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Path planning is a prevalent process that helps mobile robots find the most efficient pathway from the starting position to the goal position to avoid collisions with obstacles. In this paper, we propose a novel path planning algorithm–Intermediary RRT∗-PSO-by utilizing the exploring speed advantages of Rapidly exploring Random Trees and using its solution to feed to a metaheuristic-based optimizer, Particle swarm optimization (PSO), for fine-tuning and enhancement. In Phase 1, the start and goal trees are initialized at the starting and goal positions, respectively, and the intermediary tree is initialized at a random unexplored region of the search space. The trees were grown until one met the other and then merged and re-initialized in other unexplored regions. If the start and goal trees merge, the first solution is found and passed through a minimization process to reduce unnecessary nodes. Phase 2 begins by feeding the minimized solution from Phase 1 as the global best particle of PSO to optimize the path. After simulating two special benchmark configurations and six practice configurations with special cases, the results of the study concluded that the proposed method is capable of handling small to large, simple to complex continuous environments, whereas it was very tedious for the previous method to achieve. [ABSTRACT FROM AUTHOR]

In recent years, the COVID-19 pandemic has negatively impacted all aspects of social life. Due to ease in the infected method, i.e., through small liquid particles from the mouth or the nose when people cough, sneeze, speak, sing, or breathe, the virus can quickly spread and create severe problems for people’s health. According to some research as well as World Health Organization (WHO) recommendation, one of the most economical and effective methods to prevent the spread of the pandemic is to ask people to wear the face mask in the public space. A face mask will help prevent the droplet and aerosol from person to person to reduce the risk of virus infection. This simple method can reduce up to 95% of the spread of the particles. However, this solution depends heavily on social consciousness, which is sometimes unstable. In order to improve the effectiveness of wearing face masks in public spaces, this research proposes an approach for detecting and warning a person who does not wear or misuse the face mask. The approach uses the deep learning technique that relies on GoogleNet, AlexNet, and VGG16 models. The results are synthesized by an ensemble method, i.e., the bagging technique. From the experimental results, the approach represents a more than 95% accuracy of face mask recognition. [ABSTRACT FROM AUTHOR]