Soft origami continuum robots have attracted considerable attention because of their flexibility and adaptability, but they face challenges in control accuracy. This study presents a novel design and control scheme for large-deformation hybrid-actuated soft origami continuum robots to improve their motion performance in real tasks. An origami-based pneumatic chamber is used as the robot backbone to achieve a high extension ratio, and the hybrid variable stiffness principle combining antagonistic actuation and layer jamming further enhances the robot's overall structural stiffness. The robot performs precise movements by controlling tendons distributed in an external origami. An iterative training strategy based on long short-term memory is employed to model the inverse kinematics of the robot in consideration of the inherent hysteresis of origami robots. Step size features are introduced to improve model accuracy with limited data. The capability for variable stiffness enables the migration of the training model on the basis of a single segment, the accuracy of which is close to the submillimeter level. Decoupling control of each segment for a rear-driven multi-segment origami continuum robot is also achieved. Experiment results reveal that the trajectory tracking errors for single-segment and multi-segment of the robot are 1.58, and 2.81 mm, respectively, with relative errors of 0.71% and 0.63% over the robot length, demonstrating the good performance of the proposed design and control method. Orientation data can also be added to the dataset to achieve orientation control of the robot, with an angle error of 0.75°. The robot shows its stability and safety in a series of real tasks, including writing, LED tracking, board cleaning, and pick and place. This study demonstrates a potential solution for continuum robots performing precise tasks without any sensory feedback in confined spaces and specialized environments where electronic sensors fail.

Aiming at the robust force tracking challenge for robots in continuous contact with uncertain environments, a novel adaptive variable impedance control policy based on deep reinforcement learning (DRL) is proposed in this article. The policy includes a neural network feedforward controller and a variable impedance feedback controller. Based on the DRL algorithm, the iterative network feedforward controller explores and prelearns the optimal policy for impedance tuning in simulation scenarios with randomly generated terrain. The converged results are then used as feedforward inputs in the variable impedance feedback controller to improve the force-tracking performance of the robot during contact. A simplified dynamic contact model between the robot and the uncertain environment called the “couch model,” which satisfies the Lipschiz continuity condition, is developed to provide boundary conditions for the safe transfer of capabilities learned in simulation to real robots. Unlike the exhaustive example that relies on the completeness of the learning samples, this article gives theoretical proofs of the stability and convergence of the proposed control policy via Lyapunov’s theorem and contraction mapping principle. The control method proposed in this article is more interpretable and shows higher sample utilization efficiency and generalization ability in simulations and experiments.

Soft continuum robots have shown tremendous potential for medical and industrial applications owing to their flexibility and continuous deformability. However, their telescopic and bending capabilities and variable stiffness are still limited. This study proposes a novel origami-inspired soft continuum robot to possess large telescopic and bending capabilities while improving stiffness based on the principle of antagonistic actuation. The soft robot consists of dual origami structures. The inner forms an air chamber actuated by pneumatics, and the outer is controlled by nine tendon-driven actuators. The proposed design uses the advantages of a hybrid actuation to achieve motion and stiffness control. The performance of the soft robot is studied experimentally based on single and three robot modules. Results show that the robot has an excellent stretch ratio and a maximum bending angle of 180°. The robot can also increase stiffness to resist the bending deformation induced by self-weight and loads.

Soft climbing robots have elicited widespread attention for their potential applications in inspection, maintenance, and search and rescue tasks. However, these robots face challenges in terms of their adaptability and motion capability in various surface environments, such as surfaces with a large gap or nonflat surfaces. In this article, we develop a soft modular climbing robot called Smcbot, which is assembled based on several soft bodies and feet modules. The soft body module provides large-scale telescopic motion and high output load through two soft actuators made of shape memory alloy wires. Meanwhile, the feet modules provide considerable adhesion on different surfaces due to the applied adhesive strategy. A theoretical model is also developed to predict the output performance (e.g., stiffness and output displacement) of the soft body, and the results are verified through experiments. The adaptability and motion capability of Smcbot is demonstrated on flat and nonflat surfaces with different properties, such as surfaces with a large gap and those with smooth and rough surfaces. This article uses the modular design to expand the application scope of soft climbing robots and to facilitate their adaption to various challenging environments with low time consumption and cost.

LiDAR is widely adopted in Simultaneous Localization And Mapping (SLAM) and High Definition (HD) map production. The accuracy of LiDAR Odometry (LO) is of great importance, especially in GPS-denied environments. However, we found typical LO results are prone to drift upwards along the vertical direction in underground parking lots, leading to poor mapping results. This paper proposes a Ground Constrained LO method named GCLO, which exploits planar grounds in these specific environments to compress the vertical pose drifts. GCLO is divided into three parts. First, a sensor-centric sliding map is maintained, and the point-to-plane ICP method is implemented to perform the scan-to-map registration. Then, at each key-frame, the sliding map is recorded as a local map. Ground points nearby are segmented and modeled as a planar landmark in the form of Closest Point (CP) parameterization. Finally, planar ground landmarks observed at different key-frames are associated. The ground landmark observation constraints are fused into the pose graph optimization framework to improve the LO performance. Experimental results in HIK and KITTI datasets demonstrate GCLO's superior performances in terms of accuracy in indoor multi-floor parking lots and flat outdoor sites. The limitation of GCLO in adaptability for other environments is also discussed.

Soft grippers can be used to grasp objects with various geometric surface structures or stiffness but typically encounter difficulty in providing high grasping force. Although the combination of soft grippers with adhesive technology can increase their load capacity, this approach has disadvantages of compatibility and limited application range. This article has reported a bioinspired design of a soft gripper that integrates flat dry adhesive, soft actuator, and microspine to improve the comprehensive grasping ability of the soft gripper on smooth or rough surfaces. The adhesive strength of flat dry adhesives with different thickness or cross-linking ratios was investigated to ensure that a large grasping force can be provided. Soft actuators with uniform and nonuniform cross-sectional heights were compared, and results indicated that the soft actuator with a nonuniform cross-sectional height exhibited remarkable advantages for designing the integrated gripper. A microspine-spring-shape memory alloy coil structure was designed to control the retraction and protrusion of microspine and distribute the load on rough surfaces evenly. The proposed integrated gripper with the aforementioned design can lift regularly or irregularly shaped objects with smooth or rough surfaces and provide a higher adhesive force than the nonadhesive gripper. After inserting a flexible pressure film sensor into the soft gripper, the surface property of the grasped object can be measured, which is beneficial for the selection of a suitable grasping strategy in unknown environments. The designed gripper can be used in many applications, such as in unmanned aerial vehicles, industrial manipulators, and climbing robots.

Animals in nature, such as geckos, inchworms, and felines can climb on various surfaces using different mechanisms and serve as references for the study of bio-inspired robots. This paper presents an inchworm-inspired climbing robot that consists of soft body and feet. The soft robot is actuated by shape memory alloy wires and utilizes microspine arrays to attach its feet to rough or soft surfaces. A series of experiments to test the functionality of the feet and torso of the designed robot have verified the theoretical feasibility of the robot. Results have shown that the designed bio-inspired robot can climb on inclined or vertical curved surfaces and flat surfaces. The robot can also adapt to the underwater environment. Thus, this robot has great potential for various applications such as pipeline inspection.

This paper presents a bionic soft robot with chromatic surface micro-structure (CSM), as a new approach for the measurement of body deformation of the soft robots. Firstly, the CSM films are fabricated by diffraction gratings mold using material polydimethylsiloxane (PDMS). Then, the CSM films are attached to the surface of the soft actuators based on shape memory alloy (SMA) wires. By recording and matching the pattern through two cameras, the continuous deformation curvature degrees of SMA soft actuators are attained. Finally, by integrating three actuators mentioned previously, a bio-inspired prototype of three-legged soft robot is manufactured to carry out motions of both freely and grasp obstacles. By recording and matching the pattern of the robot, we demonstrated a non-contact deformation measurement for flexible devices. As a new non-contact measurement method for body deformation of soft robots, it may shed some light on the issues of the soft robots' sensing and controlling in the future.

Animals provide inspiration for developing soft machines in bionics, robotics research as well as potential applications. This paper presents an integrated development of locomotive soft robot platforms: starfish-inspired robots with multi-limb bodies actuated by shape memory alloys (SMAs). The designs of these robots were based on the biological specifications of the locomotion and water-vascular systems of regenerating starfish. Multi-limb robot prototypes were fabricated with soft materials and 3D printing technology, satisfying modest movement requirements with SMA spring actuators. Experimental testing of multi-limb robots were conducted in accordance with biological locomotion principles. The robots vividly displayed typical starfish motion models. Animals provide inspiration for developing soft machines in bionics, robotics research as well as potential applications.

This paper proposes a novel and mechanically decoupled leg and wheel hybrid transformable robot called HyTRo-I that combines two mobility concepts. For example, while wheeled vehicles shares higher speed than legged and tracked machines on a flat ground, they have relatively lower degree of flexibility than the other two on irregular terrain. The HyTRo-I robot evolves three motion modes: wheeled rolling, quadrupedal walking and leg-wheel hybrid mode. Despite the over-whelming complexity of obstacles, only several typical obstacles are selected for the study, which are stairs, large protrusions and ditches. Firstly, the transition locomotion mechanism between wheeled rolling mode and quadrupedal walking mode should be studied in detail. In the course of reciprocal transition locomotion, the static and reversible transformation gait not only guarantees the shifting stability and a small number of transition steps, but also the relatively balanced torque of joints. Secondly, after HyTRo-I converting to effective locomotion mode, the adaptive gait control strategies are proposed to traverse three types of obstacles. Finally, a serial of experiments were performed to verify the validity of the proposed transformation gait and adaptive step-up gaits.