Adaptive Robot Research Articles

Stretchable Shape Sensing and Computation for General Shape-Changing Robots

An ideal robot could autonomously complete diverse tasks such as ocean surveying, kitchen cleaning, and aerial environmental monitoring. However, robots optimized for each task typically have different shapes, posing a challenge in reconciling form and function. This challenge inspires the pursuit of general shape-changing robots (GSCRs). While soft materials and actuators are promising for GSCRs due to their ability to accommodate extreme deformations, there is a gap between the vision of GSCRs and the simple examples we see today. Two critical components are needed: robot-agnostic stretchable shape sensing and stretchable computing. Together, these components would enable closed-loop shape control and the first instantiations of GSCRs. This review aims to consolidate the literature on these components, encouraging researchers to bridge the gap between today's shape-changing robots and the envisioned GSCRs, ultimately advancing the field toward more versatile and adaptive robots.

A Review on Recent Trends of Bioinspired Soft Robotics: Actuators, Control Methods, Materials Selection, Sensors, Challenges, and Future Prospects

Bioinspired soft robotics is an emerging field that aims to develop flexible and adaptive robots inspired by the movement and capabilities of biological organisms. This review article examines recent advances in materials, actuation mechanisms, sensors, and control strategies and discusses the challenges and future prospects of bioinspired soft robotics. Key innovations highlighted include pneumatic, elastomer actuators, variable‐length shape memory alloy tendons, closed‐loop control with soft sensors, and the incorporation of soft materials including shape memory polymers and conductive composites. Challenges in soft robotics such as achieving complex motion control, incorporating feedback systems, modeling soft material dynamics, and replicating biological muscle efficiency with artificial muscles are also discussed. Promising future directions are explored including the integration of biodegradable materials, machine learning‐based control algorithms, and leveraging data‐driven techniques for modeling and control. Building on progress in multi‐functional materials, manufacturing techniques, and bioinspired design principles, soft robots hold considerable promise for expanding robot capabilities, enhancing versatility and adaptability, enabling applications from wearable assistive devices to search and rescue operations. This review provides a holistic perspective encompassing key drivers propelling innovations in the vibrant field of bioinspired soft robotics.

Development of a bionic hexapod robot with adaptive gait and clearance for enhanced agricultural field scouting.

High agility, maneuverability, and payload capacity, combined with small footprints, make legged robots well-suited for precision agriculture applications. In this study, we introduce a novel bionic hexapod robot designed for agricultural applications to address the limitations of traditional wheeled and aerial robots. The robot features a terrain-adaptive gait and adjustable clearance to ensure stability and robustness over various terrains and obstacles. Equipped with a high-precision Inertial Measurement Unit (IMU), the robot is able to monitor its attitude in real time to maintain balance. To enhance obstacle detection and self-navigation capabilities, we have designed an advanced version of the robot equipped with an optional advanced sensing system. This advanced version includes LiDAR, stereo cameras, and distance sensors to enable obstacle detection and self-navigation capabilities. We have tested the standard version of the robot under different ground conditions, including hard concrete floors, rugged grass, slopes, and uneven field with obstacles. The robot maintains good stability with pitch angle fluctuations ranging from -11.5° to 8.6° in all conditions and can walk on slopes with gradients up to 17°. These trials demonstrated the robot's adaptability to complex field environments and validated its ability to maintain stability and efficiency. In addition, the terrain-adaptive algorithm is more energy efficient than traditional obstacle avoidance algorithms, reducing energy consumption by 14.4% for each obstacle crossed. Combined with its flexible and lightweight design, our robot shows significant potential in improving agricultural practices by increasing efficiency, lowering labor costs, and enhancing sustainability. In our future work, we will further develop the robot's energy efficiency, durability in various environmental conditions, and compatibility with different crops and farming methods.

Adaptive robot localization in dynamic environments through self-learnt long-term 3D stable points segmentation

In field robotics, particularly in the agricultural sector, precise localization presents a challenge due to the constantly changing nature of the environment. Simultaneous Localization and Mapping algorithms can provide an effective estimation of a robot’s position, but their long-term performance may be impacted by false data associations. Additionally, alternative strategies such as the use of RTK-GPS can also have limitations, such as dependence on external infrastructure. To address these challenges, this paper introduces a novel stability scan filter. This filter can learn and infer the motion status of objects in the environment, allowing it to identify the most stable objects and use them as landmarks for robust robot localization in a continuously changing environment. The proposed method involves an unsupervised point-wise labelling of LiDAR frames by utilizing temporal observations of the environment, as well as a regression network, called Long-Term Stability Network (LTS-NET) to learn and infer 3D LiDAR points long-term motion status. Experiments demonstrate the ability of the stability scan filter to infer the motion stability of objects on a real agricultural long-term dataset. Results show that by only utilizing points belonging to long-term stable objects, the localization system exhibits reliable and robust localization performance for long-term missions compared to using the entire LiDAR frame points.

Interactive robots for personalised multimodal comedy experiments

This research proposes a novel system for personalising humour delivery using interactive robots with AI, natural language processing, and human-robot interaction technologies. The robots can adapt the multimodal content of jokes tactically now and strategically over time based on recording and analyzing each user’s subjective emotional reactions. The study evaluated pragmatic aspects of timing and framing jokes by having the robots try different “comeback tactics” and observing audience response. An ANOVA analysis revealed six distinct response categories. 78 % of participants positively rated the personalized robot comedy across the six scenarios. However, when both robot comedians took a negative tone, only 23 % approved (p < 0.001). Interestingly, audiences validated the robot comedian’s performance (89 % approval) more than the human comedian (54 % approval), a statistically significant difference (p < 0.01). These findings shed light on ideal characteristics for effective human-robot comedy interactions, highlighting the importance of topicality, maintaining a generally positive mood, and the unique strengths of robot performers. The study paves the way for further optimization of personalized, adaptive robot humour systems leveraging multimodal AI capabilities to enhance entertainment experiences tailored to individuals.

Service robots and innovation: An ecosystem approach

AbstractThe proliferation of service robots has stimulated innovation across industries. These autonomous, physically embodied, and adaptable robots engage in diverse interactions, from patient care to goods delivery and hospitality services. However, the deployment of increasingly capable service robots demands not only designing user–robot interactions, but also holistic innovation management that transcends organizational boundaries and involves various societal stakeholders. Our research draws on the emerging Public Value Theory to examine the types of service robots and the innovation ecosystems that harness the expertise of public and private stakeholders and produce Public Value. Based on literature and an illustrative case study, we conceptualize service robots along characteristics such as autonomy, aesthetics, assistive roles, and user interfaces, and introduce Service Robot‐based Innovation as the ecosystem‐enabled development and employment of such robots. The service robot's autonomy and ecosystem integration are key dimensions determining innovation management practices and Public Value creation. The illustrative case, centered on long‐term care, dissects the integration of service robots across the micro (user), meso (organizational), and macro (societal) levels of the ecosystem. An ecosystem‐as‐structure approach identifies the roles and activities of stakeholders aligning around a shared value proposition of Public Value. A research agenda presents future opportunities within and across various ecosystem levels to advance scholarly understanding of Service Robot‐based Innovation.

A Novel Recursive Algorithm for the Implementation of Adaptive Robot Controllers

In this paper, a novel recursive and efficient algorithm for real-time implementation of the adaptive and passivity-based controllers in model-based control of robot manipulators is proposed. Many of the previous methods on these topics involve the computation of the regressor matrix explicitly or non-recursive computations, which remains as the main challenge in practical applications. The proposed method achieves a compact and fully recursive reformulation without computing the regressor matrix or its elements. This paper is based on a comprehensive literature review of the previously proposed methods, presented in a unified mathematical framework suitable for understanding the fundamentals and making comparisons. The considered methods are implemented on several processors and their performances are compared in terms of real-time computational efficiency. Computational results show that the proposed Adaptive Newton-Euler Algorithm significantly reduces the computation time of the control law per cycle time in the implementation of adaptive control laws. In addition, using the dynamic simulation of an industrial robot with 6-DoF, trajectory tracking performances of the adaptive controllers are compared with those of non-adaptive control methods where dynamic parameters are assumed to be known.

Adaptive Robot Coordination: A Subproblem-Based Approach for Hybrid Multi-Robot Motion Planning

Adaptive Robot Coordination: A Subproblem-Based Approach for Hybrid Multi-Robot Motion Planning

Challenges for Regulating Tort Liability for Adaptive Robots

لا يتردد هذا البحث في تأييد الحجج التي تحاول ربط أفعال الروبوت بالإنسان، أي عدم القول بمسؤولية الروبوت الشخصية عن أعماله؛ لكن مع توضيح إشكاليات بعض النظريات التقليدية في إسنادها المسؤولية للإنسان، كما يوضح البحث كيف أن النظريات الحديثة التي حاولت تفادي قصور النظريات التقليدية؛ لم تقدم الجديد، وراوحت مكاناها. لذلك يطرح البحث مبررات رفض مسؤولية الروبوت الشخصية؛ القانونية منها، وغير القانونية التي لا تنفصل عن القانون المتمثلة في السياق الأخلاقي والنفسي للمسؤولية، ثم يقترح أساسا قانونيا مقنعا؛ يتفادى قصور النظريات المشار إليها.

Design &amp; Fabrication of Vertical Surface Cleaner

Abstract: The Wall Climbing Robot designed in this study to combines the innovative propulsion system of propellers with the traditional locomotion method of wheels to achieve versatile and efficient mobility on both vertical and horizontal surfaces. By integrating propellers and wheels, the robot can seamlessly transition between climbing walls and moving along flat surfaces. The hybrid design not only enhances the robot's mobility but also optimizes its energy efficiency, making it suitable for prolonged missions in various environments. This research explores the synergistic integration of propellers and wheels, showcasing the robot's adaptability in real- world applications such as surveillance, inspection, and maintenance tasks in both indoor and outdoor scenarios. The developed Wall Climbing Robot presents a significant advancement in robotic mobility, offering a promising solution for complex tasks in diverse settings

PARTS—A 2D Self-Reconfigurable Programmable Mechanical Structure

Modular self-reconfigurable robots hold the promise of being capable of performing a wide variety of tasks. However, many systems fall short of either delivering this promised functionality due to constraints in system architecture or validating it on functional hardware prototypes. This paper demonstrates the functional capabilities of the Planar Adaptive Robot with Triangular Structure (PARTS) and documents the versatility of this robot system using a holistic approach that combines simulations and hardware demonstrations on a prototype with nine fabricated modules. PARTS is a two-dimensional modular robot consisting of modules with a shape-shifting triangular geometry capable of forming adaptable space-covering structures. Meta-modules and mesh restructuring techniques are presented as methods for achieving topological self-reconfiguration. The feasibility of these methods is demonstrated by applying them on a simulated reconfiguration example of 62 modules. The paper showcases the versatility of PARTS on the hardware prototype using task-specific configurations, including locomotion using a meta-module and a walker configuration, module-module interaction by establishing a bridge between two separated module clusters, and interaction with the environment using a gripper and supporting structure configuration. The results validate the versatility and emphasize the potential of the system’s design concept, motivating the transfer of the hardware architecture to the third dimension.

Ontology based autonomous robot task processing framework.

In recent years, the perceptual capabilities of robots have been significantly enhanced. However, the task execution of the robots still lacks adaptive capabilities in unstructured and dynamic environments. In this paper, we propose an ontology based autonomous robot task processing framework (ARTProF), to improve the robot's adaptability within unstructured and dynamic environments. ARTProF unifies ontological knowledge representation, reasoning, and autonomous task planning and execution into a single framework. The interface between the knowledge base and neural network-based object detection is first introduced in ARTProF to improve the robot's perception capabilities. A knowledge-driven manipulation operator based on Robot Operating System (ROS) is then designed to facilitate the interaction between the knowledge base and the robot's primitive actions. Additionally, an operation similarity model is proposed to endow the robot with the ability to generalize to novel objects. Finally, a dynamic task planning algorithm, leveraging ontological knowledge, equips the robot with adaptability to execute tasks in unstructured and dynamic environments. Experimental results on real-world scenarios and simulations demonstrate the effectiveness and efficiency of the proposed ARTProF framework. In future work, we will focus on refining the ARTProF framework by integrating neurosymbolic inference.

Design of an actuator with bionic claw hook–suction cup hybrid structure for soft robot

To improve the adaptability of soft robots to the environment and achieve reliable attachment on various surfaces such as smooth and rough, this study draws inspiration from the collaborative attachment strategy of insects, cats, and other biological claw hooks and foot pads, and designs an actuator with a bionic claw hook–suction cup hybrid structure. The rigid biomimetic pop-up claw hook linkage mechanism is combined with a flexible suction cup of a ‘foot pad’ to achieve a synergistic adhesion effect between claw hook locking and suction cup adhesion through the deformation control of a soft pneumatic actuator. A pop-up claw hook linkage mechanism based on the principle of cat claw movement was designed, and the attachment mechanism of the biological claw hooks and footpads was analysed. An artificial muscle-spring-reinforced flexible pneumatic actuator (SRFPA) was developed and a kinematic model of the SRFPA was established and analysed using Abaqus. Finally, a prototype of the hybrid actuator was fabricated. The kinematic and mechanical performances of the SRFPA and entire actuator were characterised, and the attachment performance of the hybrid actuator to smooth and rough surfaces was tested. The results indicate that the proposed biomimetic claw hook–suction cup hybrid structure actuator is effective for various types of surface adhesion, object grasping, and robot walking. This study provides new insights for the design of highly adaptable robots and biomimetic attachment devices.

Adaptable robots, ethics, and trust: a qualitative and philosophical exploration of the individual experience of trustworthy AI

AbstractMuch has been written about the need for trustworthy artificial intelligence (AI), but the underlying meaning of trust and trustworthiness can vary or be used in confusing ways. It is not always clear whether individuals are speaking of a technology’s trustworthiness, a developer’s trustworthiness, or simply of gaining the trust of users by any means. In sociotechnical circles, trustworthiness is often used as a proxy for ‘the good’, illustrating the moral heights to which technologies and developers ought to aspire, at times with a multitude of diverse requirements; or at other times, no specification at all. In philosophical circles, there is doubt that the concept of trust should be applied at all to technologies rather than their human creators. Nevertheless, people continue to intuitively reason about trust in technologies in their everyday language. This qualitative study employed an empirical ethics methodology to address how developers and users define and construct requirements for trust throughout development and use, through a series of interviews. We found that different accounts of trust (rational, affective, credentialist, norms based, relational) served as the basis for individual granting of trust in technologies and operators. Ultimately, the most significant requirement for user trust and assessment of trustworthiness was the accountability of AI developers for the outputs of AI systems, hinging on the identification of accountable moral agents and perceived value alignment between the user and developer’s interests.

Enhancing Robotics with Cognitive Capabilities

In the pursuit of creating more effective and adaptable robots, the flourishing field of cognitive robotics has arisen to infuse machines with human-like cognitive functions. This paper delves into the significance of cognitive robotics and charts a course for empowering robots with advanced cognitive capabilities. Drawing inspiration from current research in cognitive architectures, the paper underscores the importance of refined perception, language processing, complex decision-making, emotional intelligence, and cognitive synergy. By integrating these cognitive functions into robotic systems, the goal is to equip robots to operate intelligently in dynamic environments, collaborate seamlessly with humans, and adeptly handle diverse tasks. The proposed enhancements mark crucial strides towards the development of more versatile and capable intelligent robots.

THE FUTURE IS NOW: AI POWERS NEXT-GENERATION ROBOTS

This article explores the transformative effect of synthetic intelligence (AI) on robotics, highlighting key regions where AI is revolutionizing the field. It discusses the evolution of robots, from simple automation to superior AI, and the position of AI in gaining knowledge of, sensing, reasoning, and communication for robots. The article also offers actual-global examples of AI robotics programs, along with self-riding automobiles, warehouse automation, and area exploration. It addresses the social implications of intelligent, self-sufficient robots, along with issues about process displacement, human relationships, and privacy. The article concludes by means of supplying interesting innovations on the horizon for AI robotics, such as smarter and more adaptable robots, more advantageous human-robot collaboration, miniaturization, self-reliant automobiles, and robotics for private use. Keywords: miniaturization, transformative, self-reliant automobiles.

FedHIP: Federated learning for privacy-preserving human intention prediction in human-robot collaborative assembly tasks

Human-robot collaboration is a promising solution to relieve construction workers from repetitive and physically demanding tasks, thus improving construction safety and productivity. Many studies have developed various deep learning models for human intention prediction, which will form the basis for proactive and adaptive robot planning and control to enable intelligent human-robot collaboration. However, there remain two challenges. First, most research only focuses on a single type of human intention, without a holistic understanding of multi-level intention, including both high-level intended actions and objects of interest, and low-level body movements. Second, conventional deep learning approaches train a centralized model with aggregated datasets, which requires the sharing of sensitive information (e.g., personal images and behavior data), posing broad privacy concerns in practical implementation. This study proposes a vision-based multi-task federated learning (FL) framework, FedHIP, for multi-level human intention prediction in human-robot collaborative assembly tasks. Specifically, taking body movements and assembly components as inputs, a long short-term memory based multi-task learning model was developed to simultaneously predict multi-level human intention in assembly tasks. FL was employed to train the model in a distributed and privacy-preserving way on local clients without the need of transmitting sensitive data. The results show that the proposal FedHIP without and with pre-train can achieve an accuracy of 80.1% and 85.7% in action prediction, 97.6% and 97.8% in object prediction, and an average displacement error of 12.7 and 11.7 pixels in motion prediction, respectively. Models trained from FedHIP were also compared with those obtained from traditional centralized training and local training. It was found that FL leads to compatible accuracy with centralized training and much higher accuracy than local training while preserving data privacy.

Bio-inspired soft robot with varied localized stiffness

Soft robots are a type of intelligent robot with high adaptability, and the majority of them are made from soft materials, so they are flexible and adaptable. The variable stiffness function of soft robots is crucial, as it can enhance the robot's adaptability, safety, and dependability. By adjusting the stiffness, the soft robot is able to maintain a stable motion state in a complex environment, reduce environmental interference, and perform human-like actions with improved target control. The elephant trunk served as inspiration for the way proposed in this study for modifying the soft robot's arm's stiffness. By changing the insertion scheme of the interference plate in a flexible manner, the robot arm can have variable bending capability, allowing it to complete a variety of work instructions given by humans and to satisfy a variety of work requirements.

Selection of Industry 4.0 technologies for Lean Six Sigma integration using fuzzy DEMATEL approach

PurposeThis study aims to explore the integration of Industry 4.0 technologies with lean six sigma practices in the manufacturing sector for enhanced process improvement.Design/methodology/approachThis study used a fuzzy decision-making trial and evaluation laboratory approach to identify critical Industry 4.0 technologies that can be harmonized with Lean Six Sigma methodologies for achieving improved processes in manufacturing.FindingsThe research reveals that key technologies such as modeling and simulation, artificial intelligence (AI) and machine learning, big data analytics, automation and industrial robots and smart sensors are paramount for achieving operational excellence when integrated with Lean Six Sigma.Research limitations/implicationsThe study is limited to the identification of pivotal Industry 4.0 technologies for Lean Six Sigma integration in manufacturing. Further studies can explore the implementation challenges and the quantifiable benefits of such integrations.Practical implicationsIntegrating Industry 4.0 technologies with Lean Six Sigma enhances manufacturing efficiency. This approach leverages AI for predictive analysis, uses smart sensors for energy efficiency and adaptable robots for flexible production. It is vital for competitive advantage, significantly improving decision-making, reducing costs and streamlining operations in the manufacturing sector.Social implicationsThe integration of Industry 4.0 technologies with Lean Six Sigma in manufacturing has significant social implications. It promotes job creation in high-tech sectors, necessitating advanced skill development and continuous learning among the workforce. This shift fosters an innovative, knowledge-based economy, potentially reducing the skills gap. Additionally, it enhances workplace safety through automation, reduces hazardous tasks for workers and contributes to environmental sustainability by optimizing resource use and reducing waste in manufacturing processes.Originality/valueThis study offers a novel perspective on synergizing advanced Industry 4.0 technologies with established Lean Six Sigma practices for enhanced process improvement in manufacturing. The findings can guide industries in prioritizing their technological adoptions for continuous improvement.

A growing soft robot with climbing plant-inspired adaptive behaviors for navigation in unstructured environments.

Self-growing robots are an emerging solution in soft robotics for navigating, exploring, and colonizing unstructured environments. However, their ability to grow and move in heterogeneous three-dimensional (3D) spaces, comparable with real-world conditions, is still developing. We present an autonomous growing robot that draws inspiration from the behavioral adaptive strategies of climbing plants to navigate unstructured environments. The robot mimics climbing plants' apical shoot to sense and coordinate additive adaptive growth via an embedded additive manufacturing mechanism and a sensorized tip. Growth orientation, comparable with tropisms in real plants, is dictated by external stimuli, including gravity, light, and shade. These are incorporated within a vector field method to implement the preferred adaptive behavior for a given environment and task, such as growth toward light and/or against gravity. We demonstrate the robot's ability to navigate through growth in relation to voids, potential supports, and thoroughfares in otherwise complex habitats. Adaptive twining around vertical supports can provide an escape from mechanical stress due to self-support, reduce energy expenditure for construction costs, and develop an anchorage point to support further growth and crossing gaps. The robot adapts its material printing parameters to develop a light body and fast growth to twine on supports or a tougher body to enable self-support and cross gaps. These features, typical of climbing plants, highlight a potential for adaptive robots and their on-demand manufacturing. They are especially promising for applications in exploring, monitoring, and interacting with unstructured environments or in the autonomous construction of complex infrastructures.