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Abstract Soft robots can undergo large elastic deformations and adapt to complex shapes. However, they lack the structural strength to withstand external loads due to the intrinsic compliance of fabrication materials (silicone or rubber). In this paper, we present a novel stiffness modulation approach that controls the robot’s stiffness on-demand without permanently affecting the intrinsic compliance of the elastomeric body. Inspired by concentric tube robots, this approach uses a Nitinol tube as the backbone, which can be slid in and out of the soft robot body to achieve robot pose or stiffness modulation. To validate the proposed idea, we fabricated a tendon-driven concentric tube (TDCT) soft robot and developed the model based on Cosserat rod theory. The model is validated in different scenarios by varying the joint-space tendon input and task-space external contact force. Experimental results indicate that the model is capable of estimating the shape of the TDCT soft robot with an average root-mean-square error (RMSE) of 0.90 (0.56% of total length) mm and average tip error of 1.49 (0.93% of total length) mm. Simulation studies demonstrate that the Nitinol backbone insertion can enhance the kinematic workspace and reduce the compliance of the TDCT soft robot by 57.7%. Two case studies (object manipulation and soft laparoscopic photodynamic therapy) are presented to demonstrate the potential application of the proposed design. 

The growing number of applications in Cyber-Physical Systems (CPS) involving different types of robots while maintaining interoperability and trust is an ongoing challenge faced by traditional centralized systems. This paper presents what is, to the best of our knowledge, the first integration of the Robotic Operating System (ROS) with the Ethereum blockchain using physical robots. We implement a specialized smart contract framework called “Swarm Contracts” that rely on blockchain technology in real-world applications for robotic agents with human interaction to perform collaborative tasks while ensuring trust by motivating the agents with incentives using a token economy with a self-governing structure. The use of open-source technologies, including robot hardware platforms such as TurtleBot3, Universal Robot arm, and ROS, enables the ability to connect a wide range of robot types to the framework we propose. Going beyond simulations, we demonstrate the robustness of the proposed system in real-world conditions with actual hardware robots. 

Soft robots are inherently compliant and have a strong potential to realize human-friendly and safe robots. Despite continued research highlighting the potential of soft robots, they remain largely confined to laboratory settings. In this work, inspired by spider monkeys' tails, we propose a hybrid soft robot (HSR) design. We detail the design objectives and methodology to improve the controllable stiffness range and achieve independent stiffness and shape control. We extend the curve parametric approach to obtain a kinematic model of the proposed HSR. We experimentally demonstrate that the proposed HSR has about 100% stiffness range increase than a previous soft robot design with identical physical dimensions. In addition, we empirically map HSR's bending shape-pressure-stiffness and present an application example-a soft robotic gripper-to demonstrate the decoupled nature of stiffness and shape variations. Experimental results show that proposed HSR can be successfully 

The use of blockchain in cyber-physical systems, such as robotics, is an area with immense potential to address many shortcomings in robotic coordination and control. In traditional swarm robotic applications, where homogeneous robots are utilized, it is possible to replace a robot if it malfunctions, and it can be assumed that all robots are interchangeable. However, in many real-world applications spanning from search and rescue missions to future household robotic appliances, heterogeneous robots will need to work together with the other robots and human agents to achieve specific tasks. Nevertheless, no such system exists. Therefore, we propose a system that utilizes a token economy for robotic agents that makes agents responsive to token acquisition as an incentive for collaboration in achieving a given task. The economy enables the system to self-govern, even under Byzantine and adversarial settings. We further incorporate a novel subcontracting framework within a blockchain environment to allow the robotic agents to efficiently and cost-effectively perform complex jobs requiring multiple agents with various capabilities. We conducted a thorough evaluation of the system in a prototype warehouse application scenario, and the results are promising. 

Snakes are a remarkable evolutionary success story. Numerous snake-inspired robots have been proposed over the years. Soft robotic snakes (SRS), with their continuous and smooth bending capability, can better mimic their biological counterparts' unique characteristics. Prior SRSs are limited to planar operation with a limited number of planar gaits. We propose a novel SRS with spatial bending ability and investigate snake locomotion gaits beyond the planar gaits of the state-of-the-art systems. We derive a complete floating-base kinematic model of the SRS and use the model to derive joint-space trajectories for serpentine and inward/outward rolling locomotion gaits. These gaits are experimentally validated under varying frequency and amplitude of gait cycles. The results qualitatively and quantitatively validate the proposed SRSs' ability to leverage spatial bending to achieve locomotion gaits not possible with current SRS designs. 

Compliant grasping is crucial for secure handling objects not only vary in shapes but also in mechanical properties. We propose a novel soft robotic gripper with decoupled stiffness and shape control capability for performing adaptive grasping with minimum system complexity. The proposed soft fingers conform to object shapes facilitating the handling of objects of different types, shapes, and sizes. Each soft gripper finger has a length constraining mechanism (an articulable rigid backbone) and is powered by pneumatic muscle actuators. We derive the kinematic model of the gripper and use an empirical approach to simultaneously map input pressures to stiffness control and bending deformation of fingers. We use these mappings to demonstrate decoupled stiffness and shape (bending) control of various grasping configurations. We conduct tests to quantify the grip quality when holding objects as the gripper changes orientation, the ability to maintain the grip as the gripper is subjected to translational and rotational movements, and the external force perturbations required to release the object from the gripper under various stiffness and shape (bending) settings. The results validate the proposed gripper's performance and show how the decoupled stiffness and shape control can improve the grasping quality in soft robotic grippers. 

Continuum arms, with their mix of compliance, payload, safety, and manipulability, are perfectly suited to serve as co-robots, and their applications range from industry and manufacturing to human healthcare. Their hyper-redundancy serves as their most significant challenge for path planning and path planning approaches commonly used with rigid-link robots, such as inverse kinematics, that fail to provide reliable trajectories for continuum arms. We propose an Inverse Kinematics-based approach to address the limitations of previously-proposed Kinematics-based approaches. Using this new approach, we are able to efficiently generate very rich sets of configurations, which, in turn, lead to smooth path planning for such continuum manipulators. To validate the smoothness of the paths generated by our approach, we apply dynamics constraints to the generated trajectories. We show that, when tracked by a controller, the paths that are generated using the proposed approach are much smoother than previously-proposed Kinematics-based approaches: The proposed approach allows the continuum arm to traverse the trajectories very accurately and in time less than half of that taken by previous (reliable) path planning approaches. 

Continuum arms are more adaptable to their environments and inherently human-friendly compared to their rigid counterparts. Path planning of continuum arms is an active research area with many challenges. The hyper-redundancy of continuum arms, which renders them highly versatile, is their curse in path planning. This problem becomes even more challenging in dynamic environments in the presence of mobile obstacles. In this paper, we propose an anticipatory path planning approach for continuum arms in dynamic environments. Our approach is based on obstacle prediction coupled with temporal graphs to model the dynamic environment. We evaluate the proposed approach’s performance and compare it to prevailing path planning approaches for continuum arms in dynamic environments.