In obstacle spatial path planning, the traditional A* algorithm has the problem of too many turning points and slow search speed. With this in mind, a path planning method that improves the A* (A-Star) algorithm is proposed. The mobile robot platform was equipped with a lidar and inertial measurement unit (IMU). The Hdl_graph_slam mapping algorithm was used to construct a two-dimensional grid map, and the improved A* algorithm was used for path planning of the mobile robot. The algorithm introduced the path smoothing strategy and safety protection mechanism, and it eliminated redundant points and minimal corner points by judging whether there were obstacles in the connection of two path nodes. The algorithm effectively improved the smoothness of the path and facilitated the robot to move in the actual operation. It could avoid the wear of the robot by expanding obstacles and improving the safety performance of the robot. Subsequently, the algorithm introduced the steering cost model and the adaptive cost function to improve the search efficiency, making the search purposeful and effective. Lastly, the effectiveness of the proposed algorithm was verified by experiments. The average path search time was reduced by 13%. The average search extension node was reduced by 11%. The problems of too many turning points and slow search speed of traditional A* algorithm in path planning were improved.

/pdf/improved-a-path-planning-method-based-on-the-grid-map-10qop14o.pdf

Improved A* Path Planning Method Based on the Grid Map

Optimization of automated garbage recognition model based on ResNet-50 and weakly supervised CNN for sustainable urban development


The purpose of this work is to provide a stochastic framework based on the scale conjugate gradient neural networks (SCJGNNs) for solving the malaria disease model of pesticides and medication (MDMPM). The host and vector populations are divided in the mathematical form of the malaria through the pesticides and medication. The stochastic SCJGNNs procedure has been presented through the supervised neural networks based on the statics of validation (12%), testing (10%), and training (78%) for solving the MDMPM. The optimization is performed through the SCJGNN along with the log-sigmoid transfer function in the hidden layers along with fifteen numbers of neurons to solve the MDMPM. The accurateness and precision of the proposed SCJGNNs is observed through the comparison of obtained and source (Runge-Kutta) results, while the small calculated absolute error indicate the exactitude of designed framework based on the SCJGNNs. The reliability and consistency of the SCJGNNs is observed by using the process of correlation, histogram curves, regression, and function fitness.


A scale conjugate neural network learning process for the nonlinear malaria disease model

Multimodal emotion recognition implies the use of different resources and techniques for identifying and recognizing human emotions. A variety of data sources such as faces, speeches, voices, texts and others have to be processed simultaneously for this recognition task. However, most of the techniques, which are based mainly on Deep Learning, are trained using datasets designed and built in controlled conditions, making their applicability in real contexts with real conditions more difficult. For this reason, the aim of this work is to assess a set of in-the-wild datasets to show their strengths and weaknesses for multimodal emotion recognition. Four in-the-wild datasets are evaluated: AFEW, SFEW, MELD and AffWild2. A multimodal architecture previously designed is used to perform the evaluation and classical metrics such as accuracy and F1-Score are used to measure performance in training and to validate quantitative results. However, strengths and weaknesses of these datasets for various uses indicate that by themselves they are not appropriate for multimodal recognition due to their original purpose, e.g., face or speech recognition. Therefore, we recommend a combination of multiple datasets in order to obtain better results when new samples are being processed and a good balance in the number of samples by class.

/pdf/an-assessment-of-in-the-wild-datasets-for-multimodal-emotion-2vdim7qc.pdf

An Assessment of In-the-Wild Datasets for Multimodal Emotion Recognition

Abstract With massive population growth and a shift in the urban culture in smart cities, the constant generation of waste continues to create unsanitary living conditions for city dwellers. Overflowing solid waste in the garbage and the rapid generation of non-degradable solid waste produce a slew of infectious illnesses that proliferate throughout the ecosystem. Conventional solid waste management systems have proved to be increasingly harmful in densely populated areas like smart cities. Also, such systems require real-time manual monitoring of garbage, high labor costs, and constant maintenance. Monitoring waste management on a timely basis and reducing labor costs is scarcely possible, realistically, for a municipal corporation. A Smart Dustbin System (SDS) is proposed that is to be implemented in densely populated urban areas to ensure hygiene. This paper undertakes a comprehensive analysis of the application of smart dustbin systems, following an extensive literature review and a discussion of recent research that is expected to help improve waste management systems. A current SDS used in real-time is implemented with the most recent advances from deep learning, computer vision, and the Internet of Things. The smart dustbin system used in day-to-day life minimizes the overloading of bins, lowers labor costs, and saves energy and time. It also helps keep cities clean, lowering the risk of disease transmission. The primary users of the SDS are universities, malls, and high-rise buildings. The evolution of the SDS over the years with various features and technologies is well analyzed. The datasets used for Smart Waste Management and benchmark garbage image datasets are presented under AI perception. The results of the existing works are compared to highlight the potential limitations of these works. 

A survey of smart dustbin systems using the IoT and deep learning

Learning fusion feature representation for garbage image classification model in human–robot interaction

The development of robot technology is faster and faster with the continuous advancement of artificial intelligent technology, which plays a vital role in human lifestyle and social progress. This paper focuses on the application of the artificial potential field method in the case of static obstacles with unknown environmental information. The traditional artificial potential field method has the problem of local minimum. In order to solve this problem, the gravitational function and repulsive function in the traditional artificial potential field method are modified, and the concepts of safe distance and time distance detection method are added. It is proved by MATLAB simulation that in some cases, the modified artificial potential field method can bypass the local Minima and reach the destination smoothly.

Path planning of robot based on artificial potential field method

To address the problem of cooperative work among right-angle coordinate robots in spacecraft structural plate mount tasks, an improved particle swarm optimization (PSO) algorithm was proposed to assign paths to three robots in a surface-mounted technology (SMT) machine. First, the optimization objective of path planning was established by analyzing the working process of the SMT machine. Then, the inertia weight update strategy was designed to overcome the early convergence of the traditional PSO algorithm, and the learning factor of each particle was calculated using fuzzy control to improve the global search capability. To deal with the concentration phenomenon of particles in the iterative process, the genetic algorithm (GA) was introduced when the particles were similar. The particles were divided into elite, high-quality, or low-quality particles according to their performance. New particles were generated through selection and crossover operations to maintain the particle diversity. The performance of the proposed algorithm was verified with the simulation results, which could shorten the planning path and quicken the convergence compared to the traditional PSO or GA. For large and complex maps, the proposed algorithm shortens the path by 7.49% and 11.49% compared to traditional PSO algorithms, and by 3.98% and 4.02% compared to GA. 

Path Planning for Mount Robot Based on Improved Particle Swarm Optimization Algorithm

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Learning fusion feature representation for garbage image classification model in human–robot interaction

Path planning of robot based on artificial potential field method

Path Planning for Mount Robot Based on Improved Particle Swarm Optimization Algorithm