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Soft pneumatic actuators (SPAs) offer a promising alternative for biomedical applications requiring high sensitivity and precise manipulation due to their inherent compliance. 3D- printed multi-modal zig-zag SPAs exhibit potential in this area by achieving repeatable and precise shape changes due to their chambered design. However, achieving accurate position control remains a challenge. This work proposes a hybrid control strategy for multi-modal zig-zag SPAs that combines feed-forward and proportional-integral-derivative (PID) control to enhance positioning accuracy. A Pseudo Rigid Body (PRB) based inverse dynamic model is employed for the feed-forward component. The effectiveness of the controller is evaluated through extensive simulations and experiments. Results demonstrate that the hybrid control strategy achieves up to 29.5% and 31.6% improvement in accuracy compared to the PID and feed-forward controllers, respectively, within the operational bandwidth. 

Soft robots, due to their flexibility, adaptability, and gentle handling over rigid robots, have shown better potential in numerous applications requiring operating in constrained spaces. Most of the soft robotic prototypes are of a linear form that can be modeled as a curve in space and are found in manipulators and limbs of locomoting robots. Planar soft robots have been proposed recently that are modeled as a surface and deform in 3D. Research on planar soft robots has been less extensive due to the challenges associated with modeling surface deformations efficiently. We present a curve-parametric approach for the deformation modeling of planar soft robot modules. Along with the Bezier patch method to approximate the surface at 30 Hz. Experimental evaluations on a prototype were developed and tested to validate that the proposed model can reasonably approximate the planar robot boundaries, and the surface derived from it. 

Multisection continuum arms are bio-inspired manipulators that combine compliance, payload, dexterity, and safety to serve as co-robots in human-robot collaborative domains. Their hyper redundancy and complex kinematics, however, pose many challenges when performing path planning, especially in dynamic environments. In this paper, we present a W-Space based Rapidly Exploring Random Trees * path planner for multisection continuum arm robots in dynamic environments. The proposed planner improves the existing state-of-art planners in terms of computation time and the success rate, while removing the need for offline computation. On average, the computation time of our approach is below 2 seconds, and its average success rate is around 70 %. The computation time of the proposed planner significantly improves that of the state-of-the-art planner by roughly a factor of 20, making the former suitable for real-time applications. Moreover, for application domains where the obstacle motion is not very predictable (e.g., human obstacles), the proposed planner significantly improves the success rate of state-of-the-art planners by nearly 50 %. Lastly, we demonstrate the feasibility of several generated trajectories by replicating the motion on a physical prototype arm. 

Soft robots, known for their compliance and deformable nature, have emerged as a transformative field, giving rise to various prototypes and locomotion capabilities. Despite continued research efforts that have shown significant promise, the quest for energy-efficient mobility in soft-limbed robots remains relatively elusive. We introduce a discrete locomotion gait called “tumbling,” designed to conserve energy and implemented in a topologically symmetric soft-limbed robot. The incorporation of tumbling enhances the overall locomotive abilities of soft-limbed robots, offering advantages such as increased agility, adaptability, and the ability to correct orientation, which are essential for navigating non-engineered environments that include natural-like irregular terrains with obstacles. The principle behind tumbling locomotion involves a deliberate shift in the robot's center of gravity in the direction of motion, guided by the kinematics of its soft limbs. To validate this locomotion strategy, we developed a robot simulation model operating within a virtual environment that incorporates physics and contact interactions. After optimizing the tumbling locomotion strategy through simulations, we conducted experimental tests on a physical robot prototype. The experiments validate the effectiveness of the proposed tumbling gait. We conducted an energy cost analysis to compare the tumbling locomotion with the previously reported crawling gait of the robot. The results of this analysis demonstrate that tumbling represents an energy-efficient mode of locomotion for soft robots, saving up to 60% and 65% energy than crawling locomotion on flat and natural-like irregular terrains, respectively. 

The multimodal Zig-zag Soft Pneumatic Actuator (SPA) provides an effective design approach for achieving de- sired extensions and bending geometries under specific pressure conditions. The rigid body approximated model introduced in this study brings valuable insights into SPA dynamics by enabling faster simulations when compared to methods such as Finite Element Analysis (FEA). The model outlined in this paper forecasts static behavior by estimating the linear expansion of linear SPA and the bending angle of bending SPA. These two modes of motion can be combined to expand the degree of freedom. Depending on the configuration of the Strain Limiting Layer (SLL), the bending angle can be adjusted by controlling the actuator stiffness, a parameter that can be precisely characterized using the proposed actuator model. To address the hysteresis phenomena in linear expansion SPA, the Bouc-Wen hysteresis model is employed to model the actuator hysteresis responses at higher actuation rates. The validity of the proposed model is experimentally confirmed through the use of 3D-printed SPA prototypes that are designed for both extension and bending actuation. 

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. 

When threatened by dangerous or harmful stimuli, animals engage in diverse forms of rapid escape behaviors. InDrosophilalarvae, one type of escape response involves C-shaped bending and lateral rolling followed by rapid forward crawling. The sensory circuitry that promotes larval escape has been extensively characterized; however, the motor programs underlying rolling are unknown. Here, we characterize the neuromuscular basis of rolling escape behavior. We used high-speed, volumetric, Swept Confocally Aligned Planar Excitation (SCAPE) microscopy to image muscle activity during larval rolling. Unlike sequential peristaltic muscle contractions that progress from segment to segment during forward and backward crawling, muscle activity progresses circumferentially during bending and rolling escape behavior. We propose that progression of muscular contraction around the larva’s circumference results in a transient misalignment between weight and the ground support forces, which generates a torque that induces stabilizing body rotation. Therefore, successive cycles of slight misalignment followed by reactive aligning rotation lead to continuous rolling motion. Supporting our biomechanical model, we found that disrupting the activity of muscle groups undergoing circumferential contraction progression leads to rolling defects. We use EM connectome data to identify premotor to motor connectivity patterns that could drive rolling behavior and perform neural silencing approaches to demonstrate the crucial role of a group of glutamatergic premotor neurons in rolling. Our data reveal body-wide muscle activity patterns and putative premotor circuit organization for execution of the rolling escape response. 

Image data plays a pivotal role in the current data-driven era, particularly in applications such as computer vision, object recognition, and facial identification. Google Maps ® stands out as a widely used platform that heavily relies on street view images. To fulfill the pressing need for an effective and distributed mechanism for image data collection, we present a framework that utilizes smart contract technology and open-source robots to gather street-view image sequences. The proposed framework also includes a protocol for maintaining these sequences using a private blockchain capable of retaining different versions of street views while ensuring the integrity of collected data. With this framework, Google Maps ® data can be securely collected, stored, and published on a private blockchain. By conducting tests with actual robots, we demonstrate the feasibility of the framework and its capability to seamlessly upload privately maintained blockchain image sequences to Google Maps ® using the Google Street View ® Publish API. 

“Data is the new oil” has become a popular catch-phrase in the world of technology, emphasizing the immense value of data in today's digital age. Most services and platforms rely on data, but collecting this data can be challenging and costly. To address this issue, we leverage a novel distributed crowdsourcing framework - termed Swarm Contracts - that utilizes blockchain and is applied to robotics technologies. The framework encourages an incentivized crowdsourcing model through open-source robots and a secure, decentralized, and transparent blockchain-based incentive system. As a demonstration of the framework's capabilities, we use it to collect Google Street View ® map data, which can be a resource-intensive task to keep up to date using traditional centralized methods. Our Swarm Contract framework uses Google Street View ® Publish API, which allows for the contribution of street view data to Google Maps @to implement the incentive-based crowdsourcing of street view images. By incorporating a swarm contract-powered framework with the Google Street View ® Publish API, we show that the incentivized crowdsourcing of street view data can be a practical solution to maintain accurate and up-to-date Google Street View ® maps.