Maintaining object grasp stability represents a pivotal challenge within the domain of robotic manipulation and upper-limb prosthetics. Perturbations originating from external sources frequently disrupt the stability of grasps, resulting in slippage occurrences. Also, if the grasping forces are not optimal while controlling the slip, it may result in the deformation of the objects. This study investigates the robustification of a reinforcement learning (RL) policy for implementing intelligent bionic reflex control, i.e., slip and deformation prevention of the grasped objects. RL-derived policies are vulnerable to failures in environments characterized by dynamic variability. To mitigate this vulnerability, we propose a methodology involving the incorporation of an adaptive sliding mode controller into a pre-trained RL policy. By exploiting the inherent invariance property of the sliding mode algorithm in the presence of uncertainties, our approach strengthens the robustness of the RL policies against diverse and dynamic variations. Numerical simulations substantiate the efficacy of our approach in robustifying RL policies trained within simulated environments.

Soft robotic devices are designed for applications such as exploration, manipulation, search and rescue, medical surgery, rehabilitation, and assistance. Due to their complex kinematics, various and often hard-to-define degrees of freedom, and nonlinear properties of their material, designing and operating these devices can be quite challenging. Using tools such as optimization methods can improve the efficiency of these devices and help roboticists manufacture the robots they need. In this work, we present an extensive and systematic literature search on the optimization methods used for the mechanical design of soft robots, particularly focusing on literature exploiting evolutionary computation (EC). We completed the search in the IEEE, ACM, Springer, SAGE, Elsevier, MDPI, Scholar, and Scopus databases between 2009 and 2024 using the keywords “soft robot,” “design,” and “optimization.” We categorized our findings in terms of the type of soft robot (i.e., bio-inspired, cable-driven, continuum, fluid-driven, gripper, manipulator, modular), its application (exploration, manipulation, surgery), the optimization metrics (topology, force, locomotion, kinematics, sensors, and energy), and the optimization method (categorized as EC or non-EC methods). After providing a road map of our findings in the state of the art, we offer our observations concerning the implementation of the optimization methods and their advantages. We then conclude our paper with suggestions for future research.

Traction of the head-neck is important in the treatment of patients suffering from neck pain due to degeneration of the intervertebral discs. Conventional neck traction is provided manually by experienced physical therapists who maintain a desired orientation of the head-neck relative to the trunk while applying the traction. It is postulated that innovative designs of neck exoskeletons can provide the same function both flexibly and accurately. This article presents a novel architecture of a parallel mechanism whose base sits on the human shoulders with 4 parallel chains, each chain having a revolute-revolute-universal-revolute (RRUR) structure, while the end-effector is connected rigidly to the human head. Each chain has five degrees-of-freedom (DOF) and applies a constraint on the motion of the end-effector. As a result, this parallel mechanism allows two DOFs to the end-effector. These are (i) forward flexion or lateral bending of the head and (ii) vertical translation. An important motivation for the current design with RRUR structure is to characterize the range of forward flexion/lateral bending of the head-neck with this structure and the vertical translation to the end-effector. A physical prototype was constructed and tested to evaluate the performance of this mechanism in hardware for the proposed application.

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.

The increase of human presence in the subsea and seabed environments necessitates the development of more capable and highly dexterous, innovative underwater manipulators. Biomimetic soft-robot arms represent a promising candidate for such manipulation systems. However, the well-known modeling techniques and control theories of traditional rigid robots do not apply to soft robots. The challenges of kinematic and dynamic modeling of soft robots with infinite degrees of freedom require the development of dedicated modeling methods. A novel procedure for representing soft-robotic arms and their motion in a rigid-body simulation environment is proposed in this paper. The proposed procedure relies on the piecewise constant curvature approach to simplify the very complex model of hyper-redundant soft-robotic arms, making it suitable for real-time applications. The proposed method is implemented and verified to be used in model-mediated teleoperation of the soft arms of a biomimetic robotic squid designed for underwater manipulation as a case study.

As a solution to soft material manipulation, dual-arm robots that are capable of multi-point grasping have become the latest trend due to their higher capability and efficiency. To explore further development in soft material manipulation and go beyond the hardware limit of dual arms, in this paper the robot platform “Quad-SCARA” that consists of four Selective Compliance Assembly Robot Arm (SCARA) is developed and introduced. With the base of hardware platform, a novel collision avoidance system designed for the motion planning of Quad-SCARA in multi-point grasping state is proposed. This system is inspired by the idea of “Buffered Voronoi Cell (BVC),” an algorithm originally proposed for multi-agent collision avoidance. After appropriate adaptation and new proposition in implementation of BVC, the experiments of folding and spreading a handkerchief are performed, and the results in simulation and robot experiments are presented and discussed for the validation and evaluation of entire system.

In this paper, we conceptualize, analyze, and assemble a prototype adaptive surface system capable of morphing its geometric configuration using an array of linear actuators to impose omnidirectional movement of objects that lie on the surface. The principal focus and contribution of this paper is the derivation of feedback control protocols–for regulating the actuators’ length in order to accomplish the object conveyance task–that scale with the number of actuators and the nonlinear kinematic constraints of the morphing surface. Simulations and experimental results demonstrate the advantages of distributed manipulation over static-shaped feeders.

Nonlinear articular geometries of biological joints have contributed to highly agile and adaptable human-body motions. However, human–machine interaction could potentially distort natural human motions if the artificial mechanisms overload the articular surfaces and constrain biological joint kinematics. It is desired to better understand the deformable articular geometries of biological joints in vivo during movements for design and control of wearable robotics. An articular geometry reconstruction method is proposed to measure the effective articular profile with a wearable compliant device and illustrated with its application to knee-joint kinematic analysis. Regarding the joint articulation as boundary constraints for the compliant mechanism, the equivalent articular geometry is constructed from the beam deformations driven by knee motions, where the continuous deformations are estimated with strain data from the embedded sensors. Both simulated analysis and experimental validation are presented to justify the proposed method.

The paper reports on a new multi-view algorithm that combines information from multiple images of a single target object, captured at different distances, to determine the identity of an object. Due to the use of global feature descriptors, the method does not involve image segmentation. The performance of the algorithm has been evaluated on a binary classification problem for a data set consisting of a series of underwater images.

This paper reviews the state-of-the-art in robotic tails intended for inertial adjustment applications on-board mobile robots. Inspired by biological tails observed in nature, robotic tails provide a separate means to enhance stabilization, and maneuverability from the mobile robot's main form of locomotion, such as legs or wheels. Research over the past decade has primarily focused on implementing single-body rigid pendulum-like tail mechanisms to demonstrate inertial adjustment capabilities on-board walking, jumping and wheeled mobile robots. Recently, there have been increased efforts aimed at leveraging the benefits of both articulated and continuum tail mechanism designs to enhance inertial adjustment capabilities and further emulate the structure and functionalities of tail usage found in nature. This paper discusses relevant research in design, modeling, analysis and implementation of robotic tails onto mobile robots, and highlight how this work is being used to build robotic systems with enhanced performance capabilities. The goal of this article is to outline progress and identify key challenges that lay ahead.

It is important to develop a robotic orthosis or exoskeleton that can provide back-drivable and good assistive performances with lightweight structures for overground gait rehabilitation of stroke patients. In this paper, we describe a robotic knee device with a five-bar linkage to allow low-impedance voluntary knee motion within a specified rotation range during the swing phase, and to assist knee extension during the stance phase. The device can provide free motion through the five-bar linkage with 2-degree-of-freedom (DOF) actuation via the patient's shank using a linear actuator, and can assist knee extension at any controlled knee angle while bearing weight via a geared five-bar linkage with 1 DOF actuation of the linear actuator. The kinematic transition between the two modes can be implemented by contact with a circular structure and a linear link, and the resultant range of motion can be determined by the linear actuator. The kinematic weight of the device was optimized using the simple genetic algorithm to reduce the mass. The optimization cost function was based on the sum of the total link lengths and the actuator power. The optimization results reduced the total link length and motor power by 47% and 43%, respectively, compared to the initial design. We expect that the device will facilitate rehabilitation of stroke patients by allowing safe and free overground walking while providing support for stumbling.

We present a model of construction using iterative amorphous depositions and give a distributed algorithm to reliably build ramps in unstructured environments. The relatively simple local strategy for interacting with irregularly shaped, partially built structures gives rise to robust adaptive global properties. We illustrate the algorithm in both single robot and multi-robot cases via simulations and describe how to solve key technical challenges to implementing this algorithm via a robotic prototype.

A new theory in contact pressure distribution and friction limit surfaces for modeling of hemicylindrical soft fingertips is introduced, to define the relationship between friction force and the moment with respect to the normal axis of contact. A general pressure-distribution function is proposed to capture material properties and contact geometry with various pressure profiles, and the coefficient of pressure distribution over the rectangular contact area is found between π and π/2. Combining the results of the contact mechanics model with the contact pressure distribution, the normalized friction limit surface can be derived for anthropomorphic soft fingers. The numerical friction limit surface of hemicylindrical soft-finger contact can be approximated by an ellipse, with the major and minor axes as the maximum friction force and the maximum moment with respect to the normal axis of contact, respectively. The results show that the friction limit surfaces are improved (13%–17%), if hemicylindrical fingertips are used rather than hemispherical fingertips at the same radius of fingertip, shape factor of the pressure profile, and applied load. Furthermore, the results of the contact mechanics model and the pressure distribution for soft fingers facilitate the construction of numerical friction limit surfaces, enabling to analyze and simulate the contact behaviors of grasping and manipulation in humanoid robots, prosthetic hands, and robotic hands.

The tip of a flexible needle with a bevel tip approximately follows a planar arc when it is inserted into soft tissue only with the force applied to the needle along the needle axis. The direction of the arc can be controlled by the rotation input around the needle axis. This flexible and steerable needle has been shown to have a considerable potential in clinical applications due to its maneuverability and steerability. Beyond the needle insertion to a single destination, this paper concerns obtaining needle trajectories that reach multiple targets. Specifically, we propose an algorithm for the insertion of a flexible needle to travel from a single insertion point (i.e. port) to multiple targets. The insertion is motivated by the observation that multiple targets can be reached by the flexible needle through a combination of insertion, partial retraction, turning, and reinsertion of the flexible needle. In this paper we develop an insertion algorithm that minimizes tissue damage during the needle insertion to multiple targets. To this end, a cost function which computes the length of needle trajectory that can be thought of as the tissue damage is defined, and is minimized. Through the minimization, we find the optimal insertion parameters such as the port location, the insertion direction at the port, the targeting order, the turning angles, and the lengths of forward insertions and retractions. To reduce the computation time, we perform workspace analysis for this approach to filter out the no-solution cases. We present numerical examples of the simulated needle insertion for multiple targets with and without obstacles and show the benefit of the proposed method in terms of the tissue damage and the number of skin punctures. Extensions of the proposed approach to more complex cases such as more than three target points and maneuvering around spherical obstacles are also discussed.

We investigate the controlled realization of a stable circular pursuit model in a multi-agent robotic system described by double-integrator dynamics with homogeneous controller gains. The dynamic convergence of the system starting from a randomly chosen, non-overlapping initial configuration to a sustained, stable pursuit configuration satisfying velocity matching and uniform inter-agent separation is demonstrated using the proposed control framework. The cyclic pursuit configuration emerges from local, linear, inter-agent interactions and is shown to be robust under stochastic perturbations of small and moderate intensities. The stability criterion discussed in this work is independent of the number of agents, permitting dynamic addition/deletion of agents without affecting overall system stability. Experimental results that validate the key theoretical results are also presented. Potential applications of the results obtained include cooperative perimeter tracking and resource distribution applications such as border patrol and wildfire monitoring.

Human fingers possess mechanical characteristics, which enable them to manipulate objects. In robotics, the study of soft fingertip materials for manipulation has been going on for a while; however, almost all previous researches have been carried on hemispherical shapes whereas this study concentrates on the use of hemicylindrical shapes. These shapes were found to be more resistant to elastic deformations for the same materials. The purpose of this work is to generate a modified nonlinear contact-mechanics theory for modeling soft fingertips, which is proposed as a power-law equation. The contact area of a hemicylindrical soft fingertip is proportional to the normal force raised to the power of γcy, which ranges from 0 to 1/2. Subsuming the Timoshenko and Goodier (S. P. Timoshenko and J. N. Goodier, Theory of Elasticity, 3rd ed. (McGraw-Hill, New York, 1970) pp. 414–420) linear contact theory for cylinders confirms the proposed power equation. We applied a weighted least-squares curve fitting to analyze the experimental data for different types of silicone (RTV 23, RTV 1701, and RTV 240). Our experimental results supported the proposed theoretical prediction. Results for human fingers and hemispherical soft fingers were also compared.

This study addresses the kinematics of a six-degrees-of-freedom parallel manipulator whose moving platform is a regular triangular prism. The moving and fixed platforms are connected to each other by means of two identical parallel manipulators. Simple forward kinematics and reduced singular regions are the main benefits offered by the proposed parallel manipulator. The Input–Output equations of velocity and acceleration are systematically obtained by resorting to reciprocal-screw theory. A case study, which is verified with the aid of commercially available software, is included with the purpose to exemplify the application of the method of kinematic analysis.

This paper deals with processes of optimisation for the design of a four degree-of-freedom robot dedicated to remote ultrasound tele-echography. This robot is designed to track the medical gestures of a remote expert moving an ultrasound probe. The goal is to optimise the kinematic structure by fixing the geometric parameters; these have a significant role in robot configuration singularities, with respect to current medical gestures and mechanism compactness. After choosing a dedicated kinematic structure, several optimisations are presented. Then an optimal choice of geometrical parameters of a global function in relation with kinematic performance indices and compactness is proposed. This robot is soon to be used in the experimental medical phase of the Prosit project.

In this work, a new translational robot formed with two different parallel manipulators with a common control point is introduced. An asymmetric parallel manipulator provides three translational degrees of freedom to the proposed robot while the orientation of the end-effector platform is kept constant by means of a Delta-like manipulator. An exact solution is easily derived to solve the forward displacement analysis while a semi-closed form solution is available for solving the inverse displacement analysis. The infinitesimal kinematics of the robot is approached by applying the theory of screws. Finally, a numerical example that consists of solving the inverse/forward displacement analysis as well as the forward acceleration analysis of the end-effector platform is presented. The example also includes the computation of the workspace and the direct/inverse singularities of the example.

Human posture prediction can often be formulated as a nonlinear multiobjective optimization (MOO) problem. The joint displacement function is considered as a benchmark of human performance measures. When the joint displacement function is used as the objective function, posture prediction is a MOO problem. The weighted-sum method is commonly used to find a Pareto solution of this MOO problem. Within the joint displacement function, the relative value of the weights associated with each joint represents the relative importance of that joint. Usually, weights are determined by trial and error approaches. This paper presents a systematic approach via an inverse optimization approach to determine the weights for the joint displacement function in posture prediction. This inverse optimization problem can be formulated as a bi-level optimization problem. The design variables are joint angles and weights. The cost function is the summation of the differences between two set of joint angles (the design variables and the realistic posture). Constraints include (1) normalized weights within limits and (2) an inner optimization problem to solve for joint angles (predicted posture). Additional constraints such as weight limits and weight linear equality constraints, obtained through observations, are also implemented in the formulation to test the method. A 24 degree of freedom human upper body model is used to study the formulation and visualize the prediction. An in-house motion capture system is used to obtain the realistic posture. Four different percentiles of subjects are selected to run the experiment. The set of weights for the general seated posture prediction is obtained by averaging all weights for all subjects and all tasks. On the basis of obtained set of weights, the predicted postures match the experimental results well.