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In order to adapt to complex and changing environments, animals have a wide variety of locomotor forms, which has inspired the investigation of their deformation and driving mechanisms. In this paper, we propose a computational design method for muscle-driven soft robots to satisfy desired deformations, aiming to mimic the deformation behavior of muscle-driven animals in nature. In this paper, we generate the ideal muscle-driven layout for the soft robot by inputting an initial shape and a desired shape, so that it can closely achieve the desired deformation. The material point method is utilized to simulate the soft medium so as to achieve the effect of coupling and coordinated deformation of arbitrary shapes. Our method efficiently searches for muscle layouts corresponding to various deformations and realizes the deformation behaviors of a variety of bio-inspired robots, including soft robots such as bionic snakes, frogs, and human faces. Experimental results show that for both the bionic snake and frog soft robots, the overlap of the geometric contour regions between the actual and simulated deformations is more than 90%, which validates the effectiveness of the method. In addition, the global muscle distributions of the bionic snake and human face soft robots during motion are generated and validated by effective simulation.

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For robots in human environments, learning complex and demanding interaction skills from humans and responding quickly to human motions are highly desirable. A common challenge for interaction tasks is that the robot has to satisfy both the task space and the joint space constraints on its motion trajectories in real time. Few studies have addressed the issue of hyperspace constraints in human-robot interaction, whereas researchers have investigated it in robot imitation learning. In this work, we propose a method of dual-space feature fusion to enhance the accuracy of the inferred trajectories in both task space and joint space; then, we introduce a linear mapping operator (LMO) to map the inferred task space trajectory to a joint space trajectory. Finally, we combine the dual-space fusion, LMO, and phase estimation into a unified probabilistic framework. We evaluate our dual-space feature fusion capability and real-time performance in the task of a robot following a human-handheld object and a ball-hitting experiment. Our inference accuracy in both task space and joint space is superior to standard Interaction Primitives (IP) which only use single-space inference (by more than 33%); the inference accuracy of the second order LMO is comparable to the kinematic-based mapping method, and the computation time of our unified inference framework is reduced by 54.87% relative to the comparison method.

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Although various soft pneumatic actuators have been studied, their performance, including load capacity, has not been satisfied yet. Enhancing their actuation capability and using them to develop soft robots with high performance is still an open and challenging issue. In this study, we developed novel pneumatic actuators based on fiber-reinforced airbags as a solution to this problem, of which the maximum pressure reaches more than 100 kPa. Through cellular rearrangement, the developed actuators could bend uni- or bidirectionally, achieving large driving force, large deformation, and high conformability. Hence, they could be used to develop soft manipulators with relatively large payload (up to 10 kg, about 50 times the body self-weight) and soft climbing robots with high mobility. In this article, we first present the design of the airbag-based actuators and then model the airbag to obtain the relationship between the pneumatic pressure, external force, and deformation. Subsequently, we validate the models by comparing the simulated and measured results and test the load capacity of the bending actuators. Afterward, we present the development of a soft pneumatic robot that can rapidly climb horizontal, inclined, and vertical poles with different cross-sectional shapes and even outdoor natural objects, like bamboos, at a speed of 12.6 mm/s generally. In particular, it can dexterously transition between poles at any angle, which, to the best of our knowledge, has not been achieved before.

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Muscle driving is a critical actuation mode of soft or flexible robots and plays a key role in the motion of most animals. Although the system development of soft robots has been extensively investigated, the general kinematic modeling of soft bodies and the design methods used for muscle-driven soft robots (MDSRs) are inadequate. With a focus on homogeneous MDSRs, this article presents a framework for kinematic modeling and computational design. Based on continuum mechanics theory, the mechanical characteristics of soft bodies were first described using a deformation gradient tensor and energy density function. The discretized deformation was then depicted using a triangular meshing tool according to the piecewise linear hypothesis. Deformation models of MDSRs caused by external driving points or internal muscle units were established by the constitutive modeling of hyperelastic materials. The computational design of the MDSR was then addressed based on kinematic models and deformation analysis. Algorithms were proposed to infer the design parameters from the target deformation and to determine the optimal muscles. Several MDSRs were developed, and experiments were conducted to verify the effectiveness of the presented models and design algorithms. The computational and experimental results were compared and evaluated using a quantitative index. The presented framework of deformation modeling and computational design of MDSRs can facilitate the design of soft robots with complex deformations, such as humanoid faces.

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Electroadhesive grippers can be used to pick up a wide range of materials, and those with variable stiffness functionality can increase load capacity and strength. This paper proposes an electroadhesive gripper (VSEAF) with variable stiffness function and a simple construction based on low melting point alloys (LMPAs) with active form adaptation through pneumatic driving. Resistance wires provide active changing stiffness. For a case study, a three-fingered gripper was designed with three electroadhesive fingers of varied stiffness. It is envisaged that these electroadhesive grippers with variable stiffness would extend the preparation process and boost the use of electroadhesion in soft robot applications.

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In this paper, we proposed a Regular Tetrahedral Array (RTA) to cope with various types of sensors expected in Ultra-Wideband (UWB) localization requiring all-directional detection capability and high accuracy, such as indoor Internet-of-Things (IoT) devices at diverse locations, UAVs performing aerial navigation, collision avoidance and takeoff/landing guidance. The RTA is deployed with four synchronized Ultra-Wideband (UWB) transceivers on its vertexes and configured with arbitrary aperture. An all-directional DOA estimation algorithm using combined TDoA and wrapped PDoA was conducted. The 3D array RTA was decomposed into four planar subarrays solved as phased Uniform Circular Array (UCA) respectively. A new cost function based on geometric identical and variable neighborhood search strategy using TDoA information was proposed for ambiguity resolution. The results of simulation and numerical experiments demonstrated excellent performance of the proposed RTA and corresponding algorithm.

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Similar to the end effectors for traditional rigid robots, those for soft robots are essential as the interacting media between the robots and their environments. Inspired by the forelegs of climbing animals, a passively adaptive soft gripper (ASG), with six claws and a compliant mechanism, is developed for grasping objects and attaching to rough surfaces. The design method, grasp adaptability, form closure, and force equilibrium of the ASG are presented and analyzed in this paper. Due to the compliance at each claw root, the ASG possesses a high passive adaption to various objects. With sharp hooks, a form closure may be achieved easily when the ASG grasps rough objects with structured or unstructured shapes. The ASG grasping an object constitutes an under-actuated system, the solution to which is difficult to obtain. The Monte Carlo method is suggested to achieve effective solutions for such systems, and the in-hull rate is proposed to evaluate the difficulty of finding solution. Tests and experiments with the ASG grasping various objects have verified the adaptability and reliability of the ASG. The analytic and experimental results show that the novel ASG may be used as a universal gripper for soft manipulators and a prospective attaching device for biped climbing soft robots.

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We propose an efficient spectral clustering method for large-scale data. The main idea in our method consists of employing random Fourier features to explicitly represent data in kernel space. The complexity of spectral clustering thus is shown lower than existing Nyström approximations on large-scale data. With m training points from a total of n data points, Nyström method requires O(nmd+m3+nm2) operations, where d is the input dimension. In contrast, our proposed method requires O(nDd+D3+n'D2) , where n' is the number of data points needed until convergence and D is the kernel mapped dimension. In large-scale datasets where n' << n hold true, our explicitly mapping method can significantly speed up eigenvector approximation and benefit prediction speed in spectral clustering. For instance, on MNIST (60 000 data points), the proposed method is similar in clustering accuracy to Nyström methods while its speed is twice as fast as Nyström.

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In kernel methods, Nyström approximation is a popular way of calculating out-of-sample extensions and can be further applied to large-scale data clustering and classification tasks. Given a new data point, Nyström employs its empirical affinity vector, k, for calculation. This vector is assumed to be a proper measurement of the similarity between the new point and the training set. In this paper, we suggest replacing the affinity vector by its projections on the leading eigenvectors learned from the training set, i.e., using k*=∑i=1ckTuiui instead, where ui is the i-th eigenvector of the training set and c is the number of eigenvectors used, which is typically equal to the number of classes designed by users. Our work is motivated by the constraints that in kernel space, the kernel-mapped new point should (a) also lie on the unit sphere defined by the Gaussian kernel and (b) generate training set affinity values close to k. These two constraints define a Quadratic Optimization Over a Sphere (QOOS) problem. In this paper, we prove that the projection on the leading eigenvectors, rather than the original affinity vector, is the solution to the QOOS problem. The experimental results show that the proposed replacement of k by k* slightly improves the performance of the Nyström approximation. Compared with other affinity matrix modification methods, our k* obtains comparable or higher clustering performance in terms of accuracy and Normalized Mutual Information (NMI).

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This paper describes a novel methodology for burst detection in a water distribution system. The proposed method has two stages. In the first stage, a clustering algorithm was employed for outlier detection, while the second stage identified the presence of bursts. An important feature of this method is that data analysis is carried out dependent on multiple flow meters whose measurements vary simultaneously in a district metering area (DMA). Moreover, the clustering-based method can automatically cope with non-stationary conditions in historical data; namely, the method has no prior data selection process. An example application of this method has been implemented to confirm that relatively large bursts (simulated by flushing) with short duration can be detected effectively. Noticeably, the method has a low false positive rate compared with previous studies and appearance of detected abnormal water usage consists with weather changes, showing great promise in real application to multi-inlet and multi-outlet DMAs.

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For a biped climbing robot with dual grippers to climb poles, trusses or trees, feasible collision-free climbing motion is inevitable and essential. In this paper, we utilize the sampling-based algorithm, Bi-RRT, to plan single-step collision-free motion for biped climbing robots in spatial trusses. To deal with the orientation limit of a 5-DoF biped climbing robot, a new state representation along with corresponding operations including sampling, metric calculation and interpolation is presented. A simple but effective model of a biped climbing robot in trusses is proposed, through which the motion planning of one climbing cycle is transformed to that of a manipulator. In addition, the pre- and post-processes are introduced to expedite the convergence of the Bi-RRT algorithm and to ensure the safe motion of the climbing robot near poles as well. The piecewise linear paths are smoothed by utilizing cubic B-spline curve fitting. The effectiveness and efficiency of the presented Bi-RRT algorithm for climbing motion planning are verified by simulations.