Capabilities Of Robots Research Articles

Design and analysis of an autonomous warehouse robot system with 6-DOF manipulator

With the increasing need for efficiency and accuracy in warehouse operations, the functions and market demands of automated warehouse robots are constantly increasing. This study presents the design, simulation, and implementation of a warehouse robot, showcasing effective automation solution. Leveraging the Robot Operating System (ROS) and Gazebo, a robot with a six-degree-of-freedom robotic arm for diverse manipulation tasks and a differential drive base for broad-spectrum navigation was designed. The simulation environment in Gazebo faithfully replicates real-world warehouse conditions, enabling comprehensive path planning and real-time modifications, powered by move_base. A camera sensor serves as the robot's safety system, designed to detect moving obstacles and initiate appropriate responses, contributing to the enhancement of warehouse safety standards. Simulation results demonstrate the robot's effectiveness in performing pick-and-place tasks while successfully navigating through the environment, indicating the significant potential for real-world warehouse automation applications. Therefore, this work provides a foundation reference for future research aimed at optimizing and expanding the capabilities of autonomous warehouse robots.

Multimodal Locomotion in a Soft Robot Through Hierarchical Actuation.

Soft and continuum robots present the opportunity for extremely large ranges of motion, which can enable dexterous, adaptive, and multimodal locomotion behaviors. However, as the number of degrees of freedom (DOF) of a robot increases, the number of actuators should also increase to achieve the full actuation potential. This presents a dilemma in mobile soft robot design: physical space and power requirements restrict the number and type of actuators available and may ultimately limit the movement capabilities of soft robots with high-DOF appendages. Restrictions on actuation of continuum appendages ultimately may limit the various movement capabilities of soft robots. In this work, we demonstrate multimodal behaviors in an underwater robot called "Hexapus." A hierarchical actuation design for multiappendage soft robots is presented in which a single high-power motor actuates all appendages for locomotion, while smaller low-power motors augment the shape of each appendage. The flexible appendages are designed to be capable of hyperextension for thrust, and flexion for grasping with a peak pullout force of 32 N. For propulsion, we incorporate an elastic membrane connected across the base of each tentacle, which is stretched slowly by the high-power motor and released rapidly through a slip-gear mechanism. Through this actuation arrangement, Hexapus is capable of underwater locomotion with low cost of transport (COT = 1.44 at 16.5 mm/s) while swimming and a variety of multimodal locomotion behaviors, including swimming, turning, grasping, and crawling, which we demonstrate in experiment.

Design and implementation of a field robot using a parallelogram mechanism.

To meet various industrial requirements such as ease of motion, scalability, and cost efficiency, it is necessary to innovate the design of robotic platforms. In this research, a novel approach, from mechanical design to control implementation, is introduced for launching a robotic platform using a parallelogram mechanism. First, a reverse engineering process is applied, progressing from kinematics to dynamics. Then, several mechanical computations are conducted to ensure the structural stability of the robot framework. Subsequently, the dynamic performance of the system is analyzed, focusing on the driving torque and moments in each link. Additionally, the electrical design and transfer function of each joint are identified to ensure practical execution. To validate the effectiveness and feasibility of the design, both numerical simulations and experimental tests are performed. Theoretical results show the dynamic response of the proposed method, particularly in terms of the driving moments of the robotic joints. In real-world tests, various trajectories, such as different rectangular paths, are demonstrated to showcase the robot's capabilities. From these results, it is clear that the proposed approach is both feasible and applicable in practical scenarios.

A Closed-Loop Shared Control Framework for Legged Robots

Shared control, as a combination of human and robot intelligence, has been deemed as a promising direction toward complementing the perception and learning capabilities of legged robots. However, previous works on human–robot control for legged robots are often limited to simple tasks, such as controlling movement direction, posture, or single-leg motion, yet extensive training of the operator is required. To facilitate the transfer of human intelligence to legged robots in unstructured environments, this article presents a user-friendly closed-loop shared control framework. The main novelty is that the operator only needs to make decisions based on the recommendations of the autonomous algorithm, without having to worry about operations or consider contact planning issues. Specifically, a rough navigation path from the operator is smoothed and optimized to generate a path with reduced traversing cost. The traversability of the generated path is assessed using fast Monte Carlo tree search, which is subsequently fed back through an intuitive image interface and force feedback to help the operator make decisions quickly, forming a closed-loop shared control. The simulation and hardware experiments on a hexapod robot show that the proposed framework gives full play to the advantages of human–machine collaboration and improves the performance in terms of learning time from the operator, mission completion time, and success rate than comparison methods.

An edge architecture for enabling autonomous aerial navigation with embedded collision avoidance through remote nonlinear model predictive control

In this article, we present an edge-based architecture for enhancing the autonomous capabilities of resource-constrained aerial robots by enabling a remote nonlinear model predictive control scheme, which can be computationally heavy to run on the aerial robots' onboard processors. The nonlinear model predictive control is used to control the trajectory of an unmanned aerial vehicle while detecting, and preventing potential collisions. The proposed edge architecture enables trajectory recalculation for resource-constrained unmanned aerial vehicles in relatively real-time, which will allow them to have fully autonomous behaviors. The architecture is implemented with a remote Kubernetes cluster on the edge side, and it is evaluated on an unmanned aerial vehicle as our controllable robot, while the robotic operating system is used for managing the source codes, and overall communication. With the utilization of edge computing and the architecture presented in this work, we can overcome computational limitations, that resource-constrained robots have, and provide or improve features that are essential for autonomous missions. At the same time, we can minimize the relative travel time delays for time-critical missions over the edge, in comparison to the cloud. We investigate the validity of this hypothesis by evaluating the system's behavior through a series of experiments by utilizing either the unmanned aerial vehicle or the edge resources for the collision avoidance mission.

Multistable States and Snap-Through Instabilities in an Interconnected Dielectric Elastomer Actuators System

Abstract Snap-through instabilities in single and interconnected dielectric elastomer actuators have demonstrated their potential in enabling many functionalities such as large deformation, high-speed actuation, and enhanced flowrates in fluid pumps. However, the nonlinear nature of dielectric elastomers and the complex interplay of mechanics in interconnected inflated actuators, make the modeling of such systems challenging. In this paper, we present a methodology to analytically model the instabilities in a system of three interconnected homogeneous spherical dielectric elastomer actuators through graphical and numerical approaches. The simulation results reveal the presence of a locus of initial stable states that the interconnected actuators can achieve at zero voltage. In specific loading conditions, the system exhibits multiple stable states which can be cyclically transitioned between by selectively applying voltage to specific actuators. In other conditions, the system may undergo two successive instabilities when the voltage applied to a single actuator in the system is increased monotonically. These results retrieve existing experimental results theoretically for the first time and identify a new behavior of cascading instabilities in inflated dielectric elastomer actuators. We hope this work will pave the way for programmable design of multistable systems to unravel new capabilities in dielectric elastomer actuators and soft robotics.

The Influence of Micro-Hexapod Walking-Induced Pose Changes on LiDAR-SLAM Mapping Performance.

Micro-hexapods, well-suited for navigating tight or uneven spaces and suitable for mass production, hold promise for exploration by robot groups, particularly in disaster scenarios. However, research on simultaneous localization and mapping (SLAM) for micro-hexapods has been lacking. Previous studies have not adequately addressed the development of SLAM systems considering changes in the body axis, and there is a lack of comparative evaluation with other movement mechanisms. This study aims to assess the influence of walking on SLAM capabilities in hexapod robots. Experiments were conducted using the same SLAM system and LiDAR on both a hexapod robot and crawler robot. The study compares map accuracy and LiDAR point cloud data through pattern matching. The experimental results reveal significant fluctuations in LiDAR point cloud data in hexapod robots due to changes in the body axis, leading to a decrease in map accuracy. In the future, the development of SLAM systems considering body axis changes is expected to be crucial for multi-legged robots like micro-hexapods. Therefore, we propose the implementation of a system that incorporates body axis changes during locomotion using inertial measurement units and similar sensors.

Multi-strategy ensemble Harris hawks optimization for smooth path planning of mobile robots

Efficient and safe path planning for autonomous navigation is paramount in advancing the motion control capabilities of mobile robots. To obtain the global optimal smooth path for mobile robots, a multi-strategy ensemble Harris hawks optimization algorithm (SDHHO) is proposed in this paper. The spiral search strategy is adopted to improve the early update method of the algorithm, which can improve the global exploration ability. To achieve better balance between global exploration and local exploitation, the Sine chaotic map is introduced to the escape energy, replacing random components. Furthermore, an elite differential mutation strategy combined with Gaussian mutation is designed to prevent the algorithm from falling into local optima. We compared the SDHHO algorithm with other classical and novel algorithms on 23 benchmark functions, and the results demonstrated the superiority of SDHHO. The proposed algorithm is applied to the smooth path planning for mobile robots, which is transformed into an optimization problem of control nodes of high-order Bezier curves. Empirical evaluations across diverse environments underscore the proficiency of the proposed method in generating paths characterized by reduced length and enhanced safety and continuity.

CGAN-Based Forest Scene 3D Reconstruction from a Single Image

Forest scene 3D reconstruction serves as the fundamental basis for crucial applications such as forest resource inventory, forestry 3D visualization, and the perceptual capabilities of intelligent forestry robots in operational environments. However, traditional 3D reconstruction methods like LiDAR present challenges primarily because of their lack of portability. Additionally, they encounter complexities related to feature point extraction and matching within multi-view stereo vision sensors. In this research, we propose a new method that not only reconstructs the forest environment but also performs a more detailed tree reconstruction in the scene using conditional generative adversarial networks (CGANs) based on a single RGB image. Firstly, we introduced a depth estimation network based on a CGAN. This network aims to reconstruct forest scenes from images and has demonstrated remarkable performance in accurately reconstructing intricate outdoor environments. Subsequently, we designed a new tree silhouette depth map to represent the tree’s shape as derived from the tree prediction network. This network aims to accomplish a detailed 3D reconstruction of individual trees masked by instance segmentation. Our approach underwent validation using the Cityscapes and Make3D outdoor datasets and exhibited exceptional performance compared with state-of-the-art methods, such as GCNDepth. It achieved a relative error as low as 8% (with an absolute error of 1.76 cm) in estimating diameter at breast height (DBH). Remarkably, our method outperforms existing approaches for single-image reconstruction. It stands as a cost-effective and user-friendly alternative to conventional forest survey methods like LiDAR and SFM techniques. The significance of our method lies in its contribution to technical support, enabling the efficient and detailed utilization of 3D forest scene reconstruction for various applications.

Unintended consequences of humanoid service robots: A case study of public service organizations

Robots guided by artificial intelligence (AI) are transforming service operations in for-profit companies. However, public and non-profit organizations have been slower to pursue these innovative new technologies. We conducted an in-depth single-case qualitative study of one public organization (a public library in Australia) to better understand the unintended consequences of humanoid service robots. Our findings theorize unintended consequences through dimensions of customer value and service interaction locus, yielding four types – service disparities, judgment-free communication, noise pollution and social touch. Our analysis reveals new insights into: 1) the unintended consequences of humanoid robots for customers; 2) the effects of these consequences on the service organization; and 3) the technology capabilities of humanoid robots as potential sources of the consequences. When public and non-profit organizations leverage these unintended consequences, they can use robots more effectively to improve the overall customer service experience.

Integration of Deep Learning and Collaborative Robot for Assembly Tasks

Human–robot collaboration has gained attention in the field of manufacturing and assembly tasks, necessitating the development of adaptable and user-friendly forms of interaction. To address this demand, collaborative robots (cobots) have emerged as a viable solution. Deep Learning has played a pivotal role in enhancing robot capabilities and facilitating their perception and understanding of the environment. This study proposes the integration of cobots and Deep Learning to assist users in assembly tasks such as part handover and storage. The proposed system includes an object classification system to categorize and store assembly elements, a voice recognition system to classify user commands, and a hand-tracking system for close interaction. Tests were conducted for each isolated system and for the complete application as used by different individuals, yielding an average accuracy of 91.25%. The integration of Deep Learning into cobot applications has significant potential for transforming industries, including manufacturing, healthcare, and assistive technologies. This work serves as a proof of concept for the use of several neural networks and a cobot in a collaborative task, demonstrating communication between the systems and proposing an evaluation approach for individual and integrated systems.

Into the ice: Exploration and data capturing in glacial moulins by a tethered robot

AbstractGlacial moulins (cylindrical meltwater drainage shafts) provide valuable insights into glacier dynamics, but are inaccessible and hazardous environments for humans to study. Exploring them using passive sensor probes has revealed their complex geometry, which has limited further exploration with passive sensor probes. To overcome these challenges, we propose a tethered robot capable of autonomously exploring and capturing data in glacial moulins. Our novel robot is equipped with a tether to support its motion. Combined with novel estimation and control algorithms, the tethered robot is able to safely and efficiently maneuver in confined, chimney‐like structures, such as moulins. Laboratory and field experiments confirm the feasibility of the proposed design, showing successful localization in environments with no access to positional measurements. Field trials on the Mer de Glace glacier demonstrate the robot's capabilities, descending into the largest moulin to depths of 25 m, capturing valuable scientific data. Onboard sensors are used to capture data to reconstruct the moulin's three‐dimensional geometry and two sampling mechanisms are presented and evaluated to extract samples from the icy surface of the moulin. Our results show promising potential for future exploration of moulins, demonstrating the effectiveness of our tethered robot for safely gathering data from these hazardous environments.

MTUW-GAN: A Multi-Teacher Knowledge Distillation Generative Adversarial Network for Underwater Image Enhancement

Underwater imagery is plagued by issues such as image blurring and color distortion, which significantly impede the detection and operational capabilities of underwater robots, specifically Autonomous Underwater Vehicles (AUVs). Previous approaches to image fusion or multi-scale feature fusion based on deep learning necessitated multi-branch image preprocessing prior to merging through fusion modules. However, these methods have intricate network structures and a high demand for computational resources, rendering them unsuitable for deployment on AUVs, which have limited resources at their disposal. To tackle these challenges, we propose a multi-teacher knowledge distillation GAN for underwater image enhancement (MTUW-GAN). Our approach entails multiple teacher networks instructing student networks simultaneously, enabling them to enhance color and detail in degraded images from various perspectives, thus achieving an image-fusion-level performance. Additionally, we employ middle layer channel distillation in conjunction with the attention mechanism to extract and transfer rich middle layer feature information from the teacher model to the student model. By eliminating multiplexed branching and fusion modules, our lightweight student model can directly generate enhanced underwater images through model compression. Furthermore, we introduce a multimodal objective enhancement function to refine the overall framework training, striking a balance between a low computational effort and high-quality image enhancement. Experimental results, obtained by comparing our method with existing approaches, demonstrate the clear advantages of our proposed method in terms of visual quality, model parameters, and real-time performance. Consequently, our method serves as an effective solution for real-time underwater image enhancement, specifically tailored for deployment on AUVs.

Event-triggered robot self-assessment to aid in autonomy adjustment.

Introduction: Human-robot teams are being called upon to accomplish increasingly complex tasks. During execution, the robot may operate at different levels of autonomy (LOAs), ranging from full robotic autonomy to full human control. For any number of reasons, such as changes in the robot's surroundings due to the complexities of operating in dynamic and uncertain environments, degradation and damage to the robot platform, or changes in tasking, adjusting the LOA during operations may be necessary to achieve desired mission outcomes. Thus, a critical challenge is understanding when and how the autonomy should be adjusted. Methods: We frame this problem with respect to the robot's capabilities and limitations, known as robot competency. With this framing, a robot could be granted a level of autonomy in line with its ability to operate with a high degree of competence. First, we propose a Model Quality Assessment metric, which indicates how (un)expected an autonomous robot's observations are compared to its model predictions. Next, we present an Event-Triggered Generalized Outcome Assessment (ET-GOA) algorithm that uses changes in the Model Quality Assessment above a threshold to selectively execute and report a high-level assessment of the robot's competency. We validated the Model Quality Assessment metric and the ET-GOA algorithm in both simulated and live robot navigation scenarios. Results: Our experiments found that the Model Quality Assessment was able to respond to unexpected observations. Additionally, our validation of the full ET-GOA algorithm explored how the computational cost and accuracy of the algorithm was impacted across several Model Quality triggering thresholds and with differing amounts of state perturbations. Discussion: Our experimental results combined with a human-in-the-loop demonstration show that Event-Triggered Generalized Outcome Assessment algorithm can facilitate informed autonomy-adjustment decisions based on a robot's task competency.

Advancing Human-Robot Collaboration: proposal of a methodology for the design of Symbiotic Assembly Workstations

The rapid advancement of robotics and artificial intelligence paves the way for collaborative robotics to revolutionise industrial operations. Collaborative robotics can bring significant improvements in productivity, efficiency, and safety. Recently, the concept of Symbiotic Human-Robot Collaboration (SHRC) has emerged within the field of collaborative robotics, emphasising the seamless and adaptive integration of human and robotic capabilities within a shared workspace to optimise overall production performance. However, existing collaborative systems frequently face difficulties in establishing dynamic relationships with humans and adapting to evolving conditions, ultimately restricting their overall potential. This paper investigates the essential features required for collaborative workstations to enable SHRC. Moreover, with the aim of fully harnessing the power of collaborative robotics, the paper introduces a design methodology that supports the development of symbiotic workstations for specific assembly operations. A real-world case study is provided, demonstrating the practical implementation and benefits of the proposed design approach.

An overview of cobots for advanced manufacturing: Human-robot interactions and research trends

Advanced manufacturing is currently undergoing a significant transition towards human-robot collaboration. This shift has attracted considerable attention from researchers seeking to harness the synergistic potential of human and robotic capabilities across a range of applications and tasks. This paper presents a comprehensive review of the classification of robots, considering key aspects like architecture, mobility, function, size, autonomy, and human contact. The study goes deeper into collaborative robots by emphasizing the levels of interaction between robots and humans. To provide a comprehensive overview, a meticulous analysis of scientific publications spanning a two-decade period is conducted, with a focus on data collected over the past decade. Furthermore, the analysis extrapolates main trends and developments to offer insights for the next decade, thereby facilitating a comparative assessment of publication rates about both collaborative and industrial robots during this timeframe.

Intelligent Fish-Inspired Foraging of Swarm Robots with Sub-Group Behaviors Based on Neurodynamic Models.

This paper proposes a novel intelligent approach to swarm robotics, drawing inspiration from the collective foraging behavior exhibited by fish schools. A bio-inspired neural network (BINN) and a self-organizing map (SOM) algorithm are used to enable the swarm to emulate fish-like behaviors such as collision-free navigation and dynamic sub-group formation. The swarm robots are designed to adaptively reconfigure their movements in response to environmental changes, mimicking the flexibility and robustness of fish foraging patterns. The simulation results show that the proposed approach demonstrates improved cooperation, efficiency, and adaptability in various scenarios. The proposed approach shows significant strides in the field of swarm robotics by successfully implementing fish-inspired foraging strategies. The integration of neurodynamic models with swarm intelligence not only enhances the autonomous capabilities of individual robots, but also improves the collective efficiency of the swarm robots.

Analysis of the workpiece orientation in a robot’s workspace for milling operations

The demand for flexible manufacturing methods is constantly increasing. Large, stable robots are associated with high acquisition costs that companies might not afford especially in uncertain times. Consequently, the capabilities of small and inexpensive robots have to be fully utilized. To perform milling tasks in a stable way, the judicious selection of a machining area in the robot’s workspace and the orientation is mandatory. In this paper, the influence of workpiece orientation in the robot workspace is investigated by combining a sensitivity analysis and a reachability analysis. The result is the robot’s stable sub-workspace where the orientation tests are performed.

Research on optimal path planning technology for industrial robots based on improved IDE algorithm

Abstract Realizing efficient and accurate positional movement of industrial robots is one of the key technologies to accelerate industrial development. In this paper, a four-wheel-drive McNamee mobile robot is selected as the research object, and the improved IPSO-IDE algorithm is proposed, which is combined with a PID controller to realize the accurate positioning and optimal path planning of industrial machines. The experimental results of robot kinematic parameter calibration show that the distance error of the robot calibrated with the algorithm is greatly reduced, and not only that, the IPSO-IDE algorithm reduces the average distance error of the robot to only 0.124 mm, which is much smaller than that of the PSO algorithm and the DE algorithm. The IPSO-IDE algorithm is capable of planning the optimal motion path in a short time, as demonstrated by the results of robot map environment simulation experiments. In the simulated map environment of 10 m × 10 m, the convergence speed of the algorithm’s planning path is 12.69 seconds, and the generated optimal path length is 15.0221 m, which is better than other algorithms. In conclusion, the proposed IPSO-IDE algorithm has an excellent performance in terms of path-planning capabilities for industrial robots.

Enhancing Robotic Navigation in Dynamic Environments

Robotic navigation in dynamic environments represents a significant challenge and opportunity in the field of robotics, with profound implications for applications ranging from autonomous vehicles to service robots in humancentric spaces. This paper addresses the critical need for advanced navigation strategies that enable robots to operate efficiently and safely in environments characterized by continuous change and unpredictability. Traditional navigation methods, which rely heavily on static maps and predefined paths, often fall short in dynamic settings where obstacles and pathways can shift rapidly. Consequently, there is a pressing need for innovative approaches that combine robust sensing, real-time data processing, and adaptive decision-making to enhance robotic navigation capabilities. To tackle these challenges, we propose a comprehensive framework that integrates several cutting-edge technologies and methodologies. At the core of our approach is the use of advanced sensor fusion techniques, which combine data from multiple sensors, including LiDAR, cameras, and ultrasonic sensors, to create a detailed and dynamic representation of the robot's surroundings. This multi-modal sensing approach ensures that the robot can detect and respond to changes in the environment with high accuracy and reliabiality. Complementing the sensor fusion process is the implementation of realtime data processing algorithms, powered by machine learning and artificial intelligence. These algorithms enable the robot to analyze vast amounts of sensory data on-the-fly, recognizing patterns, predicting potential obstacles, and making informed navigation decisions in real-time. Machine learning models are trained using extensive datasets that include a variety of dynamic scenarios, ensuring that the robot can generalize from past experiences and adapt to new situations effectively. A key component of our framework is the development of adaptive path planning algorithms that can dynamically adjust the robot's trajectory in response to environmental changes. These algorithms leverage techniques from both classical robotics and modern AI, including probabilistic roadmaps, rapidly-exploring random trees (RRT), and deep reinforcement learning. By continuously updating the robot's path based on real-time sensory input, these adaptive planning methods ensure that the robot can navigate efficiently and avoid collisions even in highly dynamic environments. In addition to the technical advancements, our framework also emphasizes the importance of human-robot interaction (HRI) in dynamic environments. Effective HRI mechanisms are crucial for scenarios where robots operate alongside humans, such as in healthcare, hospitality, and collaborative manufacturing. We incorporate natural language processing (NLP) and gesture recognition systems to facilitate intuitive communication between humans and robots, allowing for seamless cooperation and enhanced safety. To validate our proposed framework, we conducted extensive experiments in a variety of dynamic settings, including urban environments, industrial warehouses, and public spaces. The results demonstrate significant improvements in navigation efficiency, collision avoidance, and overall operational safety compared to traditional methods. Our framework not only enhances the robot's ability to navigate dynamic