In recent years, many microrobots have been developed for search applications using swarms in places where humans cannot enter, such as disaster sites. Hexapod robots are suitable for moving over uneven terrain. In order to use micro-hexapod robots for swarm exploration, it is necessary to reduce the robot’s size while maintaining its rigidity. Herein, we propose a micro-hexapod with an SU-8 rigid frame that can be assembled from a single sheet. By applying the SU-8 coating as a structure to the hexapod and increasing the rigidity, the substrate size can be reduced to within 40 mm × 40 mm and the total length when assembled to approximately 30 mm. This enables the integration of the micro electromechanical systems (MEMS) process into small and inexpensive hexapod robots. In this study, we assembled the hexapod with a rigid frame from a sheet created using the MEMS process and evaluated the leg motion.

This paper explores and experimentally compares the effectiveness of robot-stopping approaches based on the speed and separation monitoring for improving fluency in collaborative robotics. In the compared approaches, a supervisory controller checks the distance between the bounding volumes enclosing human operator and robot and prevents potential collisions by determining the robot’s stop time and triggering a stop trajectory if necessary. The methods are tested on a Franka Emika robot with 7 degrees of freedom, involving 27 volunteer participants, who are asked to walk along assigned paths to cyclically intrude the robot workspace, while the manipulator is working. The experimental results show that scaling online the dynamic safety zones is beneficial for improving fluency of human-robot collaboration, showing significant statistical differences with respect to alternative approaches.

As the proportion of the elderly population in the USA expands, so will the demand for rehabilitation and social care, which play an important role in maintaining function and mediating motor and cognitive decline in older adults. The use of social robotics and telemedicine are each potential solutions but each have limitations. To address challenges with classical telemedicine for rehabilitation, we propose to use a social robot-augmented telepresence (SRAT), Flo, which was deployed for long-term use in a community-based rehabilitation facility catering to older adults. Our goals were to explore how clinicians and patients would use and respond to the robot during rehab interactions. In this pilot study, three clinicians were recruited and asked to rate usability after receiving training for operating the robot and two of them conducted multiple rehab interactions with their patients using the robot (eleven patients with cognitive impairment and/or motor impairment and 23 rehab sessions delivered via SRAT in total). We report on the experience of both therapists and patients after the interactions.

This paper systematically investigates a soft-rigid hybrid pneumatic actuator (SRHPA), which consists of a rigid-foldable twisting skeleton capable of a large range of helical motion and a soft bellows muscle with high linear driving force. Considering the unique varying-pitch helical motion of the foldable skeleton, the analytical model mapping the input force generated by the bellows muscle and output forces of the actuator is revealed and verified with a simulation of the force analysis. Prototypes of the actuator are developed by fabricating the twisting skeleton with multilayered aluminum composite panels and 3D-printing the bellows muscle with thermoplastic polyurethane (TPU) 95A filament. The static and dynamic performances of the prototypes are tested to validate the analytical modeling of output forces. Using the actuator as a module, a novel bipedal inchworm robot with four modules is developed and tested to demonstrate its adaptability in confined space by switching between the going-straight, the turning-around, and the rotating gaits. The hybrid actuator and the inchworm robot with zero onboard electronics have the potential to be deployed in extreme environments where pneumatically actuated systems are preferred over electrical machines and drives, such as in nuclear and explosive environments.

Accurate prediction for mechanisms’ dynamic responses has always been a challenging task for designers. For modeling easiness purposes, mechanisms’ synthesis and optimization have been mostly limited to rigid systems, making consequently the designer unable to vow that the manufactured mechanism satisfies the target responses. To address this limitation, flexible mechanism synthesis is aimed in this work. Two benchmark mechanisms being the core of myriad mechanical devices are of scope, mainly, the flexible slider-crank and the four-bar. In addition to the mechanism dimensions, materials properties have been embedded in the synthesis problem. Two responses are of interest for the slider-crank mechanism, the slider velocity, and the midpoint axial displacement for the flexible connecting rod. Whereas five responses have been compiled for the four-bar mechanism synthesis. A comparative analysis of seven optimization techniques to solve the synthesis problem for both mechanisms has been performed. Subsequently, an executable computer-aided design tool for mechanisms synthesis has been developed under MATLAB®. Numerical outcomes emphasize the limits of a single-response-based synthesis for a flexible mechanism. It has been proven that combining different responses alleviates possible error and fulfill high-accuracy requirement.

Differentially flat under-actuated robots are characterized by more degrees of freedom (DOF) than actuators: this makes possible the design of lightweight cheap robots with high dexterity. The main issue of such robots is the control of the passive joint, which requires accurate dynamic modeling of the robot.

Friction is usually discarded to simplify the models, especially in the case of low-speed trajectories. However, this simplification leads to oscillations of the end-effector about the final position, which are incompatible with fast and accurate motions.

This paper focuses on planar $n$-DOF serial robotic arms with $n-1$ actuated rotational joints plus one final passive rotational joint with stiffness and friction properties. These robots, if properly balanced, are differentially flat. When the non-actuated joint can be considered frictionless, differentially flat robots can be controlled in open loop, calculating the motor torques demanded by point-to-point motions. This paper extends the open-loop control to robots with a passive joint with viscous friction adopting a Laplace transform method. This method can be adopted by exploiting the particular structure of the equations of motion of differentially flat under-actuated robots in which the last equations are linear. Analytical expressions of the motor torques are obtained. The work is enriched by an experimental validation of a $2$-DOF under-actuated robot and by numerical simulations of the $2$- and $4$-DOF robots showing the suppression of unwanted oscillations.

Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are inefficient in high-dimensional environments in terms of planning time and optimality. Collision checking poses another challenge in obtaining a real-time solution for path planning in complex environments. To address these issues, we propose an end-to-end learning-based framework viz., Path Planning and Collision checking Network (PPCNet). The PPCNet generates the path by computing waypoints sequentially using two networks: the first network generates a waypoint, and the second one determines whether the waypoint is on a collision-free segment of the path. The end-to-end training process is based on imitation learning that uses data aggregation from the experience of an expert planner to train the two networks, simultaneously. We utilize two approaches for training a network that efficiently approximates the exact geometrical collision checking function. Finally, the PPCNet is evaluated in two different simulation environments and a practical implementation on a robotic arm for a bin-picking application. Compared to the state-of-the-art path-planning methods, our results show significant improvement in performance by greatly reducing the planning time with comparable success rates and path lengths.

Flexible endoscopy is the gold standard modality for diagnosis and therapeutic intervention of various colorectal conditions. A high bar is currently set for any new technology to replace the current modern colonoscope, but limitations do exist. For a robotic system to gain acceptance, ideally a clear advantage over the established standard needs to be demonstrated. The application of robotic technology inspired by locomotion observed in animals has been demonstrated in many fields including colonoscopy. A myriad of novel concepts has been proposed, which can overcome the anatomical and technical challenges.

This review discusses novel and innovative examples of bioinspired robotic locomotion in the colon with a detailed comparison of studies alongside separating the discussion by animal sections of insect, marine and reptile locomotion. We also discuss the current advantages and challenges a bioinspired robot will bring to the colon.

Bioinspired robotics in the colon is an exciting field of research with the potential to improve upon current existing high standards of practice in colonoscopy. By addressing areas that the conventional colonoscope is weaker in, studies are demonstrating improvement upon current limitations of standard practice and providing an insight into new methods of engineering and fabrication. Focus on the technological, mechanical and regulatory barriers is key to achieve acceptance into standard practice and will allow the aspiration of a safe, low discomfort, low cost and potentially fully autonomous robotic colonoscope to be not too distant in the future of colonoscopy.

Motion planning for high-DOF multi-arm systems operating in complex environments remains a challenging problem, with many motion planning algorithms requiring evaluation of the minimum collision distance and its derivative. Because of the computational complexity of calculating the collision distance, recent methods have attempted to leverage data-driven machine learning methods to learn the collision distance. Because of the significant training dataset requirements for high-DOF robots, existing kernel-based methods, which require $O(N^2)$ memory and computation resources, where $N$ denotes the number of dataset points, often perform poorly. This paper proposes a new active learning method for learning the collision distance function that overcomes the limitations of existing methods: (i) the size of the training dataset remains fixed, with the dataset containing more points near the collision boundary as learning proceeds, and (ii) calculating collision distances in the higher-dimensional link $SE(3)^n$ configuration space – here $n$ denotes the number of links – leads to more accurate and robust collision distance function learning. Performance evaluations with high-DOF multi-arm robot systems demonstrate the advantages of the proposed active learning-based strategy vis-$\grave{\text{a}}$-vis existing learning-based methods.

Over decades of robotics research, cooperative object transportation has been studied as a meaningful model problem for robotic networks because it possesses a variety of crucial challenges. Although these challenges are demanding, the cooperation of multiple robots has the potential to solve automation problems that are beyond the scope of an individual robot. So far, the model problem has mostly been addressed by explicitly controlling the robots’ positions. However, the position-based approach suffers from some intrinsic detriments, for example, the lack of explicit feedback between robots and object. Moreover, it remains an open question how many robots shall be employed to ensure a successful transportation. This paper’s purpose is to overcome these challenges using a novel force-based approach taking into account the robots’ actual manipulation capabilities, that is, the exerted forces. Using cost-efficient hardware, the interaction forces are measured and, what is more, explicitly controlled by a highly responsive onboard controller. Employing a tailored software architecture, the novel force-based scheme, useful for robotic manipulation beyond the benchmark problem, is probably the most flexible of its kind regarding the number of robots and the object’s shape. The controller’s functionality and performance as well as the scheme’s versatility are demonstrated by several hardware experiments.

Although several studies have revealed that fractional order controllers usually outperform conventional integer-order control solutions, fractional order controllers are not yet widely applied in industrial applications due to their complex mathematical background. In this paper, further improvements of a simple weighted sum feedback design are introduced that imitates the behavior of a fractional order controller but is free from its various formal restrictions. The proposed control solution has the main characteristics of a fractional order controller, such as finite memory length, excellent transient response with no overshoot and robust behavior, but it is placed into a much simpler mathematical framework. In the current paper, a simple derivative term was incorporated in the design which made the controller’s output more stable by completely eliminating output chattering. The proposed control method was developed for a general second-order system. It was tested in a fixed point iteration-based adaptive control scenario, through simulations using a robotic example and on experimental basis as well, utilizing a simple one-degree-of-freedom electromechanical system. The presented experiments are the first systematic investigations of the fixed point iteration-based adaptive control method.

This paper proposes a new type of hybrid manipulator that can be of extensive use in industries where translational motion is required while maintaining an arbitrary end-effector orientation. It consists of two serially connected parallel mechanisms, each having three degrees of freedom, of which the upper platform performs a pure translational motion with respect to the mid-platform. Closed-form forward and inverse kinematic analysis of the proposed manipulator has been carried out. It is followed by the determination of all of its singular configurations. The theoretical results have been verified numerically, and the 3D modeling and simulation of the manipulator have also been performed. A simple optimal design is presented based on optimizing the kinematic manipulability, which further demonstrates the potential of the proposed hybrid manipulator.

The demand for flexible grasping of various objects by robotic hands in the industry is rapidly growing. To address this, we propose a novel variable stiffness gripper (VSG). The VSG design is based on a parallel-guided beam structure inserted by a slider from one end, allowing stiffness variation by changing the length of the parallel beams participating in the system. This design enables continuous adjustment between high compliance and high stiffness of the gripper fingers, providing robustness through its mechanical structure. The linear analytical model of the deflection and stiffness of the parallel beam is derived, which is suitable for small and medium deflections. The contribution of each parameter of the parallel beam to the stiffness is analyzed and discussed. Also, a prototype of the VSG is developed, achieving a stiffness ratio of 70.9, which is highly competitive. Moreover, a vision-based force sensing method utilizing ArUco markers is proposed as a replacement for traditional force sensors. By this method, the VSG is capable of closed-loop control during the grasping process, ensuring efficiency and safety under a well-defined grasping strategy framework. Experimental tests are conducted to emphasize the importance and safety of stiffness variation. In addition, it shows the high performance of the VSG in adaptive grasping for asymmetric scenarios and its ability to flexible grasping for objects with various hardness and fragility. These findings provide new insights for future developments in the field of variable stiffness grippers.

This article describes a robot walker based on a new single degree-of-freedom six-bar leg mechanism that provides rectilinear, non-rotating, movement of the foot. The walker is statically stable and requires only two actuators, one for each side, to provide effective walking movement on a flat surface. We use Curvature Theory to design a four-bar linkage with a flat-sided coupler curve and then adds a translating link so that walker foot follows this coupler curve in rectilinear movement. A prototype walker was constructed that weighs 1.6 kg, is 180 mm tall, and travels at 162 mm/s. This is an innovative legged robot that has a simple reliable design.

We present the design and deployment of a capsule endoscope via external electromagnets for locomotion in large volumes alongside its digital twin implementation based on interval type-2 fuzzy logic systems (IT2-FLSs). To perform locomotion, we developed an external mechanism comprising five external electromagnets on a two-dimensional translational platform that is to be placed underneath the patients’ bed and integrated multiple Neodymium magnets into the capsule. The interaction between the central bottom external electromagnet and the internal magnet forms a fixed body frame at the capsule center, allowing rotation. The interaction between the external electromagnets and the two internal magnets results in rotation. The elevation of the capsule is accomplished due to the interaction between the upper external electromagnet and the internal magnets. Through simulations, we model the capsule rotation as a function of torque and drive voltages. We validated the proposed locomotion approach experimentally and observed that the results are highly nonlinear and uncertain. Thus, we define a regression problem in which IT2-FLSs, capable of representing nonlinearity and uncertainty, are learned. To verify the proposed locomotion approach and test the IT2-FLS, we leverage our experimental effort to a stomach phantom and finally to an ex vivo bovine stomach. The experimental results validate the locomotion capability and show that the IT2-FLS can capture uncertainties while resulting in satisfactory prediction performance. To showcase the benefit in a clinical scenario, we present a digital twin implementation of the proposed approach in a virtual environment that can link physical and virtual worlds in real time.

The concept of distance between two rigid-body poses is important in path planning, positioning precision, mechanism synthesis, and in many other applications. In the definition of such a distance, two approaches mainly prevail, which lead to a number of formulas devised to match the needs of motion tasks. Despite the different approaches and formulas, some important theoretical results, which drive toward distance-metrics definitions useful for design and application purposes, have been stated. This paper summarizes the two different approaches together with a critical review of the literature on the distance metrics they generated, and, then, it illustrates a technique, previously proposed by the author, for combining different metrics to obtain novel distance-metric definitions that are tailored to specific applications.

An accurate dynamic model of a robot is fundamentally important for a control system, while uncertainties residing in the model are inevitable in a physical robot system. The uncertainties can be categorized as internal disturbances and external disturbances in general. The former may include dynamic model errors and joint frictions, while the latter may include external payloads or human-exerted force to the robot. Disturbance observer is an important technique to estimate and compensate for the uncertainties of the dynamic model. Different types of disturbance observers have been developed to estimate the lumped uncertainties so far. In this paper, we conducted a brief survey on five typical types of observers from a perspective of practical implementation in a robot control system, including generalized momentum observer (GMO), joint velocity observer (JVOB), nonlinear disturbance observer (NDOB), disturbance Kalman filter (DKF), and extended state observer (ESO). First, we introduced the basics of each observer including equations and derivations. Two common types of disturbances are considered as two scenarios, that is, constant external disturbance and time-varying external disturbance. Then, the observers are separately implemented in each of the two simulated scenarios, and the disturbance tracking performance of each observer is presented while their performance in the same scenario has also been compared in the same figure. Finally, the main features and possible behaviors of each type of observer are summarized and discussed. This survey is devoted to helping readers learn the basic expressions of five typical observers and implement them in a robot control system.

Rehabilitation robots help the treatment of diseases by performing cyclic exercises for a long period of time. These exercises must perform movements of the patient’s limbs; thus, the robots are required to be flexible and safe. Among rehabilitation robots, cable robots are widely used due to their unique properties, such as being lightweight and the possibility of being equipped with magnetic hooks to improve both safety and ease of use. However, the elasticity and flexibility of cables result in vibrations of the payload and hooks. In this paper, the forced vibrations due to rehabilitation exercises are studied. Since the previous studies of the authors showed a weak coupling between longitudinal and transverse vibrations, a two-cable planar model for the study of transverse vibrations is developed. The model makes it possible to study the forced transverse vibrations due to both cable motion and robot motion. Stiffness and damping of the patient’s arm are considered. Results show that the cable system exhibits a simple linear behavior when excited by robot motion and a non-linear behavior when excited by cable motion.

Bayesian inference provides a probabilistic reasoning process for drawing conclusions based on imprecise and uncertain data that has been successful in many applications within robotics and information processing, but is most often considered in terms of data analysis rather than synthesis of behaviours. This paper presents the use of Bayesian inference as a means by which to perform Boolean operations in a logic programme while incorporating and propagating uncertainty information through logic operations by inference. Boolean logic operations are implemented in a Bayesian network of Bernoulli random variables with tensor-based discrete distributions to enable probabilistic hybrid logic programming of a robot. This enables Bayesian inference operations to coexist with Boolean logic in a unified system while retaining the ability to capture uncertainty by means of discrete probability distributions. Using a discrete Bayesian network with both Boolean and Bayesian elements, the proposed methodology is applied to navigate a mobile robot using hybrid Bayesian and Boolean operations to illustrate how this new approach improves robotic performance by inclusion of uncertainty without increasing the number of logic elements required. As any logical system could be programmed in this manner to integrate uncertainty into decision-making, this methodology can benefit a wide range of applications that use discrete or probabilistic logic.

This paper discusses the design and the experimental tests on Porcospino, a bio-inspired single-track mobile robot for inspection of unstructured environments characterized by narrow spaces. It is an evolution of SnakeTrack, a single-track robot with steering capabilities; differently from SnakeTrack, the track modules of Porcospino are characterized by elastic spines, which improve traction on uneven and irregular terrains. The main body is a vertebral column, comprising a series of vertebrae connected by compliant joints and two end modules. Each end module carries two sprockets, sharing a common actuator, to drive the single peripherical track. Moreover, each end module hosts an actuator for track steering. The remaining mobilities of the vertebral column allow it to cope passively with the terrain profile, to enhance traction. The control unit, batteries, drivers, and environmental sensors are placed along the vertebral column. Both the end modules are equipped with a camera for intermittent vision, which is possible thanks to openings realized on the track modules. The experimental campaign on the first Porcospino prototype is discussed, highlighting the differences with its earlier version.