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Many animals exhibit agile mobility in obstructed environments due to their ability to tune their bodies to negotiate and manipulate obstacles and apertures. Most mobile robots are rigid structures and avoid obstacles where possible. In this work, we introduce a new framework named Haptic And Visual Environment Navigation (HAVEN) Architecture to combine vision and proprioception for a deformable mobile robot to be more agile in obstructed environments. The algorithms enable the robot to be autonomously (a) predictive by analysing visual feedback from the environment and preparing its body accordingly, (b) reactive by responding to proprioceptive feedback, and (c) active by manipulating obstacles and gap sizes using its deformable body. The robot was tested approaching differently sized apertures in obstructed environments ranging from greater than its shape to smaller than its narrowest possible size. The experiments involved multiple obstacles with different physical properties. The results show higher navigation success rates and an average 32% navigation time reduction when the robot actively manipulates obstacles using its shape-changing body.

This study investigates the characteristics of a novel origami-based, elastomeric actuator and a soft gripper, which are controlled by hand gestures that are recognized through machine learning algorithms. The lightweight paper–elastomer structure employed in this research exhibits distinct actuation features in four key areas: (1) It requires approximately 20% less pressure for the same bending amplitude compared to pneumatic network actuators (Pneu-Net) of equivalent weight, and even less pressure compared to other actuators with non-linear bending behavior; (2) The control of the device is examined by validating the relationship between pressure and the bending angle, as well as the interaction force and pressure at a fixed bending angle; (3) A soft robotic gripper comprising three actuators is designed. Enveloping and pinch grasping experiments are conducted on various shapes, which demonstrate the gripper’s potential in handling a wide range of objects for numerous applications; and (4) A gesture recognition algorithm is developed to control the gripper using electromyogram (EMG) signals from the user’s muscles.

Space exploration demands innovative robotic solutions to address complex challenges. This article provides a forward‐looking perspective on the emerging field of compliant robotics for space applications, categorizing these systems into reconfigurable, hyper‐redundant, origami‐inspired, and soft robots, each offering unique advantages and facing distinct challenges. The review explores in‐depth the critical roles these compliant robots can assume, ranging from on‐orbit servicing to planetary exploration and beyond. It also addresses material selection, accounting for the harsh conditions of space, and examines the complexities in design, actuation, sensing, and control. The article concludes with a future‐focused discussion of emerging trends, challenges, and research directions. This review aims to offer a comprehensive understanding of the current state of the art, positioning compliant robotics as a transformative force in the next frontier of space exploration. Compliant robots are increasingly becoming integral to space exploration due to their adaptability, flexibility, and lightweight design. This article reviews categories such as soft, reconfigurable, and hyper‐redundant robots and their evolving role in enhancing space missions. With advancements in autonomy and materials, these robots are expected to address challenges posed by long‐duration missions and extreme space environments.

This paper reports on a transformable sheet type robot that can be thrown from the air. Since sheet type robots can change their own shape and perform tasks according to the situation, they are expected to play an active role in situations with many restrictions, such as disaster-stricken areas. However, since most sheet type robots jump or crawl on the ground, the only way to deliver them to the site of a disaster is to transport them by vehicle or transporter. This research aims to develop a device that can be dispersed from the sky and perform activities on the ground after landing.

Animals exhibit astoundingly adaptive and supple locomotion under real world constraints. In order to endow robots with similar capabilities, we must implement many degrees of freedom, equivalent to animals, into the robots' bodies. For taming many degrees of freedom, the concept of autonomous decentralized control plays a pivotal role. However a systematic way of designing such autonomous decentralized control system is still missing. Aiming at understanding the principles that underlie animals' locomotion, we have focused on a true slime mold, a primitive living organism, and extracted a design scheme for autonomous decentralized control system. In order to validate this design scheme, this article presents a soft-bodied amoeboid robot inspired by the true slime mold. Significant features of this robot are twofold: (1) the robot has a truly soft and deformable body stemming from real-time tunable springs and protoplasm, the former is used for an outer skin of the body and the latter is to satisfy the law of conservation of mass; and (2) fully decentralized control using coupled oscillators with completely local sensory feedback mechanism is realized by exploiting the long-distance physical interaction between the body parts stemming from the law of conservation of protoplasmic mass. Simulation results show that this robot exhibits highly supple and adaptive locomotion without relying on any hierarchical structure. The results obtained are expected to shed new light on design methodology for autonomous decentralized control system.

It is necessary for the robot to use interactions from the environment through the body in order to adaptively move through various environments. When the robot is faced with a narrow path or a space with many pillars, it should be able to use its interaction with the environment to thin its own shape, i.e., it should have a flexible body. In contrast, in the case where we want the robot to move forward powerfully on a slope or uneven terrain (small steps), it is preferable for the robot to rigidify its own body and exert a strong propulsive force in response to interactions from the environment. In this paper, we present an idea of a mobile robot that can adjust its body flexibility (stiffness) to realize such adaptive behavior, and furthermore, we demonstrate its validity through experiments. Specifically, we propose a closed-link deformable mobile robot whose stiffness can be adjusted by indirectly driving joints. We design a function that increases the stiffness of the body by controlling the joints to follow the target angle quickly, and a function that decreases the stiffness of the body by controlling the joints to follow the angle slowly. The effectiveness of a robot that can adjust its stiffness is demonstrated through experiments of traversing narrow paths and steps. We also discuss propulsion control that takes advantage of the deformable mobile robot and its applicability to uneven slopes due to the flexibility of the links.

This paper introduces a novel spherical robot, KisBot III, with a newly designed wind-driven driving mechanism. The ducted fan for wind propulsion of the robot is installed at the center of the sphere, and according to the direction of the fan, the robot is able to move forwards or backwards. The outer shell is an open framework of spring carbon rods, and also includes two arms that can be folded-out to make the robot stop and partially deform its shape. Plus, for turning and balance control, a pendulum is located under the ducted-fan frame. By adopting wind as the driving mechanism, the robot has enough propulsion to drive over flat and uneven terrain, and negotiate a raised curb and slope. Experiments verify the driving motions and efficiency of the proposed spherical robot.

Currently, cables are manually installed in aircraft manufacturing scenarios. The utilization of robots in cable assemblies can enhance efficiency and ensure quality, presenting great promise for the future. However, the assembly process must control the shape of the cables, which is a significant challenge for robots. On the one hand, cables have high degrees of freedom in space, making accurate modeling of cable dynamics for robotic arm manipulation difficult; on the other hand, cable deformation has uncertainty, and the applied forces cause simultaneous deformation and motion, which is difficult to control. To address these problems, this study proposes a cable‐shape control method based on graph neural networks and online visual shape‐servoing. The method first approximates cable dynamics with a graph neural network. Then, in practice, the learnt model is used alongside visual‐based shape‐servoing to generate optimal robotic arm movements, controlling the cable to attain the desired shape. In the experiments, precise control of three different cable types is realized, and an example of a completed cable assembly is shown. Herein, a novel cable shape control method is presented using graph neural networks (GNNs) and visual‐based shape servoing for robotic cable assembly in aircraft manufacturing. The approach addresses the challenges of accurately modeling cable dynamics and controlling deformation by approximating cable behavior with a GNN and then guiding dual‐arm robot movement to achieve precise shape control.

Stiffness or elasticity estimation of everyday objects using robot grippers is highly desired for object recognition or classification in application areas like food handling and single-stream object sorting. However, standard robot grippers are not designed for material recognition. We experimentally evaluated the accuracy with which material properties can be estimated through object compression by two standard parallel jaw grippers and a force/torque sensor mounted at the robot wrist, with a professional biaxial compression device used as reference. Gripper effort versus position curves were obtained and transformed into stress/strain curves. The modulus of elasticity was estimated at different strain points and the effect of multiple compression cycles (precycling), compression speed, and the gripper surface area on estimation was studied. Viscoelasticity was estimated using the energy absorbed in a compression/decompression cycle, the Kelvin-Voigt, and Hunt-Crossley models. We found that (1) slower compression speeds improved elasticity estimation, while precycling or surface area did not; (2) the robot grippers, even after calibration, were found to have a limited capability of delivering accurate estimates of absolute values of Young’s modulus and viscoelasticity; (3) relative ordering of material characteristics was largely consistent across different grippers; (4) despite the nonlinear characteristics of deformable objects, fitting linear stress/strain approximations led to more stable results than local estimates of Young’s modulus; and (5) the Hunt-Crossley model worked best to estimate viscoelasticity, from a single object compression. A two-dimensional space formed by elasticity and viscoelasticity estimates obtained from a single grasp is advantageous for the discrimination of the object material properties. We demonstrated the applicability of our findings in a mock single-stream recycling scenario, where plastic, paper, and metal objects were correctly separated from a single grasp, even when compressed at different locations on the object. The data and code are publicly available.