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Comparison for global optimal paths obtained by several algorithms by experiment in the indoor equipment room
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The intelligent patrol car with environmental sensing and autonomous navigation is a special robot, which is mostly used for equipment defect detection in industrial areas such as the power distribution room or data center room. A path planning algorithm for the navigation system of intelligent patrol car is proposed to ensure efficient and secure...
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Context 1
... map created by SLAM is shown in Fig. 9, the environment of indoor equipment room is transformed into a cost map, the gray area in the map represents obstacles, i.e. all kinds of servers, power cabinets, distribution cabinets, network switches, and concrete pillars. The black wrapped area outside the obstacles represents the obstacle range of the opened door in an electrical ...
Context 2
... APFM+MOPSO and A*algorithm are used to find the optimal path, and each algorithm is run 10 times. The global path length, total turning-angle variation, total motion time and collision times recorded in the experiment is shown in TABLE V. The global optimal paths obtained by APFM+MOCMCSO, APFM+MOCSO, APFM+MOPSO and A*algorithm is shown in Fig. 9. The optimal path obtained by APFM+ MOCMCSO in the 4th experiments is 36.52 m in global path length and 8.37 rad in total turning-angle variation, which are both the minimum in 10 results, ensuring the highest movement efficiency for the patrol car while taking into account the global path smoothness, and there is no obstacle collision ...
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... and APFM+MOPSO, and the value is only 48.39 m, 12.97 rad and 196.9 s. The reason for this phenomenon is that, A*algorithm has too much meaningless search paths in the global search process, which is inefficient, and the planned path fails to reach global optimum, the mean value of total time is also too long. In addition, it can be seen from Fig. 9 that the optimal path obtained by APFM+MOCSO ensures the shortest path between the starting point and the target point. Compared with the other three algorithms, the optimal path is shorter and smoother, in particular, the turning-angle variation between the adjacent lines is the smallest, so the patrol car will not reduce the movement ...
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Citations
... For instance, studies [11][12][13] introduced chaotic mapping during algorithm initialization to expand the initial search space, thereby enhancing search efficiency and avoiding local optima. Other works [14][15][16] suggested using Cauchy mutation to maintain population diversity, which helps improve convergence efficiency. Furthermore, studies [17][18][19] proposed hybridizing different optimization algorithms, leveraging the global search capability of global optimization algorithms and the precise local search capability of local optimization algorithms to enhance overall performance. ...
The Crowned Porcupine Optimization (CPO) algorithm exhibits certain deficiencies in initialization efficiency, convergence speed, and adaptability. To address these issues, this paper proposes an enhanced Crowned Porcupine Optimization algorithm (ICPO) based on multiple improvement strategies. ICPO optimizes the initialization process by introducing Logistic chaotic mapping, thereby expanding the search space. It accelerates convergence through an elite retention strategy and enhances global search capability by integrating stochastic operations, mutation-like operations, and crossover-like operations to increase population diversity. Additionally, adaptive step tuning based on fitness values is employed to comprehensively improve the algorithm’s performance. To verify the effectiveness of ICPO, 23 standard functions were used for a comprehensive evaluation, and its practicality was further validated through optimization of actual engineering design problems. The experimental results demonstrate significant improvements in convergence speed, solution quality, and adaptability with ICPO.
... To alleviate the susceptibility of HHO algorithm to local optima, drawing inspiration from the work referenced in [31][32], this study incorporates optimization by integrating the Cauchy distribution function. Harnessing the unique attributes of the Cauchy distribution function, characterized by a smaller peak at the origin and an extended distribution at both ends, the inclusion of the Cauchy operator in the HHO algorithm amplifies mutation effects at both ends during optimization, yielding enhanced performance. ...
... Taking inspiration from prior studies [32], this research integrates the dynamic opposition-based learning strategy to improve the efficiency of acquiring optimal solutions. The core principle of opposition-based learning entails generating a solution derived from the current one. ...
In the pursuit of enhancing harmonic detection precision within microgrids, this paper introduces a pioneering algorithm, VMD-DCHHO-HD, which amalgamates Variational Mode Decomposition (VMD) with an advanced Harris Hawk Optimization algorithm characterized by dynamic opposition-based learning and Cauchy mutation (DCHHO). This study establishes a fitness function based on Shannon entropy, thereby minimizing the Local Minimum Entropy (LME) as the optimization objective for DCHHO. Building upon this, the VMD crucial parameters are efficiently identified using the enhanced HHO algorithm (DCHHO), enabling precise decomposition of complex voltage signals. The proposed method effectively addresses issues commonly encountered in traditional Empirical Mode Decomposition (EMD) during harmonic analysis, such as mode mixing, endpoint effects, and significant errors. Notably, it adeptly captures harmonic components spanning diverse frequencies, offering a nuanced solution to common pitfalls in traditional methodologies. In simulation experiments, VMD-DCHHO-HD showcases remarkable proficiency in extracting microgrid voltage signals, excelling at discerning high-order, low-amplitude harmonic components amid noise. The algorithm’s superior precision and heightened reliability, as affirmed by comparative analyses against existing methods, position it as an advanced tool for precise and robust harmonic analysis in microgrid systems.
... The population size and the maximum iteration of the developed lecture video indexing and retrieval model are 10 and 25, respectively. The suggested AACOA-MRAN implemented video retrieval and indexing model was compared with various algorithms like Deer Hunting Optimization Algorithm (DHOA)-MRAN [33], Water Strider Algorithm (WSA)-MRAN [34], Cat Swarm Optimization (CSO)-MRAN [35] and ACOA-MRAN [31] and also with various existing techniques like Visual Geometry Group (VGG)-16 [36], Inception-V3 [37] and Xception [38] to prove the high efficiency of the proposed model in the video retrieval and indexing process. ...
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Due to technological development, the mass production of video and its storage on the Internet has increased. This made a huge amount of videos to be available on websites from various sources. Thus, the retrieval of essential lecture videos from multimedia is difficult. So, an effective way of indexing and retrieving the video by considering various similarities in the video features is suggested using the deep learning method in this paper. From the standardized set of data, the videos containing lectures are obtained for training. The optimal keyframes are selected from the obtained videos employing the Adaptive Anti-Corona virus Optimization Algorithm. Then the video contents are segmented and arranged on the basis of the optimized keyframes. The optical characters, such as semantic words and keywords, are recognized by means of Optical Character Reorganization, and the image features are extracted from the segmented frames with the help of a Multi-scale Residual Attention Network (MRAN). The generated pool of features is arranged and stored in the database according to the contents. Text and video queries are given as the input for testing the trained model. The features from the text query and the features of the optimized keyframes from the video query are obtained with the help of MRAN in the testing phase. The generated pool features from the text and video queries are compared with the features that are stored in the database for analyzing the similarities using Cosine, Jacquard, and Euclidean similarity indices. From this, the multi-similarity features are used for retrieval of the relevant videos in accordance with the provided query. The experimental results show that the performance of the proposed system for video indexing and retrieval is better and more efficient than the existing methods of video retrieval.
... In the future, with the further improvement and development of swarm intelligence algorithms as well as deep learning networks and other related sciences, the proposed method will be explored for applications within the fields of image processing [40], unmanned vehicles [41], production scheduling [42], and so on. ...
In recent years, deep learning has been increasingly used in fault diagnosis of rotating machinery. However, the actual acquisition of rolling bearing fault signals often contains ambient noise, making it difficult to determine the optimal values of the parameters. In this paper, a sparrow search algorithm (LSSA) based on backward learning of lens imaging and Gaussian Cauchy variation is proposed. The lens imaging reverse learning strategy enhances the traversal capability of the algorithm and allows for a better balance of algorithm exploration and development. Then, the performance of the proposed LSSA was tested on the benchmark function. Finally, LSSA is used to find the optimal modal component K and the optimal penalty factor α in VMD-GRU, which in turn realizes the fault diagnosis of rolling bearings. The experimental results show that the model can achieve a 96.61% accuracy in rolling bearing fault diagnosis, which proves the effectiveness of the method.
... The safe and stable operation of the power system is the most important task, and in order to ensure the safe and stable operation of the system, we must first ensure the state of the equipment. A large number of failures and problems will occur in the daily operation of electric power and communication equipment, and the maintenance and maintenance of these equipment is the focus of the operation and guarantee work (Zhao et al. 2020). The traditional maintenance method is usually in the form of manual maintenance tickets, which has the advantages of low inspection efficiency and high safety risk. ...
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... For example, it knows whether the door of the refrigerator is open and when to turn on the coffee machine. It also has a motion detector to determine where people are in the room [11]. ...
In this paper, warehouse logistics robot as the research object, starting from the reality of modern e-commerce logistics industry, proposed a warehousing logistics mobile robot path planning method. Ant colony algorithm is used to plan the forward path of mobile robot in static and dynamic environment of warehouse logistics. The results show that the elite-based strategy improves the global search capability of ACA and eliminates redundant nodes in the path, and the path length of the obtained plan is significantly better than that of traditional ACA, which is reduced by 3.46% and 5.90%, respectively, indicating that the elite-based strategy and the central-point-based smoothing method play their roles. The path length of the robot’s final operation is larger than that of the global path planning, which increases by 1, accounting for 6.67% of the original path length. Therefore, the storage and logistics mobile robot based on ACA-E algorithm has short driving distance and superior obstacle avoidance ability.
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... Xu et al (2020) proposed an artificial bee colony algorithm introducing a co-evolutionary framework for path planning of mobile robots, which improves the convergence speed and avoids dimensionality dependence. Zhao et al (2020) proposed a co-optimization of a multi-objective Cauchy mutation cat colony optimization and an artificial potential field method to solve the path planning problem of an intelligent patrol car navigation system. Song et al (2021) solved the problem of local optimum and premature convergence by an improved PSO algorithm that combines continuous high-degree Bezier curves to plan the smooth path of the robot. ...
In this paper, an improved sparrow search algorithm (SSA) for local path planning problem of mobile robot in an unknown environment is presented. The problems of premature convergence and decline of population diversity of basic SSA are solved by the inspiration of fitness-distance balance (FDB) selection and Harris Hawks Algorithm. A hybrid fitness function is formulated considering both path length and path safety, which enables the mobile robot to move to the target location safely. The effectiveness and superiority of the proposed improved SSA (ISSA) is verified in CEC 2017 suite for comparison experiments with multiple intelligent optimization algorithms. Local path planning simulation experiments are implemented using the proposed algorithm in the unknown environment and compared with other algorithms, and the results show that our algorithm is effective and robust in solving local path planning problem of mobile robots.
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... From the three parameters of equation ( The Artificial Potential Field (APF) algorithm [51]- [54] was first discovered by Khatib [55], which employs the theory of attractive and repulsive forces such as magnetic fields. In other words, artificial potential field force [56] is the sum of the gravitational potential field force [57] and the repulsive potential field force [58] . Equation (10) shows the formula of APF. ...
The disinfection robot is a virus-sterilizing robot that uses a nonholonomic robot model. Route planning algorithms are needed to allow disinfection robots to sterilize rooms in unknown areas and perform the task while navigating using a potential field algorithm. There is a problem applying the algorithm to nonholonomic robots: avoiding obstacles. The proposed route planning algorithm has been transformed into a potential force used to plan the path of disinfection robots in static and dynamic environments and environments with static obstacles. A potential field algorithm is used. There are some issues when the potential force algorithm is applied to nonholonomic disinfection robots in the area. Like any other robot, it takes a long time to avoid static obstacles. Therefore, this paper proposed a potential force algorithm that allows a robot to move towards a target point while avoiding static obstacles. The algorithm showed that a modified potential field algorithm with potential force could be applied to differential-driven robots for path planning. The disinfection robot could avoid obstacles with a faster response using this algorithm.
