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A major intent of scientific research is the replication of the behaviour observed in natural spaces. In robotics, these can be through biomimetic movements in devices and inspiration from diverse actions in nature, also known as bioinspired features. An interesting pathway enabling both features is the fabrication of soft actuators. Specifically, 3D‐printing has been explored as a potential approach for the development of biomimetic and bioinspired soft actuators. The extent of this method is highlighted through the large array of applications and techniques used to create these devices, as applications from the movement of fern trees to contraction in organs are explored. In this review, different 3D‐printing fabrication methods, materials, and types of soft actuators, and their respective applications are discussed in depth. Finally, the extent of their use for present operations and future technological advances are discussed.

Gecko‐inspired robots outperform other climbing robots with their high‐efficiency dry‐adhesion mechanism and superior terrain adaptability. However, a lack of precise adhesive‐force sensing and control compromises their locomotion stability under severe disturbances such as slipping. This article presents a neural adhesion controller that enables a gecko‐inspired robot to adapt effectively to slip disturbances. To overcome the challenges associated with sensing and predicting highly nonlinear ground adhesive forces, the controller integrates an echo state network (ESN) with a multilayer perceptron (MLP). It accurately estimates real‐time adhesion in the normal and shear directions via proprioceptive joint torque feedback, achieving a low prediction error (mean squared error (MSE) = 0.06 across limbs). Additionally, the system can forecast incipient slip over consecutive strides by leveraging learned temporal patterns. The proposed controller successfully recovers the climbing robot from destabilizing slip events on a low‐adhesion surface, representing a significant advantage toward robust and reliable real‐world locomotion. This study introduces a neural adhesion controller to improve the stability of gecko‐inspired climbing robots. By integrating an echo state network and a multilayer perceptron, the system utilizes joint torque feedback to accurately estimate adhesion in both normal and shear directions and predict slips. This enables effective recovery from slip events, ensuring robust locomotion on challenging, low‐adhesion terrains.

There is great potential for legged robots in unstructured environments. However, model‐based approaches benefit from precise model analysis, which can be cumbersome and demand substantial domain expertise, while learning‐based methods, though promising, often necessitate prolonged training periods and may result in complex and opaque controllers. This architecture aims to mimic neural‐muscle control and sensory feedback mechanisms, enabling legged robots to adjust neural signal intensity based on proprioceptive feedback and achieve behavior responses similar to those observed in animals. Specifically, and the central pattern generator creates insect‐like gaits, the virtual motoneurons network generates continuously adjustable trajectories for omnidirectional motion and limb control. The sensorimotor integration module, event‐based finite state machine, and local reactive strategies allow robots to traverse unstructured terrains. The method is experimentally applied to a newly developed hexapod robot named RENS H2. The results indicate that the proposed method enhances the robot's locomotion diversity, enabling adaptive navigation in unstructured terrains, including overcoming steps with heights up to 66.7% of its leg length. A novel hexapod robot named RENS H2 based on the synthesized neural control system. The control system is constructed based on the central pattern generator and virtual motoneurons network, serving as the foundation of the behavioral network. It encompasses omnidirectional movement, regulation of motor neuron intensity, sensorimotor integration, local reactive strategies, and the event‐based finite state machine, endowing the robot with a variety of locomotion capabilities.

In nature, various animal groups like bird flocks display proficient collective navigation achieved by maintaining high consistency and cohesion simultaneously. Both metric and topological interactions have been explored to ensure high consistency among groups. The topological interactions found in bird flocks are more cohesive than metric interactions against external perturbations, especially the spatially balanced topological interaction (SBTI). However, it is revealed that in complex environments, pursuing cohesion via existing interactions compromises consistency. The authors introduce an innovative solution, assemble topological interaction, to address this challenge. Contrasting with static interaction rules, the new interaction empowers individuals with self‐awareness to adapt to the complex environment by switching between interactions through visual cues. Most individuals employ high‐consistency k‐nearest topological interaction when not facing splitting threats. In the presence of such threats, some switch to the high‐cohesion SBTI to avert splitting. The assemble topological interaction thus transcends the limit of the trade‐off between consistency and cohesion. In addition, by comparing groups with varying degrees of these two features, the authors demonstrate that group effects are vital for efficient navigation led by a minority of informed agents. Finally, the real‐world drone‐swarm experiments validate the applicability of the proposed interaction to artificial robotic collectives.

To ensure that the robot can follow the planned trajectory, smooth switching between swinging legs and a smooth transition of motion process is realised. The previous motion planning work is analysed, and a method for improving the optimisation objective function and constraint conditions is proposed to eliminate the sudden change of acceleration and reduce the peak value of acceleration change. This method eliminates the impact phenomenon in the motor drive process and reduces the motor drive energy consumption, thus ensuring the smooth and consistent movement of the robot. The results show that the improved optimisation method has a better motion effect than the previous approach in terms of centre of mass motion speed, trajectory fitting and body posture change, and realises more robust motion of quadruped robots in a senseless state.

When a person's neuromuscular system is affected by an injury or disease, Activities‐for‐Daily‐Living (ADL), such as gripping, turning, and walking, are impaired. Electroencephalography (EEG) and Electromyography (EMG) are physiological signals generated by a body during neuromuscular activities embedding the intentions of the subject, and they are used in Brain–Computer Interface (BCI) or robotic rehabilitation systems. However, existing BCI or robotic rehabilitation systems use signal classification technique limitations such as (1) missing temporal correlation of the EEG and EMG signals in the entire window and (2) overlooking the interrelationship between different sensors in the system. Furthermore, typical existing systems are designed to operate based on the presence of dominant physiological signals associated with certain actions; (3) their effectiveness will be greatly reduced if subjects are disabled in generating the dominant signals. A novel classification model, named BIOFIS is proposed, which fuses signals from different sensors to generate inter‐channel and intra‐channel relationships. It explores the temporal correlation of the signals within a timeframe via a Long Short‐Term Memory (LSTM) block. The proposed architecture is able to classify the various subsets of a full‐range arm movement that performs actions such as forward, grip and raise, lower and release, and reverse. The system can achieve 98.6% accuracy for a 4‐way action using EEG data and 97.18% accuracy using EMG data. Moreover, even without the dominant signal, the accuracy scores were 90.1% for the EEG data and 85.2% for the EMG data. The proposed mechanism shows promise in the design of EEG/EMG‐based use in the medical device and rehabilitation industries.

Roboticists have long drawn inspiration from nature to develop navigation and simultaneous localization and mapping (SLAM) systems such as RatSLAM. Animals such as birds and bats possess superlative navigation capabilities, robustly navigating over large, three-dimensional environments, leveraging an internal neural representation of space combined with external sensory cues and self-motion cues. This paper presents a novel neuro-inspired 4DoF (degrees of freedom) SLAM system named NeuroSLAM, based upon computational models of 3D grid cells and multilayered head direction cells, integrated with a vision system that provides external visual cues and self-motion cues. NeuroSLAM’s neural network activity drives the creation of a multilayered graphical experience map in a real time, enabling relocalization and loop closure through sequences of familiar local visual cues. A multilayered experience map relaxation algorithm is used to correct cumulative errors in path integration after loop closure. Using both synthetic and real-world datasets comprising complex, multilayered indoor and outdoor environments, we demonstrate NeuroSLAM consistently producing topologically correct three-dimensional maps.

This study explores the potential of insect-derived sensory systems, bio-inspired robotics, and bio-inspired materials in advancing modern technology. Insects possess highly specialized sensory mechanisms and locomotion strategies that have evolved over millions of years, offering novel insights for the development of bio-electronic sensors, adaptive robots, and innovative materials. The study employs a combination of electrophysiological recordings, computational modeling, and bio-engineering techniques, including microfabrication of bio-sensors, neural interfacing for insect-controlled robotics, and genetic modifications for analyzing insect sensory mechanisms to investigate the sensory capabilities of insects such as honeybees, fruit flies, and cockroaches. The findings reveal that insect olfactory receptors, mechanosensory systems, and locomotion patterns can be effectively mimicked in bio-hybrid robots and bio-inspired materials. These bio-inspired systems demonstrated enhanced chemical detection capabilities, multi-terrain adaptability, and efficient material properties. Furthermore, insect-derived nanostructures were successfully replicated to create superhydrophobic surfaces and energy-efficient photonic materials. This study underscores the vast potential of biomimicry in addressing intricate engineering challenges, including the development of sustainable materials, adaptive robotics, and advanced sensing technologies However, scalability and ethical considerations remain key challenges for the widespread adoption of insect-inspired systems. The integration of AI, computational modeling, and bio-engineering presents a promising pathway toward next-generation technologies. Article highlightsInsect-inspired sensors enhance chemical detection and environmental monitoring for bio-sensing applications.Bio-inspired robotics replicate insect locomotion for efficient and adaptable robotic movement strategies.Biomimetic materials mimic insect structures for self-cleaning, photonic, and superhydrophobic surfaces.

This study presents a finite element analysis (FEA)-driven design and preliminary experimental validation of a bio-inspired amphibious spider robot’s flapping fin mechanism for hybrid terrestrial–aquatic locomotion. The robot incorporates a six-legged walking system and a passive deployable fin-based swimming mechanism actuated via leg-tip hooks with spring-loaded retraction, enabling automatic transition between land and water operation when triggered by a water contact sensor. Structural performance of the fin under combined hydrostatic and dynamic pressures was evaluated in ANSYS, with dynamic loads derived from fin tip velocity corresponding to a baseline flapping frequency of 1 Hz. Candidate materials, including Nylon (PA12), PETG, TPU (98 A), and 304 L stainless steel foil, were compared through stress–strain–deformation analysis. A multi-criteria decision analysis identified 304 L stainless steel foil as the optimal choice for minimal deformation (0.64 mm) and high fatigue resistance. A functional prototype was fabricated using FDM-based 3D printing, integrating macro and micro servo motors for locomotion and fin deployment. Equipped with TPU fins (0.15 mm thickness) for initial trials, the 1.311 kg prototype achieved a measured flapping speed of 53.4 RPM (0.89 Hz) using a non-contact tachometer, closely matching simulation assumptions. The results confirm the feasibility of the proposed design, validate its actuation performance, and provide a foundation for future in-water propulsion measurements and fluid–structure interaction studies.

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.