Visually guided swarm motion coordination via insect-inspired small target motion reactions.

Despite progress developing experimentally-consistent models of insect in-flight sensing and feedback for individual agents, a lack of systematic understanding of the multi-agent and group performance of the resulting bio-inspired sensing and feedback approaches remains a barrier to robotic swarm implementations. This study introduces the small-target motion reactive (STMR) swarming approach by designing a concise engineering model of the small target motion detector (STMD) neurons found in insect lobula complexes. The STMD neuron model identifies the bearing angle at which peak optic flow magnitude occurs, and this angle is used to design an output feedback switched control system. A theoretical stability analysis provides bi-agent stability and state boundedness in group contexts. The approach is simulated and implemented on ground vehicles for validation and behavioral studies. The results indicate despite having the lowest connectivity of contemporary approaches (each agent instantaneously regards only a single neighbor), STMR achieves collective group motion. STMR group level metric analysis also highlights continuously varying polarization and decreasing heading variance.

Material Scrunching Enables Working Channels in Miniaturized Vine-Inspired Robots.

A new subclass of soft robot, known as tip-extending or "vine" robots, consists of long inflatable devices that move through the environment by extending from the tip. A key requirement for many applications of these robots is a working channel-a hollow tube through the core of the robot for passing tools, sensors, fluids, etc. While working channels have been proposed in a few vine robots, it remains an open challenge to create miniaturized vine robots (diameter < 1 cm) with working channels that enable continuous access through the core. In this paper, we analyze the growth models of current vine robot designs and show that the working channel greatly increases required pressure to grow at small scales due to internal friction. Based on this insight, we propose the concept of storing scrunched material at the tip of the vine robot to circumvent this frictional force. We validate our models and demonstrate this concept via prototypes down to diameters of 2.3 mm. Overall, this work enables the creation of miniaturized vine robots with working channels, which significantly enhances their practicality and potential for impact in applications such as minimally invasive surgery.

Bio-Inspired Space Robotic Control Compared to Alternatives.

Controlling robots in space with necessarily low material and structural stiffness is quite challenging at least in part due to the resulting very low structural resonant frequencies or natural vibration. The frequencies are sometimes so low that the very act of controlling the robot with medium or high bandwidth controllers leads to excitation of resonant vibrations in the robot appendages. Biomimetics or biomimicry emulates models, systems, and elements of nature for solving such complex problems. Recent seminal publications have re-introduced the viability of optimal command shaping, and one recent instantiation mimics baseball pitching to propose control of highly flexible space robots. The readership will find a perhaps dizzying array of thirteen decently performing alternatives in the literature but could be left bereft selecting a method(s) deemed to be best suited for a particular application. Bio-inspired control of space robotics is presented in a quite substantial (perhaps not comprehensive) comparison, and the conclusions of this study indicate the three top performing methods based on minimizing control effort (i.e., fuel) usage, tracking error mean, and tracking error deviation, where 96%, 119%, and 80% performance improvement, respectively, are achieved.

Air-to-land transitions: from wingless animals and plant seeds to shuttlecocks and bio-inspired robots.

Recent observations of wingless animals, including jumping nematodes, springtails, insects, and wingless vertebrates like geckos, snakes, and salamanders, have shown that their adaptations and body morphing are essential for rapid self-righting and controlled landing. These skills can reduce the risk of physical damage during collision, minimize recoil during landing, and allow for a quick escape response to minimize predation risk. The size, mass distribution, and speed of an animal determine its self-righting method, with larger animals depending on the conservation of angular momentum and smaller animals primarily using aerodynamic forces. Many animals falling through the air, from nematodes to salamanders, adopt a skydiving posture while descending. Similarly, plant seeds such as dandelions and samaras are able to turn upright in mid-air using aerodynamic forces and produce high decelerations. These aerial capabilities allow for a wide dispersal range, low-impact collisions, and effective landing and settling. Recently, small robots that can right themselves for controlled landings have been designed based on principles of aerial maneuvering in animals. Further research into the effects of unsteady flows on self-righting and landing in small arthropods, particularly those exhibiting explosive catapulting, could reveal how morphological features, flow dynamics, and physical mechanisms contribute to effective mid-air control. More broadly, studying apterygote (wingless insects) landing could also provide insight into the origin of insect flight. These research efforts have the potential to lead to the bio-inspired design of aerial micro-vehicles, sports projectiles, parachutes, and impulsive robots that can land upright in unsteady flow conditions.

Gecko adhesion based sea star crawler robot.

Over the years, efforts in bioinspired soft robotics have led to mobile systems that emulate features of natural animal locomotion. This includes combining mechanisms from multiple organisms to further improve movement. In this work, we seek to improve locomotion in soft, amphibious robots by combining two independent mechanisms: sea star locomotion gait and gecko adhesion. Specifically, we present a sea star-inspired robot with a gecko-inspired adhesive surface that is able to crawl on a variety of surfaces. It is composed of soft and stretchable elastomer and has five limbs that are powered with pneumatic actuation. The gecko-inspired adhesion provides additional grip on wet and dry surfaces, thus enabling the robot to climb on 25° slopes and hold on statically to 51° slopes.

An online learning algorithm for adapting leg stiffness and stride angle for efficient quadruped robot trotting.

Animals adjust their leg stiffness and stride angle in response to changing ground conditions and gait parameters, resulting in improved stability and reduced energy consumption. This paper presents an online learning algorithm that attempts to mimic such animal behavior by maximizing energy efficiency on the fly or equivalently, minimizing the cost of transport of legged robots by adaptively changing the leg stiffness and stride angle while the robot is traversing on grounds with unknown characteristics. The algorithm employs an approximate stochastic gradient method to change the parameters in real-time, and has the following advantages: (1) the algorithm is computationally efficient and suitable for real-time operation; (2) it does not require training; (3) it is model-free, implying that precise modeling of the robot is not required for good performance; and (4) the algorithm is generally applicable and can be easily incorporated into a variety of legged robots with adaptable parameters and gaits beyond those implemented in this paper. Results of exhaustive performance assessment through numerical simulations and experiments on an under-actuated quadruped robot with compliant legs are included in the paper. The robot platform used a pneumatic piston in each leg as a variable, passive compliant element. Performance evaluation using simulations and experiments indicated that the algorithm was capable of converging to near-optimal values of the cost of transport for given operating conditions, terrain properties, and gait characteristics with no prior knowledge of the terrain and gait conditions. The simplicity of the algorithm and its demonstrably improved performance make the approach of this paper an excellent candidate for adaptively controlling tunable parameters of compliant, legged robots.

Biomechanics, motor control and dynamic models of the soft limbs of the octopus and other cephalopods.

Muscular hydrostats are organs composed entirely of packed arrays of incompressible muscles and lacking any skeletal support. Found in both vertebrates and invertebrates, they are of great interest for comparative biomechanics from engineering and evolutionary perspectives. The arms of cephalopods (e.g. octopus and squid) are particularly interesting muscular hydrostats because of their flexibility and ability to generate complex behaviors exploiting elaborate nervous systems. Several lines of evidence from octopus studies point to the use of both brain and arm-embedded motor control strategies that have evolved to simplify the complexities associated with the control of flexible and hyper-redundant limbs and bodies. Here, we review earlier and more recent experimental studies on octopus arm biomechanics and neural motor control. We review several dynamic models used to predict the kinematic characteristics of several basic motion primitives, noting the shortcomings of the current models in accounting for behavioral observations. We also discuss the significance of impedance (stiffness and viscosity) in controlling the octopus's motor behavior. These factors are considered in light of several new models of muscle biomechanics that could be used in future research to gain a better understanding of motor control in the octopus. There is also a need for updated models that encompass stiffness and viscosity for designing and controlling soft robotic arms. The field of soft robotics has boomed over the past 15 years and would benefit significantly from further progress in biomechanical and motor control studies on octopus and other muscular hydrostats.

Bio-Inspired Micro- and Nanorobotics Driven by Magnetic Field.

In recent years, there has been explosive growth in the number of investigations devoted to the development and study of biomimetic micro- and nanorobots. The present review is dedicated to novel bioinspired magnetic micro- and nanodevices that can be remotely controlled by an external magnetic field. This approach to actuate micro- and nanorobots is non-invasive and absolutely harmless for living organisms in vivo and cell microsurgery, and is very promising for medicine in the near future. Particular attention has been paid to the latest advances in the rapidly developing field of designing polymer-based flexible and rigid magnetic composites and fabricating structures inspired by living micro-objects and organisms. The physical principles underlying the functioning of hybrid bio-inspired magnetic miniature robots, sensors, and actuators are considered in this review, and key practical applications and challenges are analyzed as well.

Optimizing and designing a leg shape to increase robustness of a running robot on rough terrain.

The superior ability of dynamic legged locomotion in traversing rough terrain relative to wheeled or tracked mechanisms comes with the cost of fragile stability. Simple control methods that use only a few basic detection sensors and apply a single controller help robots keep their balance when traversing unforeseen rough terrain. Exploiting multiple controllers simultaneously, such as the free leg length and stiffness in our hopping monopod, can further improve robustness but is often mechanically hard to implement. This work demonstrates that a curved leg shape can improve the robustness of a robot to perturbations in both terrain levels and initial horizontal velocity without complicating the control scheme. Our work develops spring loaded inverted pendulum (SLIP) based models that manifest the coupling of the leg's parameters and capture the rolling motion. We use these models to find an optimal combination of parameters that maximizes a measure for long-term stability-reaching a desired relative height above terrain. We demonstrate that when traversing unknown rough terrain, such optimal coupling can increase robustness to perturbations in the initial horizontal velocity by 93% relative to the optimal conventional SLIP model. We further demonstrate our results in experiments.

A Scientometric Review of Soft Robotics: Intellectual Structures and Emerging Trends Analysis (2010-2021).

Within the last decade, soft robotics has attracted an increasing attention from both academia and industry. Although multiple literature reviews of the whole soft robotics field have been conducted, there still appears to be a lack of systematic investigation of the intellectual structure and evolution of this field considering the increasing amount of publications. This paper conducts a scientometric review of the progressively synthesized network derived from 10,504 bibliographic records using a topic search on soft robotics from 2010 to 2021 based on the Web of Science (WoS) core database. The results are presented from both the general data analysis of included papers (e.g., relevant journals, citation, h-index, year, institution, country, disciplines) and the specific data analysis corresponding to main disciplines and topics, and more importantly, emerging trends. CiteSpace, a data visualization software, which can construct the co-citation network maps and provide citation bursts, is used to explore the intellectual structures and emerging trends of the soft robotics field. In addition, this paper offers a demonstration of an effective analytical method for evaluating enormous publication citation and co-citation data. Findings of this review can be used as a reference for future research in soft robotics and relevant topics.

A gecko-inspired robot with CPG-based neural control for locomotion and body height adaptation.

Today's gecko-inspired robots have shown the ability of omnidirectional climbing on slopes with a low centre of mass. However, such an ability cannot efficiently cope with bumpy terrains or terrains with obstacles. In this study, we developed a gecko-inspired robot (Nyxbot) with an adaptable body height to overcome this limitation. Based on an analysis of the skeletal system and kinematics of real geckos, the adhesive mechanism and leg structure design of the robot were designed to endow it with adhesion and adjustable body height capabilities. Neural control with exteroceptive sensory feedback is utilised to realise body height adaptability while climbing on a slope. The locomotion performance and body adaptability of the robot were tested by conducting slope climbing and obstacle crossing experiments. The gecko robot can climb a 30° slope with spontaneous obstacle crossing (maximum obstacle height of 38% of the body height) and can climb even steeper slopes (up to 60°) without an obstacle or bump. Using 3D force measuring platforms for ground reaction force analysis of geckos and the robot, we show that the motions of the developed robot driven by neural control and the motions of geckos are dynamically comparable. To this end, this study provides a basis for developing climbing robots with adaptive bump/obstacle crossing on slopes towards more agile and versatile gecko-like locomotion.

Unified Approach to the Motion Design for a Snake Robot Negotiating Complicated Pipe Structures.

A unified method for designing the motion of a snake robot negotiating complicated pipe structures is presented. Such robots moving inside pipes must deal with various "obstacles," such as junctions, bends, diameter changes, shears, and blockages. To surmount these obstacles, we propose a method that enables the robot to adapt to multiple pipe structures in a unified way. This method also applies to motion that is necessary to pass between the inside and the outside of a pipe. We designed the target form of the snake robot using two helices connected by an arbitrary shape. This method can be applied to various obstacles by designing a part of the target form specifically for given obstacles. The robot negotiates obstacles under shift control by employing a rolling motion. Considering the slip between the robot and the pipe, the model expands the method to cover cases where two helices have different properties. We demonstrated the effectiveness of the proposed method in various experiments.

4D Printing of Magnetoactive Soft Materials for On-Demand Magnetic Actuation Transformation.

Four-dimensional (4D) printed magnetoactive soft material (MASM) with a three-dimensional (3D) patterned magnetization profile possesses programmable shape transformation and controllable locomotion ability, showing promising applications in actuators and soft robotics. However, typical 4D printing strategies for MASM always introduced a printing magnetic field to orient the magneto-sensitive particles in polymers. Such strategies not only increase the cooperative control complexity of a 3D printer but may also induce local agglomeration of magneto-sensitive particles, which disturbs the magnetization of the already-printed structure. Herein, we proposed a novel 4D printing strategy that coupled the traditional 3D injection printing with the origami-based magnetization technique for easy fabrication of MASM objects with a 3D patterned magnetization profile. The 3D injection printing that can rapidly create complex 3D structures and the origami-based magnetization technique that can generate the spatial magnetization profile are combined for fabrication of 3D MASM objects to yield programmable transformation and controllable locomotion. A physics-based finite element model was also developed for the design guidance of origami-based magnetization and magnetic actuation transformation of MASM. We further demonstrated the diverse functions derived from the complex shape deformation of MASM-based robots, including a bionic human hand that played "rock-paper-scissors" game, a bionic butterfly that swung the wings on the flower, and a bionic turtle that crawled on the land and swam in the water.

Design of a novel wheeled tensegrity robot: a comparison of tensegrity concepts and a prototype for travelling air ducts.

Efforts in the research of tensegrity structures applied to mobile robots have recently been focused on a purely tensegrity solution to all design requirements. Locomotion systems based on tensegrity structures are currently slow and complex to control. Although wheeled locomotion provides better efficiency over distances there is no literature available on the value of wheeled methods with respect to tensegrity designs, nor on how to transition from a tensegrity structure to a fixed structure in mobile robotics. This paper is the first part of a larger study that aims to combine the flexibility, light weight, and strength of a tensegrity structure with the efficiency and simple control of a wheeled locomotion system. It focuses on comparing different types of tensegrity structure for applicability to a mobile robot, and experimentally finding an appropriate transitional region from a tensegrity structure to a conventional fixed structure on mobile robots. It applies this transitional structure to what is, to the authors' knowledge, the design of the world's first wheeled tensegrity robot that has been designed with the goal of traversing air ducts.

Cognitive Mapping Based on Conjunctive Representations of Space and Movement.

It is a challenge to build robust simultaneous localization and mapping (SLAM) system in dynamical large-scale environments. Inspired by recent findings in the entorhinal-hippocampal neuronal circuits, we propose a cognitive mapping model that includes continuous attractor networks of head-direction cells and conjunctive grid cells to integrate velocity information by conjunctive encodings of space and movement. Visual inputs from the local view cells in the model provide feedback cues to correct drifting errors of the attractors caused by the noisy velocity inputs. We demonstrate the mapping performance of the proposed cognitive mapping model on an open-source dataset of 66 km car journey in a 3 km × 1.6 km urban area. Experimental results show that the proposed model is robust in building a coherent semi-metric topological map of the entire urban area using a monocular camera, even though the image inputs contain various changes caused by different light conditions and terrains. The results in this study could inspire both neuroscience and robotic research to better understand the neural computational mechanisms of spatial cognition and to build robust robotic navigation systems in large-scale environments.