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"Autonomous Robots"
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Sensory systems for robotic applications
Thanks to advances in sensing and computer vision technologies, robots can be found today in healthcare, medicine and the industry. Topics covered in this edited book include various types of sensors used in robotics, sensing schemes, sensing technologies and their applications including robotics, prosthetics, wearables and healthcare. Written for those working in robotics, sensor technologies and electronics, and their applications in robotics, haptics, prosthetics, wearable and interactive systems, cognitive engineering, neuro-engineering, computational neuroscience, medicine, and healthcare technologies.
BOOK
A long-term localization and mapping system for autonomous inspection robots in large-scale environments using 3D LiDAR sensors
Inspection mobile robots equipped with 3D LiDAR sensors are now widely used in substations and other critical circumstances. However, the application of traditional LiDAR sensors is restricted in large-scale environments. Prolonged operation poses the risk of sensor degradation, while the presence of dynamic objects disrupts the stability of the constructed map, consequently impacting the accuracy of robot localization. To address these challenges, we propose a 3D LiDAR-based long-term localization and map maintenance system, enabling autonomous deployment and operation of inspection robots. The whole system is composed of three key subsystems: a hierarchical SLAM system, a global localization system, and a map maintenance system. The SLAM subsystem includes Local Map Representation, LiDAR Odometry, Global Map Formulation and Optimization, and Dense Map Generation. Specifically, we construct an efficient map representation that voxelizes only the occupied space and computes local geometry within each voxel. The design of LiDAR Odometry ensures high consistency with this map representation mechanism. Then, to address drift errors, we formulate the global map as a graph of local submaps that undergo global optimization. Furthermore, we utilize marching cubes to generate a mesh model of the map. Our system outperforms the state-of-the-art LiDAR odometry method, LOAM, reducing average absolute position error by 30 % and 38 % on two public datasets. The comparative evaluation highlights the system’s superior accuracy and robustness, and demonstrates its high SLAM ranking in real-world scenarios. For global localization, we propose a novel ScanContext-ICP method, which integrates our improved ScanContext method, termed ScanContext++, for place recognition and global pose initialization. The Iterative Closest Point (ICP) algorithm is then employed for precise point cloud alignment and pose refinement, enabling the recovery of the robot’s position on the offline map when localization is lost. Finally, the map maintenance system tracks environmental changes, distinguishing stable features from dynamic ones. The system assigns higher weight to stable voxels, thereby improving localization accuracy. Furthermore, our time distribution mechanism refines map updates by filtering unstable points through temporal and segment-level analysis, which further enhances map maintenance. We conduct extensive experiments on public datasets to validate our system. The experimental results demonstrate that our system is effective and can be deployed on inspection mobile robots.
Journal Article
Human activity-aware coverage path planning for robot-based mosquito control
Automating mosquito control is a pivotal advancement in the pest control industry with the primary objective of mitigating the prevalence of vector-borne diseases. Recent progress in pest control robotics has enabled the automation of mosquito activity restrictions. However, existing robotic solutions have exhibited limitations in effectively addressing mosquito control while lacking a sensitive strategy for maximizing area coverage with crowded areas as a priority. In response to these challenges, this article proposes a novel human-first approach for complete coverage path planning (HFA-CCPP) that leverages the Glasius Bio-inspired Neural Network (GBNN) to cover areas that simulate and consider human activity patterns systematically. In this study, a mosquito-capturing robot, Dragonfly, is demonstrated with HFA-CCPP. This article provides an in-depth exploration of the technical intricacies of the proposed solution. The efficacy of the proposed technique is evaluated in terms of total area coverage and times taken to cover the high human activity region in simulation and real-world environments by comparing results with traditional GBNN. Across all scenarios, the proposed HFA-CCPP surpasses the traditional method by delivering efficient area coverage with minimal time for human-dense area coverage and efficiency in mosquito trapping. This finding stands as a newfound direction in automated mosquito control, holding great potential for curbing vector-borne diseases.
Journal Article
A robotic framework for the mobile manipulator : theory and application
\"This book helps readers visualize an end-to-end workflow for making a robot system work in a targeted environment. It is considered as a bridge from theories to real products, in which robotic software modules and the robotic system integration are mainly concerned\"-- Provided by publisher.
Book
IoT-Integrated robotic system for automated plant disease detection and environmental monitoring
Plant diseases pose a critical threat to global food security, agricultural sustainability, and farmer livelihoods, particularly in regions with limited access to advanced diagnostic technologies. Traditional methods of disease detection rely heavily on manual inspection, which is time-consuming, error-prone, and often results in delayed interventions. This paper presents a novel, solar-powered autonomous robotic system designed to detect plant diseases in real time using deep learning and IoT technologies. The proposed system integrates a high-resolution imaging unit, IoT-based environmental sensors, and an onboard processing module based on Raspberry Pi. Deep CNNs, trained on diverse datasets including PlantVillage, are used for accurate disease classification, while soil moisture and temperature sensors provide contextual environmental data to support diagnosis. The robot’s mobility, powered by solar energy, allows for continuous field monitoring with minimal human intervention. Experimental results demonstrate the system’s high classification performance, achieving 99.39% training accuracy, 97.47% validation accuracy, and 97.13% testing accuracy. Furthermore, the model achieved 99.63% overall accuracy, with a Precision of 99.40%, a Recall/Sensitivity of 99.56%, an F1-score of 99.46%, and a Specificity of 99.99% across multiple disease classes. These results highlight the robustness of the proposed approach in real-world agricultural conditions, enabling reliable disease detection and monitoring. The integration of cloud-based monitoring enables farmers to receive real-time alerts and insights, supporting timely and informed decision-making. This cost-effective, scalable, and environmentally sustainable solution has the potential to transform precision agriculture by enhancing early disease detection, reducing pesticide overuse, and improving crop yield and health.
Journal Article
Swarm Crawler Robots Using Lévy Flight for Targets Exploration in Large Environments
This study tackles the task of swarm robotics, where robots explore the environment to detect targets. When a robot detects a target, the robot must be connected with a base station via intermediate relay robots for wireless communication. Our previous results confirmed that Lévy flight outperformed the usual random walk for exploration strategy in an indoor environment. This paper investigated the search performance of swarm crawler robots with Lévy flight on target detection problems in large environments through a series of real robots’ experiments. The results suggest that the swarm crawler robots with Lévy flight succeeded in the target’s discovery in the indoor environment with a 100% success rate, and were able to find several targets in a given time in the outdoor environment. Thus, we confirmed that target exploration in a large environment would be possible by crawler robots with Lévy flight and significant variances in the detection rate among the positions to detect the outdoor environment’s target.
Journal Article
Robot programming : a guide to controlling autonomous robots
A beginner's guide to programming and automating modern robots. Drawing on their experience teaching thousands of robotics beginners, Cameron and Tracy Hughes show how to automate robots (or teams of robots), translating your ideas into specific tasks they can perform on their own, with no remote controls.
Book
The Effect of Emotions and Social Behavior on Performance in a Collaborative Serious Game Between Humans and Autonomous Robots
The aim of this paper is to investigate performance in a collaborative human–robot interaction on a shared serious game task. Furthermore, the effect of elicited emotions and perceived social behavior categories on players’ performance will be investigated. The participants collaboratively played a turn-taking version of the Tower of Hanoi serious game, together with the human and robot collaborators. The elicited emotions were analyzed in regards to the arousal and valence variables, computed from the Geneva Emotion Wheel questionnaire. Moreover, the perceived social behavior categories were obtained from analyzing and grouping replies to the Interactive Experiences and Trust and Respect questionnaires. It was found that the results did not show a statistically significant difference in participants’ performance between the human or robot collaborators. Moreover, all of the collaborators elicited similar emotions, where the human collaborator was perceived as more credible and socially present than the robot one. It is suggested that using robot collaborators might be as efficient as using human ones, in the context of serious game collaborative tasks.
Journal Article