Robot navigation in crowded environments has recently benefited from advances in deep reinforcement learning (DRL) approaches. However, it still presents a challenge to designing socially compliant robot behavior. Avoiding collisions and the difficulty of predicting human behavior are crucial and challenging tasks while the robot navigates in a congested social environment. To address this issue, this study proposes a dynamic warning zone that creates a circular sector around humans based on the step length and speed of humans. To properly comprehend human behavior and keep a safe distance between the robot and the humans, warning zones are implemented during the robot’s training using deep enforcement learning techniques. In addition, a short-distance goal is established to help the robot efficiently reach the goal through a reward function that penalizes it for going away from the goal and rewards it for advancing towards it. The proposed model is tested on three state-of-the-art methods: collision avoidance with deep reinforcement learning (CADRL) , long short-term memory (LSTM-RL), and social attention with reinforcement learning (SARL). The suggested method is tested in the Gazebo simulator and the real world with a robot operating system (ROS) in three scenarios. The first scenario involves a robot attempting to reach a goal in free space. The second scenario uses static obstacles, and the third involves humans. The experimental results demonstrate that the model performs better than previous methods and leads to safe navigation in an efficient time.

The evolution of learning techniques has led robotics to have a considerable influence in industrial and household applications. With the progress in technology revolution, the demand for service robots is rapidly growing and extends to many applications. However, efficient navigation of service robots in crowded environments, with unpredictable human behaviors, is still challenging. The robot is supposed to recognize surrounding information while navigating, and then act accordingly. To address this issue, the proposed method crowd Aware Memory-based Reinforcement Learning (CAM-RL) uses gated recurrent units to store the relative dependencies among the crowd, and utilizes the human–robot interactions in the reinforcement learning framework for collision-free navigation. The proposed method is compared with the state-of-the-art techniques of multi-agent navigation, such as Collision Avoidance with Deep Reinforcement Learning (CADRL), Long Short-Term Memory Reinforcement Learning (LSTM-RL) and Social Attention Reinforcement Learning (SARL). Experimental results show that the proposed method can identify and learn human–robot interactions more extensively and efficiently than above-mentioned methods while navigating in a crowded environment. The proposed method achieved a success rate of greater than or equal to $$99\%$$99 % and a collision rate of less than or equal to $$1\%$$1 % in all test case scenarios, which is better compared to the previously proposed methods.

Shape from Focus (SFF) has been studied extensively in computer vision for 3D shape and depth recovery. The first stage in SFF methods is to compute the focus value of every pixel by converting the colored images into gray scale and then apply the focus measure operator. Converting colored values in the images into gray scale values may lead to imprecise mapping of pixels with different colored values onto the same gray scale value, this affects the overall accuracy of the system. In a colored image, the focused pixels maintain a considerable color difference from their neighboring pixels as compared to the defocused ones, which are blended into their neighborhood. This article presents an alternative method to measure the degree of focus by directly processing colored images. The color differences of the neighbor pixels with respect to the central pixel are obtained and summed together, this is followed by calculating their spread. The sum and the spread are combined to measure the degree of focus of the pixel in consideration. The proposed focus measure is then used for shape recovery of various simulated and real objects and is compared with previous techniques. The comparison results show the proposed method has the highest correlation and smallest RMSE values confirming the effectiveness of using color images for shape recovery.

Shape from Focus (SFF) is one of the passive techniques to recover the shape of an object under consideration. It utilizes the focus cue present in the stack of images, obtained by a single camera. In SFF when the images are acquired, the inter-frame distance, also known as the sampling step size, is assumed to be constant. However, in practice, due to mechanical constraints, sampling step size cannot remain constant. The inconsistency in the sampling step size causes the problem of jitter, and produces Jitter noise in focus curves. This Jitter noise is not visible in images, because each pixel in an image (of the stack) will be subjected to the same error in focus. Thus, traditional image denoising techniques will not work. This paper formulates a model of the Jitter noise, followed by the design of system and measurement models for Kalman filter. Then, the jittering problem for SFF systems is solved using the proposed filtering technique. Experiments are performed on simulated and real objects. Ten noise levels are considered for simulated, and four for real objects. RMSE and Correlation are used to measure the reconstructed shape. The results show the effectiveness of the proposed scheme.

Ensuring food security has become of paramount importance due to the rising global population. In particular, the agriculture sector in South Korea faces several challenges such as an aging farming population and a decline in the labor force. These issues have led to the recognition of smart farms as a potential solution. In South Korea, the smart farm is divided into three generations. The first generation primarily concentrates on monitoring and controlling precise cultivation environments by leveraging information and communication technologies (ICT). This is aimed at enhancing convenience for farmers. Moving on to the second generation, it takes advantage of big data and artificial intelligence (AI) to achieve improved productivity. This is achieved through precise cultivation management and automated control of various farming processes. The most advanced level is the 3rd generation, which represents an intelligent robotic farm. In this stage, the entire farming process is autonomously managed without the need for human intervention. This is made possible through energy management systems and the use of robots for various farm operations. However, in the current Korean context, the adoption of smart farms is primarily limited to the first generation, resulting in the limited utilization of advanced technologies such as AI, big data, and cloud computing. Therefore, this research aims to develop the second generation of smart farms within the first generation smart farm environment. To accomplish this, data was collected from nine sensors spanning the period between 20 June to 30 September. Following that, we conducted kernel density estimation analysis, data analysis, and correlation heatmap analysis based on the collected data. Subsequently, we utilized LSTM, BI-LSTM, and GRU as base models to construct a stacking ensemble model. To assess the performance of the proposed model based on the analyzed results, we utilized LSTM, BI-LSTM, and GRU as the existing models. As a result, the stacking ensemble model outperformed LSTM, BI-LSTM, and GRU in all performance metrics for predicting one of the sensor data variables, air temperature. However, this study collected nine sensor data over a relatively short period of three months. Therefore, there is a limitation in terms of considering the long-term data collection and analysis that accounts for the unique seasonal characteristics of Korea. Additionally, the challenge of including various environmental factors influencing crops beyond the nine sensors and conducting experiments in diverse cultivation environments with different crops for model generalization remains. In the future, we plan to address these limitations by extending the data collection period, acquiring diverse additional sensor data, and conducting further research that considers various environmental variables.