Soft robotics, an emerging domain in modern robotics, introduces innovative possibilities alongside challenges in controllability, particularly with multi-degree inflatable actuators. We present a novel manipulation method using underactuated soft fingers that addresses these challenges by harnessing symmetry breaking. Central to our approach is the mechanism of self-organization within a ring actuator equipped with five fingers. Typically considered a drawback, we exploit the actuator’s buckling behavior to facilitate in-hand manipulation. This strategic utilization enables object motion in both clockwise and counterclockwise directions via system perturbations and adjustments in frequency and duty cycle parameters. Employing the self-organizing properties of our actuator, our method is empirically validated through simulations and real actuator experiments, demonstrating the system’s ability in manipulating objects by leveraging the inherent flexibility and morphological advantages. The design enables two degrees of freedom with minimal input, allowing objects to rotate due to the actuator’s self-organizing actions. This simplification of control mechanisms is essential for soft robotics manipulation. Our findings indicate that control systems in soft robotics can be significantly simplified, harnessing the adaptable behavior inherent in its morphology.

This article presents a control strategy to carry out multiobject manipulation on a novelsoft robotictable (SoTa), which is a new form of the planar distributed manipulator. Manipulating multiple delicate objects simultaneously is an attractive feature of SoTa. The challenge here is to coordinate multiple objects in a confined planar space while avoiding interference with each other. The SoTa system adopts a manipulation strategy that includes a planning and a tracking stage for the purpose of sorting objects. The planning stage consists of two phases: 1) discrete path planning to find a path for each object on a grid map with respect to time; 2) trajectory generation to optimize and produce workable trajectories for SoTa. In the discrete path planning phase, a hierarchical searching method based on the time-varying potential field is proposed. Constraints of the SoTa system are modeled and incorporated into the path searching process. In the trajectory generation phase, a piecewise B-spline method is adopted to generate trajectories based on previously found discrete paths. Next, in the tracking stage, the objects are led to their goals along the trajectories ensuring safety and SoTa's capability. The performances of the proposed algorithm were simulated, analyzed, and compared with the conflict-based search method, which is optimal for multiagent path finding. A multiobject manipulation experiment of three objects on a$4\times 4$grid was conducted on the SoTa. The results demonstrated the effectiveness of the proposed control strategy in executing multiobject manipulations for sorting tasks on the SoTa.

Compared to wearable devices, handheld haptic devices are promising for large scale virtual reality applications because of their portability and capability of supporting large workspace haptic interaction. However, it remains a challenge to render multimodal haptic stimuli in handheld devices due to space confinement. In this paper, we present a modular approach to build a Multimodal Handheld Haptic Controller called “Haptic Handshank” that includes a thumb feedback component, a palm feedback component, and a motion tracking component. In the thumb feedback component, a compact pneumatically-driven silicone airbag is utilized to simulate softness, and a flexible membrane based on the electro-vibration principle which covers the top portion of the airbag for rendering virtual textures. In the palm feedback component, vibrational motors and Peltier devices are embedded into the device's body for rendering vibrotactile flow and distributing thermal stimuli. In the motion tracking component, an HTC-Vive tracker is mounted on the bottom of the controller's handle to enable 6-DOF palm motion tracking. The performance of the handheld device is evaluated through quantitative experimental studies, which validate the ability of the device to simulate multimodal haptic sensations in accordance with diverse hand manipulation gestures such as enclosure, static contact, rubbing, squeezing and shaking of a cup of cold drink in 3D virtual space.

This article presents the design, development, and validation of a closed-loop control system for a soft robotic table. The soft table can provide independent manipulation to multiple objects simultaneously, which is an advantage over traditional devices such as robotic arms and belt conveyor systems. The table also provides intrinsic soft handling for delicate objects. In order to execute automation tasks, a vision feedback system and an operation controller are developed and incorporated into the existing table prototype to form a closed-loop control. The vision feedback system implements image processing techniques to identify the object location from captured images. The operation controller is integrated from three sets of algorithms, including movement planning, actuator selection, and actuation planning. Two sets of experiments were conducted to validate the closed-loop system and the capability of the soft table. The first set validated the manipulation task of transporting an object to a desired location with autonomous planning and execution. The second set validated the table's ability to manipulate multiple objects simultaneously and independently. Videos of both experiments have been supplied.

Immersion, interaction, and imagination are three features of virtual reality (VR). Existing VR systems possess fairly realistic visual and auditory feedbacks, and however, are poor with haptic feedback, by means of which human can perceive the physical world via abundant haptic properties. Haptic display is an interface aiming to enable bilateral signal communications between human and computer, and thus to greatly enhance the immersion and interaction of VR systems. This paper surveys the paradigm shift of haptic display occurred in the past 30 years, which is classified into three stages, including desktop haptics, surface haptics, and wearable haptics. The driving forces, key technologies and typical applications in each stage are critically reviewed. Toward the future high-fidelity VR interaction, research challenges are highlighted concerning handheld haptic device, multimodal haptic device, and high fidelity haptic rendering. In the end, the importance of understanding human haptic perception for designing effective haptic devices is addressed.

Wind disturbance has alway been a critical challenge for safety flight and high precision control of UAV due to the uncertainty of wind in time and space domain. To this end, an acceleration feedback (AF) enhanced H∞controller is proposed to enhanced the ability of UAV against wind disturbance and it has been deigned and implemented on a hex-rotor. Firstly, The dynamic of UAV system is decoupled into inner-loop (attitude) and outer-loop (position). Then, a hierarchical H∞controller is designed for the decoupled system. Finally, a AF enhanced method is introduced into the system without changing controller structure. The stability of the AF enhanced method for the UAV system is analyzed and can be ensured by H∞theory as well. The comparison results of trajectory tracking performance between H∞controller and AF enhanced H∞controller under continuous and gusty wind verify that the proposed method is not only robust but effective for both types of wind disturbances.

Place recognition is one of the major challenges for the LiDAR-based effective localization and mapping task. Traditional methods are usually relying on geometry matching to achieve place recognition, where a global geometry map need to be restored. In this paper, we accomplish the place recognition task based on an end-to-end feature learning framework with the LiDAR inputs. This method consists of two core modules, a dynamic octree mapping module that generates local 2D maps with the consideration of the robot's motion; and an unsupervised place feature learning module which is an improved adversarial feature learning network with additional assistance for the long-term place recognition requirement. More specially, in place feature learning, we present an additional Generative Adversarial Network with a designed Conditional Entropy Reduction module to stabilize the feature learning process in an unsupervised manner. We evaluate the proposed method on the Kitti dataset and North Campus Long-Term LiDAR dataset. Experimental results show that the proposed method outperforms state-of-the-art in place recognition tasks under long-term applications. What's more, the feature size and inference efficiency in the proposed method are applicable in real-time performance on practical robotic platforms.

Soft robots must be able to structure an automation problem into a sequence of actions that lead to a desired state, before they can fulfill a meaningful role in automation applications. This, however, can only be successful if the robot can predict the outcome of an action. The theory of rigid industrial robots is not applicable without major changes, because kinematic chains do not adequately describe the continuous deformation of the complex, often biologically inspired shapes of soft robots. Analytic solutions have not been found yet. Numerical solutions based on finite elements are slow, technically challenging, and only suitable for one specific robot. It is, however, possible to observe the outcome of an action, and use these observations to plan a sequence of actions that let the robot accomplish an automation task. In this paper, we analyze a probabilistic automaton that computes the optimal sequence of actions to bring the robot into a desired state. An earlier article explained the functioning of the method in a toy example. In this paper, we analyze if it is feasible to apply the method to a planning problem inspired by a real soft robot. We show the results and document the planning process. We identify the analog of an impulse response, although it is not closed form due to the nonparametric nature of the method.

Stylometry is the statistical analyses of variationsin the author's literary style. The technique has been used inmany linguistic analysis applications, such as, author profiling, authorship identification, and authorship verification. Over thepast two decades, authorship identification has been extensivelystudied by researchers in the area of natural language processing. However, these studies are generally limited to (i) a small number of candidate authors, and (ii) documents with similar lengths. In this paper, we propose a novel solution by modeling authorship attribution as a set similarity problem to overcome the two stated limitations. We conducted extensive experimental studies on a real dataset collected from an online book archive, Project Gutenberg. Experimental results show that in comparison to existing stylometry studies, our proposed solution can handlea larger number of documents of different lengths written by alarger pool of candidate authors with a high accuracy.

A peristaltic, soft-bodied xy-sorting table manipulates objects by producing moving wave shapes on its surface. The waves exert forces on the objects which can be used for transportation, reorientation, and local repositioning. The control of such peristaltic robots is mostly unsolved, because important properties of the kinematics, dynamics, and effect of actuation are unknown. Fundamental and practical limitations in measuring the system state lead to numerical difficulties in the form of discontinuous signals in non-Euclidean spaces. To solve these problems, we introduce a probabilistic automaton that models the static and dynamic effect of actuation based on a discretized representation of the objects to be manipulated. The automaton only considers the qualitative input-output behavior of the system. It is therefore mostly independent of a particular hardware setup and controls the behavior of objects on the table without explicitly predicting mechanical forces. Our theoretical findings advance the field significantly: We show that the proposed class of automata is suited to model both static and dynamic movements. We introduce a cost function that enables the search and identification of optimal sequences of control patterns to bring the system into a desired state. We prove that the optimal control sequence can be found efficiently and give the respective algorithm.

This paper analyzes the application of machine learning techniques to the control of a soft, peristaltic, xy-sorting table. In particular, we address peristaltic tables made of a soft upper silicone layer and actuated by an array of integrated air-filled chambers. The chambers are pneumatically inflated in order to deform the table and move objects on the table. To control the robot precisely, it is necessary to model both the inverse mapping between the control signals of the actuators and the resulting surface deformation. There is currently no parametric model available. In this paper, we, therefore, study if nonparametric approaches are applicable. In these approaches, the mapping would be learned from a database of input signals and observed behaviors. From our analysis, we conclude that the most promising research direction consists in the nonparametric modeling of a limited set of peristaltic actuation patterns. However, the nonlinear hardware design that impedes a parametric model also affects the nonparametric optimization process. Our simulation suggests that the optimization is nonconvex, approximately monotonous, and feasible in terms of the number of observations of the physical robot.

This paper presents kinematics and two dynamic characteristics including stiffness and natural frequency of a four degrees-of-freedom (DOFs) masticatory robot. Based on the biological architecture of human masticatory system, this robot is modeled in the form of a redundantly actuated parallel mechanism constrained by two point-contact higher kinematic pairs (HKPs). The aim of this robot is to chew samples of newly developed foods in a human way while the food textures can be characterized. The kinematics including inverse kinematics, workspace and singularity are studied systematically. The inverse kinematics is given in closed-form solutions. The workspace is generated by a numerical search method. In virtue of the Jacobian matrix derived from differential kinematics, the singularity and the stiffness are investigated. Via the derived stiffness matrix and mass matrix, natural frequency is also computed. Finally, simulation is carried out, one real incisor trajectory of a healthy human subject is reproduced, illustrating the robot is able to reproduce kinematic behaviors of the human masticatory system, and it owns a satisfactory performance in terms of stiffness and natural frequency. Comparison between the redundantly actuated masticatory robot and its two non-redundantly actuated counterparts also exhibits that the former owns better performance, in terms of a similar incisor's workspace as the Posselt envelope, no singularity, larger stiffness and higher natural frequency.

The development of an outdoor portable radio-acoustic sounding system is presented using a novel composite antenna. A parabolic antenna is coupled with a centrally located acoustic emitter which facilitates monostatic operation. The radar operates within the 2.4 GHZ ISM bandwidth at 2.45 GHz, corresponding to a typical acoustic frequency of 5.6 KHz (at 15 °C). Modeling and simulation was carried out to provide a basis for development of the prototype, yielding an expected return power of -94.5 dBm at the desired range of 100 m. The performance of individual Doppler radar and signal processing subsystems has been validated using a highly-reflective metal target. Initial prototype testing was conducted using the feed antenna placed alongside the speaker in order to generate a response consistent with Bragg matching; this has revealed microphonic behavior, rendering temperature immeasurable at close range. Further work is to be done to configure the parabolic antenna so it can be used in testing; this is expected to improve the signal to noise ratio by 25.8 dB, improving the likelihood of obtaining measurable backscatter.

Flexible needle with bevel tip offers greater mobility for puncture surgery. This would expand the scope of the puncture surgery. However, motion planning for flexible needle is still a challenge due to its non-holonomic property and the complicated interactions with soft tissues. In this paper, a multilayer tissue model is constructed to simulate human tissue, and a dynamic programming is employed to plan the motion of flexible needle in the multilayer environment. In order to improve the security of the puncture process, the obstacles are fuzzed up. Then, an optimal algorithm is developed to determine a more suitable puncture angle. In addition, to deal with more complex environment, we develop a reverse algorithm to confirm the entry point in line with the target. Finally, we take some simulations to verify the proposed algorithms, and analyze the results.

Wearable assistive devices (WAD) assist rehabilitation and recuperation. However, WADs' development is impeded by traditional actuators and current control paradigms' lack of compliance and adaptability, in contrast to the mammalian central nervous system. A limb's joint's compliance is a function of muscles' level of activation and passive viscoelastic properties. Increased equal and unequal co-activation of a limb joint's muscles leads to an increasingly stable joint. Unequal limb joint muscle activation leads to an angular displacement. Muscles' activation is controlled by alpha motor neurons, which are innervated by the spinal-cord's autonomous neural networks, i.e. central pattern generators. Given a continuous excitatory supraspinal input, a two-level, mutually inhibiting, CPG network can control an antagonistically actuated limb joint's muscles' level of activation, thus the joint's compliance, and joint oscillation frequency.

A macro-micro manipulator system, which is composed of a long flexible-link manipulator as a macro manipulator and a relatively smaller rigid manipulator attached to the tip of the macro one as a micro manipulator, is employed to improve the motion precision of a flexible space manipulator. At the same time the macro-micro manipulator system preserves the capabilities of large work volume, high operation speed and low energy consumption of the flexible space manipulator. In this study, using the forward/inverse kinematics and a perturbation principle, an error compensation approach is proposed to eliminate as far as possible the endpoint errors of a flexible system through the fast and fine adjustment of the motion of the micro manipulator. A feedback model-based control scheme is proposed to realize the endpoint trajectory tracking of the flexible macro-micro manipulator system. Experimental results on a planar flexible 2-DOF macro/2-DOF micro manipulator system are presented herein to show the effectiveness of the proposed methods.

Classical PD control for a direct drive motor system is enhanced to suppress disturbing torque by incorporating an acceleration feedback loop. This is intended to achieve a stable and high stiffness control without the need of adjusting the PD controller, either the structure or the parameters. The acceleration feedback control is presented with a focus on designing the acceleration closed-loop in terms of its stability and ability in resisting the dynamic disturbances. The sensing and modeling of angular acceleration via servo-type linear accelerometers is dealt with from the viewpoint of practical implementation. Extensive experiments are conducted on the second joint of a three-link direct drive robot. Results are compared to those obtained by classical PD control without the acceleration feedback loop, to mainly investigate the ability of the acceleration feedback control in resisting disturbing torque, and its influence on conventional PD control.

Considers the tracking problem of dynamic nonholonomic systems with unknown inertia parameters. A new controller is proposed. The proposed controller not only ensures the entire state of the dynamic system asymptotically track the desired trajectory, but also is characterized by low dimension and the absence of singular points. Simulation results show that the proposed controller is effective.

Joint acceleration feedback control is employed to damp oscillations during the contact transition with non-zero approaching speed. A classical integral force controller is refined by means of joint acceleration and velocity feedback. The intention is to achieve a stable contact transition without need of adjusting the controller parameters adaptive to the unknown or changing environments. Extensive experiments are conducted on the third joint of a three-link direct-drive robot to verify the proposed scheme for various stiffness of the contacted environments, including elastic (sponge), less elastic (hardboard) and hard (steel plate) surfaces. Results are compared with those experimental ones by the transition control with only velocity feedback damping. The advantages offered by our approach are addressed.

A simple general motion planning approach for nonholonomic mechanical systems of chained from is proposed, in which the intricate motion planning problem (steering the system from an initial state to a final state) is converted to a simple curve fitting problem (satisfying a set of end-point conditions). By means of this approach, other geometric constraints can be easily handled, while the control inputs can be derived directly from the smooth path planned. For verifying the effectiveness of the proposed approach, a number of simulations are conducted with respect to a three-input firetruck (two-chain system).

Fuzzy reactive control, incorporating a local target switching scheme, is applied to the automatic navigation of an intelligent mobile robot in an unknown and changing environment. Sensed-ranging signals and relative target position signals are input to the fuzzy controller. The steering angle and the velocity change are inferred to drive the mobile robot. A reactive rule base governing the robot behavior is synthesized from the human heuristics with respect to various situations of environment. A local target switching scheme is proposed to serve as a front-end processor of the fuzzy active controller and to deal with the local trapping and wandering cycle problem in the navigation of a behavior based mobile robot. The algorithm is described, together with some particular considerations about implementation. Efficiency and effectiveness of the proposed approach are verified through simulation and experiments conducted on a Nomad 200 mobile robot.

A virtual target approach is proposed for resolving the limit cycle problem in navigation of a behaviour-based mobile robot. Starting from the onset point of a possible limit cycle path, the real target is switched to a virtual location and the robot is navigated according to the virtual target set up temporarily and the real environment information sensed, until a switching back condition is reached. The condition for switching back to the real target is established using a specific change in the obstacle information sensed. The algorithm is described together with some particular considerations in implementation. Efficiency and effectiveness of the proposed approach are verified through simulation and experiments conducted with a Nomad 200 robot incorporating a fuzzy, behaviour-based controller.