The annelid, which consists of several identical segments, exploits its soft structures to move effectively in complex natural environments. Elongation and shortening of different segments produce a reverse peristaltic wave while retractable setae generate a variable friction, enabling bidirectional crawling locomotion. Although several designs have applied soft technologies towards the construction of annelid-like robots, these robots do not exhibit the homonymous segmentation, reverse peristaltic wave and variable friction. This paper reports the development of an annelid-like soft robot based on an improved dielectric elastomer (DE) minimum energy structure actuator to have these annelidan features. Each biomimetic segment of the robot is supported by a polyethylene terephthalate (PET) frame adhered to the DE actuator. The DE actuator induces segment elongation or shortening, which causes silica gel pads attached to the PET frame to contact or separate from the ground, producing a variable friction. The designed robot, whose identical segments conform to the homonymous segmentation, achieves forward or backward movement via the cooperative efforts of all the biomimetic segments. This cooperative movement, which produces the reverse peristaltic wave, strongly resembles that of natural annelidan locomotion. In addition, the kinematic analysis of the robot is investigated. Experimental results confirm that the designed robot is capable of bidirectional and rapid locomotion. The robot can achieve a maximum velocity of 11.5 mm s −1 and a maximum velocity/mass ratio of 86.25 mm (min −1 g −1 ). Compared to other existing annelid-like soft robots, this designed robot exhibits a superior average velocity, velocity/length ratio, body length/cycle, and velocity/mass ratio, and its performance affords the best approximation to that of the natural annelid.

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Accurate state-of-health (SOH) diagnosis and remaining useful life (RUL) prediction of lithium-ion batteries (LIBs) play an extremely important role in ensuring safe and reliable operation of electric and hybrid vehicles. However, due to the complex electrochemical properties, it is difficult to achieve the goal of accurate diagnosis and prediction. Here, we propose a novel data-model fusion method to perform accurate SOH estimation and RUL prediction for LIBs, which considers nonlinear dynamics of not only discharging process but also charging process. A long short-term memory (LSTM) network is first employed to model battery SOH dynamics. A neural network (NN) model is then developed to describe battery capacity degradation mechanism according to the prior knowledge extracted from the charging process. Finally, an unscented Kalman filter (UKF) algorithm is incorporated with the LSTM network and NN model to filter out the noises and further reduce the estimation errors. Different from the traditional model fusion approaches, this proposed method uses full information from all sensors, and with no need for any physical model. Experiments and verification demonstrate both the effectiveness of this proposed method and its superior modeling performance as compared with several commonly used methods.INDEX TERMS State-of-health diagnosis, remaining useful life prediction, long short-term memory, model fusion, unscented Kalman filter.

Several remarkable robots inspired by animals, such as fish, [1][2][3] inchworms, [4,5] annelids, [6,7] insects, [8][9][10] geckos, [11,12] and octopuses, [13,14] have attracted significant interest of researchers. These nature-based robots have been created for potential applications in search operations, inspection, cleaning, and manipulation. Among them, the inchworm has a soft and flexible body, simple and distinctive multimodal locomotion, including inverted climbing, vertical climbing, horizontal crawling, and turning, and high adaptability to complex natural environments. Over the decades, these characteristics have inspired the design of several robots with the morphology and locomotion patterns of inchworms. For instance, rigid inchworm-like robots capable of inverted climbing, vertical climbing, and horizontal crawling functions have been developed. [15,16] A study reported a crawling robot composed of servo motors, rigid linkages, and electromagnetic feet that could crawl on and climb pipes and flat surfaces. [5] An inchworm-like capsule robot with rigid expanders and extensors was developed that could crawl inside tubes. [17] However, these robots with rigid drivers and mechanical components were heavy, exhibited complex structures, and operated noisily. Furthermore, as opposed to actual inchworms, the rigid inchworm-like robots failed to adapt suitably to unstructured environments owing to the lack of resilience and flexibility of the components.To overcome the disadvantages of rigidity and endow the robots with flexible body characteristics, researchers have used soft actuator technology to design robots with inchworm-like structures and functions. [18][19][20][21] At present, pneumatics, magnetic fields, shape memory alloys (SMAs), twisted and coiled polymers (TCPs), liquid crystal elastomers (LCEs), ionic polymer-metal composites (IPMCs), and dielectric elastomers (DEs) are the major classes of soft actuators used for designing inchworm-like soft robots. In comparison with the conventional rigid robots, inchworm-like soft robots exhibit certain advantages, such as a simpler structure, higher flexibility, lighter weight, better mobility, and stronger adaptability. [22][23][24] Pneumatic actuators that can generate sufficient deformation and output force have been used to realize soft crawling robots, with vertical climbing and horizontal crawling motions, [25,26] and differential-drive soft robots capable of turning locomotion. [27] Alternatively, a versatile soft crawling robot comprising vacuum-actuated spring actuators and electrostatic footpads can accomplish vertical climbing and turning locomotion while carrying a payload that is 69 times its self-weight on a horizontal plane. [28] However, these robots are heavy, move slowly, and

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