Mechanoreception for Soft Robots via Intuitive Body Cues

A retrofit sensing strategy for soft fluidic robots

Soft robots are intrinsically capable of adapting to different environments by changing their shape in response to interaction forces. However, sensory feedback is still required for higher level decisions. Most sensing technologies integrate separate sensing elements in soft actuators, which presents a considerable challenge for both the fabrication and robustness of soft robots. Here we present a versatile sensing strategy that can be retrofitted to existing soft fluidic devices without the need for design changes. We achieve this by measuring the fluidic input that is required to activate a soft actuator during interaction with the environment, and relating this input to its deformed state. We demonstrate the versatility of our strategy by tactile sensing of the size, shape, surface roughness and stiffness of objects. We furthermore retrofit sensing to a range of existing pneumatic soft actuators and grippers. Finally, we show the robustness of our fluidic sensing strategy in closed-loop control of a soft gripper for sorting, fruit picking and ripeness detection. We conclude that as long as the interaction of the actuator with the environment results in a shape change of the interval volume, soft fluidic actuators require no embedded sensors and design modifications to implement useful sensing.

Prefrontal Cortex Activation during Memory Training by Virtual Drum Beating: A Randomized Controlled Trial

The use of virtual reality (VR) content in neurological disorders with cognitive impairment is increasing. We have developed a device that incorporates virtual drum beating content, designed for digit memorization training. This study aimed to investigate the effects of realistic cognitive training on brain activity using functional near-infrared spectroscopy (fNIRS). Thirty healthy individuals were recruited and randomly assigned into two groups: conventional cognitive exercise (CCE) and a realistic cognitive exergame (RCE). Subjects in the CCE group underwent memory training by memorizing numbers displayed on a computer screen and then writing them on paper. The main outcome measure was the oxyhemoglobin level in the dorsolateral prefrontal cortex (DLPFC). As a result, the average number of digits was 7.86 ± 0.63 for the CCE and 7.6 ± 0.82 for the RCE. The mean difference in ΔHbO was 1.417 ± 0.616 μm (p = 0.029) in channel 2, located in the right DLPFC. Channel 7 and channel 10, which measured activations in the hypothesized medial orbitofrontal cortex (OFC), also showed a significant mean difference of ΔHbO. DLPFC and OFC presented higher activation in the RCE group (p < 0.05), attributable to the simultaneous memory training and virtual drum beating, which provided various sensory inputs (visual, auditory, and vibration). Although DLPFC involvement in cognitive processes remains controversial, our findings suggest that realistic memory training using drumming content can lead to safer activation of the DLPFC compared to conventional cognitive training.

Soft Robot Proprioception Using Unified Soft Body Encoding and Recurrent Neural Network

Compared with rigid robots, soft robots are inherently compliant and have advantages in the tasks requiring flexibility and safety. But sensing the high dimensional body deformation of soft robots is a challenge. Encasing soft strain sensors into the internal body of soft robots is the most popular solution to address this challenge. But most of them usually suffer from problems like nonlinearity, hysteresis, and fabrication complexity. To endow the soft robots with body movement awareness, this work presents a bioinspired architecture by taking cues from human proprioception system. Differing from the popular usage of smart material-based sensors embedded in soft actuators, we created a synthetic analog to the human muscle system, using paralleled soft pneumatic chambers to serve as receptors for sensing body deformation. We proposed to build the system with redundant receptors and explored deep learning tools for generating the kinematic model. Based on the proposed methodology, we demonstrated the design of three degrees of freedom continuum joint and how its kinematic model was learned from the unified pressure information of the actuators and receptors. In addition, we investigated the response of the soft system to receptor failures and presented both hardware and software level solutions for achieving graceful degradation. This approach offers an alternative to enable soft robots with proprioception capability, which will be useful for closed-loop control and interaction with environment.

Jointless Bioinspired Soft Robotics by Harnessing Micro and Macroporosity

Although natural continuum structures, such as the boneless elephant trunk, provide inspiration for new versatile grippers, highly deformable, jointless, and multidimensional actuation has still not been achieved. The challenging pivotal requisites are to avoid sudden changes in stiffness, combined with the capability of providing reliable large deformations in different directions. This research addresses these two challenges by harnessing porosity at two levels: material and design. Based on the extraordinary extensibility and compressibility of volumetrically tessellated structures with microporous elastic polymer walls, monolithic soft actuators are fabricated by 3D printing unique polymerizable emulsions. The resulting monolithic pneumatic actuators are printed in a single process and are capable of bidirectional movements with just one actuation source. The proposed approach is demonstrated by two proof-of-concepts: a three-fingered gripper, and the first ever soft continuum actuator that encodes biaxial motion and bidirectional bending. The results open up new design paradigms for continuum soft robots with bioinspired behavior based on reliable and robust multidimensional motions.

An Underwater Glider with Muscle—Actuated Buoyancy Control and Caudal Fin Turning

Underwater robotic gliders exploit gravity and buoyancy for long-distance cruising with ultra-low energy consumptions, making them ideal for open ocean surveying operations. However, the gliding-based motion generation principle also prevents their maneuverability, limiting their use in the short distances that are usually encountered in harbors or coastal scenarios. In this work, an innovative underwater glider robot is developed, enabling maneuverability through the introduction of an efficiently actuated caudal fin with bidirectional turning capabilities. In addition, modular actuator units, based on soft actuated materials, are integrated to control pitch angle by dynamically shifting the center of mass from the center of buoyancy. As a result, the high energy efficiency feature of the gliders is maintained, while high maneuverability is also achieved. The design concept, modeling of key components, and framework for control are presented, with the prototyped glider tested in a series of bench and field trials for validation of its motion performance.

Underwater Crawling Robot With Hydraulic Soft Actuators

Benthic operation plays a vital role in underwater applications, where crawling robots have advantages compared with turbine-based underwater vehicles, in locomotion accuracy, actuation efficiency, current resistance, and in carrying more payloads. On the other hand, soft robots are quickly trending in underwater robotic design, with their naturally sealed body structure and intrinsic compliance both desirable for the highly unstructured and corrosive underwater environment. However, the limitations resulting directly from the inherent compliance, in structural rigidity, actuation precision, and limited force exertion capability, have also restricted soft robots in underwater applications. To date soft robots are adopted mainly as grippers and manipulators for atraumatic sampling, rather than as locomotion platforms. In this work, we present a soft-robotic approach to designing underwater crawling robots, with three main innovations: 1) using rigid structural components to strategically reinforce the otherwise omni-directionally flexible soft actuators, drastically increasing their loading capability and actuation precision; 2) proposing a rigid–soft hybrid multi-joint leg design, with quasi-linear motion range and force exertion, while maintaining excellent passive impact compliance by exploiting the inherent flexibility of soft actuators; 3) developing a novel valve-free hydraulic actuation system with peristaltic pumps, achieving a compact, lightweight, and untethered underwater crawling robot prototype with a 5:1 payload-to-weight ratio and multi-gait capability. The prototype was tested for design verification and showcasing the advantages of the proposed hybrid mechanism and actuation approach.

Reafference and the origin of the self in early nervous system evolution

Discussions of the function of early nervous systems usually focus on a causal flow from sensors to effectors, by which an animal coordinates its actions with exogenous changes in its environment. We propose, instead, that much early sensing was reafferent; it was responsive to the consequences of the animal's own actions. We distinguish two general categories of reafference-translocational and deformational-and use these to survey the distribution of several often-neglected forms of sensing, including gravity sensing, flow sensing and proprioception. We discuss sensing of these kinds in sponges, ctenophores, placozoans, cnidarians and bilaterians. Reafference is ubiquitous, as ongoing action, especially whole-body motility, will almost inevitably influence the senses. Corollary discharge-a pathway or circuit by which an animal tracks its own actions and their reafferent consequences-is not a necessary feature of reafferent sensing but a later-evolving mechanism. We also argue for the importance of reafferent sensing to the evolution of the body-self, a form of organization that enables an animal to sense and act as a single unit. This article is part of the theme issue 'Basal cognition: multicellularity, neurons and the cognitive lens'.

Failure Handling of Robotic Pick and Place Tasks With Multimodal Cues Under Partial Object Occlusion

The success of a robotic pick and place task depends on the success of the entire procedure: from the grasp planning phase, to the grasp establishment phase, then the lifting and moving phase, and finally the releasing and placing phase. Being able to detect and recover from grasping failures throughout the entire process is therefore a critical requirement for both the robotic manipulator and the gripper, especially when considering the almost inevitable object occlusion by the gripper itself during the robotic pick and place task. With the rapid rising of soft grippers, which rely heavily on their under-actuated body and compliant, open-loop control, less information is available from the gripper for effective overall system control. Tackling on the effectiveness of robotic grasping, this work proposes a hybrid policy by combining visual cues and proprioception of our gripper for the effective failure detection and recovery in grasping, especially using a proprioceptive self-developed soft robotic gripper that is capable of contact sensing. We solved failure handling of robotic pick and place tasks and proposed (1) more accurate pose estimation of a known object by considering the edge-based cost besides the image-based cost; (2) robust object tracking techniques that work even when the object is partially occluded in the system and achieve mean overlap precision up to 80%; (3) contact and contact loss detection between the object and the gripper by analyzing internal pressure signals of our gripper; (4) robust failure handling with the combination of visual cues under partial occlusion and proprioceptive cues from our soft gripper to effectively detect and recover from different accidental grasping failures. The proposed system was experimentally validated with the proprioceptive soft robotic gripper mounted on a collaborative robotic manipulator, and a consumer-grade RGB camera, showing that combining visual cues and proprioception from our soft actuator robotic gripper was effective in improving the detection and recovery from the major grasping failures in different stages for the compliant and robust grasping.

Model-Based Control and External Load Estimation of an Extensible Soft Robotic Arm

Soft robotics has widely been known for its compliant characteristics when dealing with contraction or manipulation. These soft behavior patterns provide safe and adaptive interactions, greatly relieving the complexity of active control policies. However, another promising aspect of soft robotics, which is to achieve useful information from compliant behavior, is not widely studied. This characteristic could help to reduce the dependence of sensors, gain a better knowledge of the environment, and enrich high-level control strategies. In this paper, we have developed a state-change model of a soft robotic arm, and we demonstrate how compliant behavior could be used to estimate external load based on this model. Moreover, we propose an improved version of the estimation procedure, further reducing the estimation error by compensating the influcence of pressure deadzone. Experiments of both methods are compared, displaying the potential effectiveness of applying these methods.

A Cephalopod-Inspired Soft-Robotic Siphon for Thrust Vectoring and Flow Rate Regulation

Cephalopods could simultaneously achieve both accurate positioning and agile bodily maneuvers by coordinating the mantle and the funnel, which is ideal for underwater robotic applications toward a compact propulsor with combined thrust vectoring and regulation. For a wide range of underwater applications from videography to manipulation, this novel approach would offer a compact and integrated alternative to the state-of-the-art with multiple vectoring thrusters. This article presents a biomimetic soft-robotic siphon (BSRS) as the propulsor unit, consisting of a novel central flow-regulative duct (CFRD) encircled by three circumferential siphon actuation muscles (SAMs). Hydraulic pressurization of the SAMs could enable both thrust vectoring by deflecting the BSRS and flow regulation by proportionally alternating the orifice of the CFRD. The design, modeling, and fabrication of the BSRS are presented in detail. Experiments using a prototype BSRS were conducted for validating the performances of deflection deformation and flow regulation, showing bending range of over 180° and flow-restricting capability of up to 100%. A burst effect was achieved with the ability of exceeding the constant flow rate by up to 50%, enabling tremendous thrust increase in very short time. This work proves the feasibility of combining omnidirectional deflection with flow regulation within a soft-robotic mechanism, paving the way to compact water-jetting propulsion for underwater robots.