Mobile robots have emerged as versatile substitutes for human labor across diverse domains, offering promising applications in surveillance, healthcare, and beyond. Fundamental to their autonomy are the core capabilities of movement, perception, cognition, and navigation. This research introduces a novel approach known as the Dual PID based low-cost navigation system (DPLNS), designed specifically for indoor warehouse-like environments. The primary objective of this technique is to enable seamless point-to-point traversal. This is achieved through a fusion of gyroscope correction and visual PID control mechanisms. Leveraging a strategically positioned eagle-eye perspective camera, the system gained crucial insights for navigation. To ensure the uninterrupted execution of planned trajectories, the system employs the Message Queuing Telemetry Transport (MQTT) protocol. This technology ensures smooth communication and coordination of actions. The experimental validation of the proposed strategy highlights its efficacy, positioning it as a promising solution for modern warehouse automation needs. By advancing the capabilities of mobile robots within warehouse contexts, this research not only addresses immediate operational demands, but also paves the way for broader applications across various other fields. The developed system enables robust detection and successful path traversal to the designated spot through a position control operating at average of 12.3 rad/s.

Landscape maintenance is essential for ensuring agricultural productivity, promoting sustainable land use, and preserving soil and ecosystem health. Pruning is a labor-intensive task among landscaping applications that often involves repetitive pruning operations. To address these limitations, this paper presents the development of a dual-arm holonomic robot (called the KOALA robot) for precision plant pruning. The robot utilizes a cross-functionality sensor fusion approach, combining light detection and ranging (LiDAR) sensor and depth camera data for plant recognition and isolating the data points that require pruning. The You Only Look Once v8 (YOLOv8) object detection model powers the plant detection algorithm, achieving a 98.5% pruning plant detection rate and a 95% pruning accuracy using camera, depth sensor, and LiDAR data. The fused data allows the robot to identify the target boxwood plants, assess the density of the pruning area, and optimize the pruning path. The robot operates at a pruning speed of 10–50 cm/s and has a maximum robot travel speed of 0.5 m/s, with the ability to perform up to 4 h of pruning. The robot’s base can lift 400 kg, ensuring stability and versatility for multiple applications. The findings demonstrate the robot’s potential to significantly enhance efficiency, reduce labor requirements, and improve landscape maintenance precision compared to those of traditional manual methods. This paves the way for further advancements in automating repetitive tasks within landscaping applications.

Effective pest management in urban areas is critically challenged by the rapid proliferation of mosquito breeding sites. Traditional fumigation methods expose human operators to harmful chemicals, posing significant health risks ranging from respiratory problems to long-term chronic conditions. To address these issues, a novel fumigation robot equipped with sensor fusion technology for optimal pest control in urban landscapes is proposed. The proposed robot utilizes light detection and ranging data, depth camera inputs processed through the You Only Look Once version 8 (YOLOv8) algorithm for precise object recognition, and inertial measurement unit data. These technologies allow the robot to accurately identify and localize mosquito breeding hotspots using YOLOv8, achieving a precision of 0.81 and a mean average precision of 0.74. The integration of these advanced sensor technologies allows for detailed and reliable mapping, enhancing the robot’s navigation through complex urban terrains and ensuring precise targeting of fumigation efforts. In a test case, the robot demonstrated a 62.5% increase in efficiency by significantly reducing chemical usage through targeted hotspot fumigation. By automating the detection and treatment of breeding sites, the proposed method boosts the efficiency and effectiveness of pest management operations and significantly diminishes the health risks associated with chemical exposure for human workers. This approach, featuring real-time object recognition and dynamic adaptation to environmental changes, represents a substantial advancement in urban pest management, offering a safer and more effective solution to a persistent public health issue.

Doors are part of the building infrastructure that mobile robots have to pass through to reach zones on the other side. If robots were to clear these obstacles, they would require human assistance, advanced end-effectors, and complex control systems, making it challenging for robots. Therefore, a robot deployed in an environment should be capable of minimizing the passing through doors as well as path distance to improve overall efficiency. This paper proposes a novel Door-Density-Aware (DDA) path planning method. A vision-based door-detecting framework based on YOLOv8 has been developed to tag the door locations in a robot’s navigation map. The proposed DDA path planner uses a door-tagged map to plan an efficient path considering the cost of moving through doors and the path distance. Genetic Algorithm (GA) and Gray Wolf Optimization (GWO) have been considered for solving this optimization problem. According to the experimental results, the proposed method can effectively detect and tag doors in the navigation map and plan efficient paths. The proposed DDA path planner with GA produced the path plan with the least cost compared to other approaches.

Robotics and artificial intelligence have led to a significant improvement in the automation of various industrial processes. 6D pose estimation for industrial robotic arms refers to the process of accurately determining the position and orientation of an object in 3D space relative to a robotic arm, typically in an industrial or manufacturing context. This is important for tasks such as object recognition, grasping, and manipulation, where the robot needs to know the precise position and orientation of the object in order to perform its task but the accuracy of 6D pose estimation remains a challenge. To address this, researchers created deep learning algorithms that assess the posture of objects. However, these techniques still face significant challenges in real-world industrial applications, such as lighting conditions, occlusions, and the large number of objects that need to be recognized in a short amount of time. This research aims to present an efficient method of 6D pose estimation for texture less objects. Synthetic data generation provides better generalization of the real-world data sources, making it more time and cost-effective than collecting real-world data. The major challenge is bridging the reality gap between virtual and real environments, and the data must be photorealistic for it to be used in a real time model. Domain randomization and powerful simulators are employed to replicate a real environment in order to solve these issues.