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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. [ABSTRACT FROM AUTHOR]

Mobile service robots experience excessive vibrations when travelling over uneven surfaces in their workspace, increasing the degradation rate of the mechanical components or disrupting the robot's sensing abilities for proper localization and navigational capabilities. Robot inclusivity principles can determine the suitability of a site for robot performance by considering the ground's unevenness. This paper proposes a novel framework to autonomously evaluate the Robot Inclusivity Level of buildings based on surface unevenness (RIL-SU) by quantifying the surface unevenness of floor surfaces. The surface unevenness values are converted to RIL-SU using a rule-based approach, and the corresponding RIL-SU is tagged to the map location. A coloured heatmap based on the RIL-SU values is created as a visual representation of the RIL-SU of a given space. This heatmap would be useful for modifying the environment to make it more robot-friendly or restrict the robot's operation in certain areas to avoid possible robot failures. The experimental results show that the proposed framework can successfully generate a valid RIL-SU heatmap for building environments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Regular exercise plays a crucial role in promoting overall well-being in today's lifestyle. However, individuals often find it challenging to properly execute exercises, including maintaining correct postures and appropriate movement speeds. Robotic companions have emerged as potential solutions to assist and motivate users during exercise sessions. This research paper proposes a novel robot companion designed for exercise scenarios using a reconfigurable robot. In contrast to existing non-reconfigurable robotic companions, the use of a reconfigurable robot provides added flexibility in generating emotions. The system incorporates a module that utilizes fuzzy logic to evaluate the correctness of exercise performance based on posture variations and movement speeds. The robot generates emotions and provides feedback to users based on the exercise correctness score. The robot expresses emotions through reconfigurations, motion patterns, and variations in robot speed. This emotion-based feedback could be helpful for creating engaging and interactive exercise experiences. Apart from emotion generation, the robot utilizes vocal cues as feedback. Experimental results validate the effectiveness of the proposed system in evaluating exercise correctness and demonstrating meaningful emotion transitions. The findings of this work contribute to the development of innovative robotic companions for improving exercise adherence and overall well-being. [ABSTRACT FROM AUTHOR]

Reconfigurable robots are deployed for applications demanding area coverage, such as cleaning and inspections. Reconfiguration per context, considering beyond a small set of predefined shapes, is crucial for area coverage performance. However, the existing area coverage methods of reconfigurable robots are not always effective and require improvements for ascertaining the intended goal. Therefore, this article proposes a novel coverage strategy based on internal rehearsals to improve the area coverage performance of a reconfigurable robot. In this regard, a reconfigurable robot is embodied with the cognitive ability to predict the outcomes of its actions before executing them. A genetic algorithm uses the results of the internal rehearsals to determine a set of the robot's coverage parameters, including positioning, heading, and reconfiguration, to maximize coverage in an obstacle cluster encountered by the robot. The experimental results confirm that the proposed method can significantly improve the area coverage performance of a reconfigurable robot. [ABSTRACT FROM AUTHOR]