for all components with the addition of this high-conductivity layer. Additionally, a reduction method is introduced to transfer the material distribution from the optimization to a coarser mesh of any size, which is useful to facilitate its implementation in existing software, where a large number of degrees of freedom can be a limitation.

Jerk limited continuous tool path generation for flexible systems in non-cartesian coordinate systems

Inspired by computer numerical control (CNC) technology and drastic changes in the world of electronics and microcontrollers, the idea of using non-cartesian coordinate system for CNC machines became a topic of interest. Non-cartesian coordinate systems provide freedom of movement in systems with large working spaces where high dimensional accuracy is not a prime concern. Applications of such systems can be found in murals, where a painting is applied to a large wall of any arbitrary build material. In the present paper, a new design was introduced to automate the process of drawing murals on large surfaces. The proposed mechanism utilized chain and sprocket to overcome the size limitations of traditionally available x-y tables. In addition to that, the developed mechanism does not utilize the common robust structures used in the conventional CNC systems and the tool motion is created using flexible arms that are not heavily constrained. The effect of systems unwanted vibration has been eliminated using continuous trajectory and kinematic profiles. Parametric curve interpolation algorithms for trajectory planning were implemented to increase the flexibility of drawing complex curves. Parametric curves such as B-spline and non-uniform rational B-spline (NURBS) were employed to generate a jerk limited trajectory for motion control and extraction of kinematic profiles. Such trajectory constrains the jerk of the system and guarantees a smooth motion free of feed-rate fluctuations. Compared to other methods available for extracting the kinematic profiles, jerk limited method decreases the travel time and trajectory error. Ultimately, motion commands were produced by using kinematic profiles and the second order Taylor interpolator. STM32746ZG card was used as the main processor and two stepper motors controlled by a Bit Pattern interpolation algorithm were utilized to drive the proposed mechanism. The input pulses of stepper motors were stored as binary bits and transmitted to drivers in real-time. The feedback was also evaluated by two rotary encoders, placed at the end of the motors’ shafts. Encoder data were acquired and transmitted using a TCP/IP protocol to guarantee zero data loss during the transmission.

Topology optimisation of fibre-reinforced composites accounting for buckling resistance and manufacturability

A topology optimisation approach that accounts for buckling resistance and manufacturability using fibre-reinforced composites is presented. This approach combines topology optimisation and fibre orientation optimisation to achieve a design with maximum buckling resistance. To ensure the optimal designs can be manufactured using 3D printing, constraints based on the continuity of fibre orientation and fibre path generation are applied. A compressed column, a stubby cantilever and an MBB beam are designed to demonstrate the response of the topology and fibre orientation when accounting for buckling resistance, as well as the compromise due to the inclusion of manufacturing constraints. The results show that the presented approach successfully guarantees manufacturability with a significant increase in buckling resistance.

Multi-AUV Kinematic Task Assignment Based on Self-Organizing Map Neural Network and Dubins Path Generator.

To deal with the task assignment problem of multi-AUV systems under kinematic constraints, which means steering capability constraints for underactuated AUVs or other vehicles likely, an improved task assignment algorithm is proposed combining the Dubins Path algorithm with improved SOM neural network algorithm. At first, the aimed tasks are assigned to the AUVs by the improved SOM neural network method based on workload balance and neighborhood function. When there exists kinematic constraints or obstacles which may cause failure of trajectory planning, task re-assignment will be implemented by changing the weights of SOM neurals, until the AUVs can have paths to reach all the targets. Then, the Dubins paths are generated in several limited cases. The AUV's yaw angle is limited, which results in new assignments to the targets. Computation flow is designed so that the algorithm in MATLAB and Python can realize the path planning to multiple targets. Finally, simulation results prove that the proposed algorithm can effectively accomplish the task assignment task for a multi-AUV system.

Automated 3D-printing path generation for parts with holes using continuous fibre filament

ABSTRACT The increasing popularity of continuous fibre 3D-printing brings new challenges to the composite design process. Usually, composite parts include physical obstacles: holes for bearings, inserts, bolts, external shape limitations, or exclusion zones. For such parts the well-known types of in-plane reinforcements – classical unidirectional infill, spiral reinforcement of holes, and generalised perimeters – do not provide satisfactory solutions. This article describes a completely new, specialised type of reinforcement based on the generalised belt-and-pulleys transmission principle, where the pulleys are generalised to any convex obstacles and the belt shows the fibre path connecting them. This type of fibre reinforcement provides an intuitive curvilinear connection between the obstacles and optimises the fibre direction in the most common case, when a load is applied to the obstacles. The developed algorithm has been implemented and is now an integral part of fibrify® Design Suite by 9T Labs.

Spiral tool path generation method for complex pocket machining based on electrostatic field theory

Spiral tool path generation method for complex pocket machining based on electrostatic field theory

Robotic navigation with deep reinforcement learning in transthoracic echocardiography

Abstract Purpose The search for heart components in robotic transthoracic echocardiography is a time-consuming process. This paper proposes an optimized robotic navigation system for heart components using deep reinforcement learning to achieve an efficient and effective search technique for heart components. Method The proposed method introduces (i) an optimized search behavior generation algorithm that avoids multiple local solutions and searches for the optimal solution and (ii) an optimized path generation algorithm that minimizes the search path, thereby realizing short search times. Results The mitral valve search with the proposed method reaches the optimal solution with a probability of 74.4%, the mitral valve confidence loss rate when the local solution stops is 16.3% on average, and the inspection time with the generated path is 48.6 s on average, which is 56.6% of the time cost of the conventional method. Conclusion The results indicate that the proposed method improves the search efficiency, and the optimal location can be searched in many cases with the proposed method, and the loss rate of the confidence in the mitral valve was low even when a local solution rather than the optimal solution was reached. It is suggested that the proposed method enables accurate and quick robotic navigation to find heart components.

RAF-AG: Report analysis framework for attack path generation

Information sharing is a key practice in cybersecurity for coping with the ever-changing cyberattacks that are targeting computer systems. Thus, when cyber incidents happen, cyber threat intelligence (CTI) reports are prepared and shared among cybersecurity practitioners to help them get up-to-date information about those incidents. However, reading and analyzing the report text to comprehend the included information is a cumbersome process. Although techniques based on deep learning were proposed to speed up report analysis in order to obtain the enclosed essential information, such as attack path, training data insufficiency makes these methods inefficient in practical circumstances.This paper presents RAF-AG, a report analysis framework for attack path generation. To analyze CTI reports, RAF-AG utilizes the sentence dependency tree for entity and relation extraction, and a weak supervision approach for entity labeling. This is followed by graph building and graph alignment for generating the attack paths. Our approach resolves the data insufficiency problem in the cybersecurity domain by lowering the need for expert involvement. We evaluated RAF-AG by comparing the generated attack paths with those produced by AttacKG, a state-of-the-art automatic report analysis framework. RAF-AG was able to identify cyberattack steps by matching their appearance order inside the report, and link them with techniques from the MITRE ATT&CK knowledge base with an improved F1 score compared to AttacKG (0.708 versus 0.393).

Contact-Map-Driven Exploration of Heterogeneous Protein-Folding Paths.

We have recently shown how physically realizable protein-folding pathways can be generated using directed walks in the space of inter-residue contact-maps; combined with a back-transformation to move from protein contact-maps to Cartesian coordinates, we have demonstrated how this approach can generate protein-folding trajectory ensembles without recourse to molecular dynamics. In this article, we demonstrate that this framework can be used to study a challenging protein-folding problem that is known to exhibit two different folding paths which were previously identified through molecular dynamics simulation at several different temperatures. From the viewpoint of protein-folding mechanism prediction, this particular problem is extremely challenging to address, specifically involving folding to an identical nontrivial compact native structure along distinct pathways defined by heterogeneous secondary structural elements. Here, we show how our previously reported contact-map-based protein-folding strategy can be significantly enhanced to enable accurate and robust prediction of heterogeneous folding paths by (i) introducing a novel topologically informed metric for comparing two protein contact maps, (ii) reformulating our graph-represented folding path generation, and (iii) introducing a new and more reliable structural back-mapping algorithm. These changes improve the reliability of generating structurally sound folding intermediates and dramatically decrease the number of physically irrelevant folding intermediates generated by our previous simulation strategy. Most importantly, we demonstrate how our enhanced folding algorithm can successfully identify the alternative folding mechanisms of a multifolding-pathway protein, in line with direct molecular dynamics simulations.

Adaptive remanufacturing for freeform surface parts based on linear laser scanner and robotic laser cladding

Freeform surface parts play a significant role in the aerospace industry, the mold- manufacturing industry and the automobile industry, and it is energy-saving, material-saving, time-saving and environmentally beneficial to remanufacture the damaged components to restore their functionality and performance. Due to the complex geometry of the freeform surface wear, the adaptive remanufacturing of freeform surface parts is confronted with challenges. In this paper, an adaptive remanufacturing method for freeform surface parts based on linear laser scanner and robotic laser cladding is proposed to realize the precise freeform surface measurement and optimized remanufacturing path generation. On the one hand, a systematic wear measurement and assessment method is proposed to precisely locate and quantify the wear. With the noncontact calibration of the laser scanner and industrial robot, the contour of the target surface is real-timely measured and the reverse model is efficiently constructed, which provides detailed 3D morphological information of the worn freeform surface for the latter wear analysis. Next, considering the considerable difference between the reverse model and the nominal model, a refined model aligning method weighted by surface wear segmentation is proposed to minimize the alignment error and, further, the difference entity to be additively manufactured is obtained by discrete model comparison. On the other hand, to cope with the unsatisfactory binding strength over the freeform surface basis and small fragments of the working path for the traditional plane or cylinder slicing method, a novel remanufacturing path generation method is proposed. Considering the curvature distribution of the freeform surface, an optimized equidistant freeform surface slicing method is especially proposed for the difference entity to realize the adaptive fitting to the freeform basin. Furthermore, based on the equivalent volume overlapping model of laser cladding, the cladding track filling method for the freeform surface slicing is designed with the optimized track-to-track distance, which can reduce surface waviness and improve remanufacturing efficiency. Finally, simulations and experiments for the remanufacturing scenario of the steam turbine blade are conducted to verify the validity and feasibility of the proposed adaptive remanufacturing method for freeform surface parts based on linear laser scanner and robotic laser cladding.

Flat-Bottom Elastic Network Model for Generating Improved Plausible Reaction Paths.

Rapid generation of a plausible reaction path connecting a given reactant and product in advance is crucial for the efficient computation of precise reaction paths or transition states. We propose a computationally efficient potential energy based on the molecular structure to generate such paths. This potential energy has a flat bottom consisting of structures without atomic collisions while preserving nonreactive chemical bonds, bond angles, and partial planar structures. By combining this potential energy with the direct MaxFlux method, a recently developed reaction-path/transition-state search method, we can find the shortest plausible path passing within the bottom. Numerical results show that this combination yields lower energy paths compared to the paths obtained by the well-known image-dependent pair potential. We also theoretically investigate the differences between these two potential energies. The proposed potential energy and path generation routine are implemented in our Python version of the direct MaxFlux method, available on GitHub.

Research on Path Planning Technology of a Line Scanning Measurement Robot Based on the CAD Model

With the development of robotics and vision measurement technology, the use of robots with line laser scanners for 3D scanning and measurement of parts has become a mainstream trend in the field of industrial inspection. Traditional scanning and measuring robots mainly use the teach-in scanning method, which has unstable scanning quality and low scanning efficiency. In this paper, the adaptive sampling method for a free-form surface, which can realize the adaptive distribution of surface measurement points according to the curvature features of free-form surfaces, is proposed first. Then, integrated with the proposed adaptive sampling method, the automatic path planning method is proposed. This method consists of adaptive sampling, scanning attitude calculation based on a quaternion, scanning viewpoint planning based on viewable cones, and scan path generation based on bi-directional scanning. Based on the proposed automatic path planning method, the scanning and measuring robot can obtain complete 3D information of the surface to be measured with high measurement accuracy and efficiency. The performance index of the laser scanner can be fully reached.