The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights.

/pdf/a-comprehensive-review-of-coverage-path-planning-in-robotics-6u94w2far3.pdf

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

The traditional particle swarm optimization (PSO) path planning algorithm represents each particle as a path and evolves the particles to find an optimal path. However, there are problems in premature convergence, poor global search ability, and to the ease in which particles fall into the local optimum, which could lead to the failure of fast optimal path obtainment. In order to solve these problems, this paper proposes an improved PSO combined gray wolf optimization (IPSO-GWO) algorithm with chaos and a new adaptive inertial weight. The gray wolf optimizer can sort the particles during evolution to find the particles with optimal fitness value, and lead other particles to search for the position of the particle with the optimal fitness value, which gives the PSO algorithm higher global search capability. The chaos can be used to initialize the speed and position of the particles, which can reduce the prematurity and increase the diversity of the particles. The new adaptive inertial weight is designed to improve the global search capability and convergence speed. In addition, when the algorithm falls into a local optimum, the position of the particle with the historical best fitness can be found through the chaotic sequence, which can randomly replace a particle to make it jump out of the local optimum. The proposed IPSO-GWO algorithm is first tested by function optimization using ten benchmark functions and then applied for optimal robot path planning in a simulated environment. Simulation results show that the proposed IPSO-GWO is able to find an optimal path much faster than traditional PSO-GWO based methods.

An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning.

Energy efficiency has been one of the main issues in manufacturing. Rapid energy consumption (EC) calculation is the key to improving the efficiency of EC optimization. In this paper, rapid EC calculation of robot laser processing systems (RLPSs) is studied based on structural decomposition. Firstly, the generalized motion EC models of main functional units of RLPSs are established for manufacturing processes, while the power consumption characteristics and computational complexity of EC are analyzed. Then, a modeling method of EC is proposed for industrial robots (IRs), based on structural decomposition, considering the generating mechanisms and computational complexity of EC. The models of all EC components are established, which are all parameterized functions of time or key factors (joint angle paths of the robot and the average acceleration of the end-effector). Moreover, EC models of other functional units are also well established according to the corresponding EC characteristics. A KUKA robot case is described to verify the proposed method. The results show that the established EC model of the IR has reliable prediction accuracy within the feasible speed range, which can well reflect the characteristic relationships between EC and time. The proposed study solution is also suitable for EC modeling of other robot manufacturing systems. Furthermore, the energy-saving planning of processing complex parts are analyzed for a laser processing IR. The RLPS has considerable energy-saving potentials under situations concerned. 

Structural decomposition-based energy consumption modeling of robot laser processing systems and energy-efficient analysis

The Complete Coverage Path Planning (CCPP) problem is a subfield of industrial motion planning that has applications in various domains, ranging from mobile robotics to treatment applications. Especially in precision agriculture with a high level of automation, the use of CCPP techniques is essential for efficient resource utilization, reduced soil compaction, and increased yields. This paper reviews the CCPP problem in the context of machines operating in agricultural fields and proposes a methodological approach consisting of three steps: Generating the Guidance Tracks (i.e., the track system along which the path should be oriented), determining the traversing sequence through these tracks, and planning a smooth and drivable path. This paper provides an in‐depth review of optimization‐based approaches that deal with the first step, the generation of the guidance track system. Thereby, a comprehensive and pedagogical approach for the generation of guidance tracks for arbitrarily shaped two‐dimensional regions of interest is provided, along with an overview and detailed elaboration on different exact cellular decomposition techniques found in literature. Furthermore, cost functions are outlined for the different approaches presented in this work, which are utilized to generate optimal guidance tracks. Finally, this survey serves as an introductory guide for research and practitioners to solve the CCPP problem effectively and efficiently.

Optimal guidance track generation for precision agriculture: A review of coverage path planning techniques

Complete coverage path planning (CCPP) is vital in mobile robot applications. Optimizing CCPP is particularly significant in precision agriculture, where it enhances resource utilization, reduces soil compaction, and boosts crop yields. This work offers a comprehensive approach to CCPP for agricultural vehicles with curvature constraints. Our methodology comprises four key stages. First, it decomposes complex agricultural areas into simpler cells, each equipped with guidance tracks, forming a fixed track system. The subsequent route planning and smooth path planning stages compute a path that adheres to path constraints, optimally traverses the cells, and aligns with the track system. We use the generalized traveling salesman problem (GTSP) to determine the optimal traversing sequence. Additionally, we introduce an algorithm for calculating paths that are both smooth and curvature-constrained within individual cells, as well as paths that enable seamless transitions between cells, resulting in a smooth, curvature-constraint coverage path. Our modular approach allows method flexibility at each step. We evaluate our method on real agricultural fields, demonstrating its effectiveness in minimizing path length, ensuring efficient coverage, and adhering to curvature constraints. This work establishes a strong foundation for precise and efficient agricultural coverage path planning, with potential for further real-world applications and enhancements.

Optimal Coverage Path Planning for Agricultural Vehicles with Curvature Constraints

Coverage path planning on a complex free-form surface is a representative problem that has been steadily investigated in path planning and automatic control. However, most methods do not consider many optimisation conditions and cannot deal with complex surfaces, closed surfaces, and the intersection of multiple surfaces. In this study, a novel and efficient coverage path-planning method is proposed that considers trajectory optimisation information and uses point cloud data for environmental modelling. First, the point cloud data are denoised and simplified. Then, the path points are converted into the rotation angle of each joint of the manipulator. A mathematical model dedicated to energy consumption, processing time, and path smoothness as optimisation objectives is developed, and an improved ant colony algorithm is used to solve this problem. Two measures are proposed to prevent the algorithm from being trapped in a local optimum, thereby improving the global search ability of the algorithm. The standard test results indicate that the improved algorithm performs better than the ant colony algorithm and the max–min ant system. The numerical simulation results reveal that compared with the point cloud slicing technique, the proposed method can obtain a more efficient path. The laser ablation de-rusting experiment results specify the utility of the proposed approach.

Laser Ablation Manipulator Coverage Path Planning Method Based on an Improved Ant Colony Algorithm

Abstract. In this paper, the machining principle of variable hyperbolic
circular-arc-tooth-trace (VH-CATT) cylindrical gears is analysed, and a
modular configuration design of this special machine tool is carried out.
Based on the importance of parameter and knowledge dependence of rough set
theory, the degree of influence of the index hidden in the data on the
decision-making results is mined, and an algorithm for calculating the
objective weight of the index is given. The comprehensive weight of the
index is then obtained, combining the analytic hierarchy process. After
obtaining the results of the index's comprehensive weight and module
attribute classification, a module retrieval strategy is formulated using
the fuzzy similarity priority ratio algorithm. Based on the modular division
of this special gear-making machine tool, the modular configuration sequence
of the machine tool using the depth-first search algorithm is determined.
According to the module retrieval strategy and configuration sequence, a
traversal of module instances is carried out, and a variety of modular
configuration schemes of the machine tool is obtained, which provides a
practical and efficient method for the rapid design of a special machine tool
for VH-CATT cylindrical gears.


/pdf/modular-configuration-design-of-a-special-machine-tool-for-295rksg6.pdf

Modular configuration design of a special machine tool for variable hyperbolic circular-arc-tooth-trace cylindrical gears

The 4SPRR-SPR parallel robot, which has considerable potential for application in the field of machining, is a novel closed-loop mechanism with a high rigid-weight ratio. Kinematics and workspace analyses of the 4SPRR-SPR parallel robot are key requirements for its application in machining. In this study, the inverse kinematics of the 4SPRR-SPR parallel robot is analyzed using a geometric method based on the mechanism arrangement of the robot. The forward kinematics model is derived by training the vector-quantified temporal associative memory (VQTAM) network, which originates from a self-organizing map (SOM). Furthermore, an improved algorithm is obtained by combining the locally linear embedding (LLE) and VQTAM methods. A boundary extraction algorithm for the workspace analysis of the parallel robot is proposed. The performance of the boundary extraction algorithm is analyzed and compared with that of a global search algorithm; the result indicates that the novel algorithm has the same computational accuracy in addition to higher efficiency. The workspace of the 4SPRR-SPR parallel robot is analyzed using the boundary extraction algorithm. Finally, the 3D model of the 4SPRR-SPR parallel robot is simulated using the ADAMS software to verify the reliability of the proposed algorithms. The simulation results demonstrate the effectiveness of the methods proposed in this study. In addition, the robot kinematics and workspace analysis methods described herein can be extended to other serial and parallel robots. This research provides a theoretical framework for trajectory planning of mechanisms, workspace optimization of robots, and robotic control.

/pdf/kinematics-and-workspace-analysis-of-4sprr-spr-parallel-3xssa2zecc.pdf

Kinematics and workspace analysis of 4SPRR-SPR parallel robots

ABSTRACT The geometric parameters of the pressure-swirl nozzle demonstrate a significant effect on the atomization performance such as the spray cone angle and mass flow rate. However, these objectives are incompatible, and obtaining a nozzle geometric parameter that optimizes all the atomization performances is difficult. The effectiveness of the nozzle optimization process is typically low, resulting in high costs. To this purpose, an enhanced CBSO-Kriging surrogate model was developed to solve this expensive black box problem, and the spray cone angle and mass flow rate were optimized using a multi-objective optimization algorithm. Firstly, the optimal Latin hypercube sampling method was used to generate 65 sets of sample points, and the corresponding response values were calculated using CFD simulation. The established surrogate model of atomization performance can predict the atomization characteristics of the nozzle with arbitrary shape. The correlation between the key geometric parameters and the spray cone angle and the mass flow rate was determined on this basis. Then, to find the optimal geometric parameters for the nozzle, the NSGA-II algorithm was used. In comparison to the initial model, the optimization results show that the spray cone angle and mass flow rate increased by 4.26% and 37.74%, respectively.

Multi-objective optimization of the geometric parameters of a pressure-swirl nozzle

The lubrication characteristics of a cylindrical gear transmission with a variable hyperbolic circular-arc-tooth-trace (VH-CATT) are important theoretical bases for the tribology design of the gear and its fatigue life prediction. Existing lubrication characteristics studies of it are based on simplified linear contact models, which are limited to one specific meshing position only. Therefore, the mathematical models for tooth contact analysis (TCA) and loaded tooth contact analysis (LTCA) are established to calculate most time-varying parameters in a meshing period, such as the kinematic parameters, spatial geometric parameters, and contact parameters, are calculated. Accordingly, a thermo-elastohydrodynamic lubrication (TEHL) model of an elliptical contact of this gear transmission is presented. Using all the proposed mathematical models, the TEHL results with smooth and rough tooth surfaces in a meshing period are analyzed. The obtained results reveal that these models can be used to predict the distribution of the film thickness, pressure, and temperature rise on the smooth and rough tooth surfaces of VH-CATT cylindrical gear transmission in each contact area of a meshing period. And it shows that the time-varying parameters have great influences on lubrication performance. Furthermore, the lubrication mechanism in a meshing period is investigated systematically in VH-CATT cylindrical gear transmission.

TEHL analysis of VH-CATT cylindrical gear transmission in elliptical contact considering time-varying parameters

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Papers

Laser Ablation Manipulator Coverage Path Planning Method Based on an Improved Ant Colony Algorithm

Modular configuration design of a special machine tool for variable hyperbolic circular-arc-tooth-trace cylindrical gears

Kinematics and workspace analysis of 4SPRR-SPR parallel robots

Multi-objective optimization of the geometric parameters of a pressure-swirl nozzle

TEHL analysis of VH-CATT cylindrical gear transmission in elliptical contact considering time-varying parameters