Robot Locomotion Research Articles

Relaxation and rheology in beam-vibrated granular system

The yielding transition in dense granular matter under vibrated beams, despite its significance for animal and robotic locomotion on granular surfaces and underground structural engineering, remains underexplored. In this study, we systematically modulate the frequency and amplitude of beam vibrations through experiments and simulations to investigate the granular relaxation dynamics. We uncover dual yielding behaviors: gradual, ductile transitions in the time domain, where the system smoothly stabilizes, and abrupt, brittle transitions in the frequency domain, characterized by sharp shifts between metastable states and pronounced hysteresis, highlighting the dynamic consistency between the behavior of the beam and the granular materials. Through detailed analysis of the mesostructural evolution, encompassing particle motion, and mechanical stability, we unveil the root of the hysteresis as stemming from anomalous diffusion driven by memory effects, where the system's response is influenced by its stress history. These findings lead to the development of a nonmonotonic constitutive law that captures the unique frequency-dependent coupling between the beam and granular material. Our findings pave the way for advanced theoretical models in this domain, offering profound insights into the nuanced behaviors of vibrated granular systems.

Control and locomotion of tensegrity robots through manipulation of the center of mass

Abstract This paper provides the methodology used to simulate and control an icosahedral tensegrity structure augmented with movable masses attached to each bar to provide a means of locomotion. The center of mass of the system can be changed by moving the masses along the length of each of the bars that compose the structure. Moving the masses changes the moments created by gravitational force, allowing for the structure to roll. With this methodology in mind, a controller was created to move the masses to the desired locations to cause such a roll. As shown later in this paper, such a methodology, assuming the movable masses have the required mass, allows for full control of the system using a quasi-static controller created specifically for this system. This system has advantages over traditional tensegrity controllers because it retains its shape and is designed for high-shock scenarios.

Self‐Sensing Artificial‐Muscle‐Empowered Humanlike Perception, Interaction, and Positioning

Bioinspired soft and hybrid robots provide a promising solution to developing robots that can interact and collaborate with humans safely and effectively. Bellow‐like pneumatic artificial muscles can produce biological muscle‐like contraction with comparable response time and force output, but closed‐loop control has been a challenge without an effective length sensing solution. Herein, a self‐sensing artificial muscle (SSAM), which can sense its own length regardless of the external loadings and driving pressures, is proposed. Empowered by a seamlessly integrated mutual‐inductance‐based length sensor (MILS), the SSAM can sense its length change as small as 0.01 mm (0.012% of the initial liength 80 mm) in a wide range. The working principle of the MILS is analyzed theoretically to provide a design guideline. An SSAM‐based earthworm‐like locomotion robot is developed with the capability of knowing its real‐time body length change at different gaits. An artificial arm driven by one SSAM is also developed and demonstrated with humanlike capabilities of loading perception, interactive and responsive movements, and accurate positioning. This work provides a promising solution to develop muscle‐driven hybrid robotic systems with embodied intelligence for skilled manipulation and sophisticated human–machine interactions.

Analysis of Robot–Environment Interaction Modes in Anguilliform Locomotion of a New Soft Eel Robot

Bio-inspired robots with elongated anatomy, like eels, are studied to discover anguilliform swimming principles and improve the robots’ locomotion accordingly. Soft continuum robots replicate animal–environment physics better than noncompliant, rigid, multi-body eel robots. In this study, a slender soft robot was designed and tested in an actual swimming experiment in a still-water tank. The robot employs soft pneumatic muscles laterally connected to a flexible backbone and activated with a rhythmic input. The position of seven markers mounted on the robot’s backbone was recorded using QualiSys® Tracking Manager (QTM) 1.6.0.1. The system was modeled as a fully coupled fluid–solid interaction (FSI) system using COMSOL Multiphysics® 6.1. Further data postprocessing and analysis were conducted, proposing a new mode decomposition algorithm using simulation data. Experiments show the success of swimming with a velocity of 28 mm/s and at a frequency of 0.9 Hz. The mode analysis allowed the modeling and explanation of the fluctuation. Results disclose the presence of traveling waves related to anguilliform waves obtained by the superposition of two main modes. The similarities of the results with natural anguilliform locomotion are discussed. It is concluded that soft robot undulation is ruled by dynamic modes induced by robot–environment interaction.

Reconfigurable origami with variable stiffness joints for adaptive robotic locomotion and grasping.

With its compactness and foldability, origami has recently been applied to robotic systems to enable versatile robots and mechanisms while maintaining a low weight and compact form. This work investigates how to generate different motions and shapes for origami by tuning its creases' stiffness on the fly. The stiffness tuning is realized by a composite material made by sandwiching a thermoplastic layer between two shape memory polymer layers. This enables the composite to act as a living hinge, whose stiffness can be actively controlled through Joule heating. To demonstrate our concept, we fabricate an origami module with four variable stiffness joints (VSJs), allowing it to have freely controlled crease stiffnesses across its surface. We characterize the origami module's versatile motion when heating different VSJs with different temperatures. We further use two origami modules to build a two-legged robot that can locomote on the ground with different locomotion gaits. The same robot is also used as an adaptive gripper for grasping tasks. Our work can potentially enable more versatile robotic systems made from origami as well as other mechanical systems with programmable properties (e.g. mechanical metamaterials).This article is part of the theme issue 'Origami/Kirigami-inspired structures: from fundamentals to applications'.

Mechanochemically responsive polymer enables shockwave visualization

Understanding the physical and chemical response of materials to impulsive deformation is crucial for applications ranging from soft robotic locomotion to space exploration to seismology. However, investigating material properties at extreme strain rates remains challenging due to temporal and spatial resolution limitations. Combining high-strain-rate testing with mechanochemistry encodes the molecular-level deformation within the material itself, thus enabling the direct quantification of the material response. Here, we demonstrate a mechanophore-functionalized block copolymer that self-reports energy dissipation mechanisms, such as bond rupture and acoustic wave dissipation, in response to high-strain-rate impacts. A microprojectile accelerated towards the polymer permanently deforms the material at a shallow depth. At intersonic velocities, the polymer reports significant subsurface energy absorption due to shockwave attenuation, a mechanism traditionally considered negligible compared to plasticity and not well explored in polymers. The acoustic wave velocity of the material is directly recovered from the mechanochemically-activated subsurface volume recorded in the material, which is validated by simulations, theory, and acoustic measurements. This integration of mechanochemistry with microballistic testing enables characterization of high-strain-rate mechanical properties and elucidates important insights applicable to nanomaterials, particle-reinforced composites, and biocompatible polymers.

Leveraging large language models for comprehensive locomotion control in humanoid robots design

This paper investigates the utilization of large language models (LLMs) for the comprehensive control of humanoid robot locomotion. Traditional reinforcement learning (RL) approaches for robot locomotion are resource-intensive and rely heavily on manually designed reward functions. To address these challenges, we propose a method that employs LLMs as the primary designer to handle key aspects of locomotion control, such as trajectory planning, inverse kinematics solving, and reward function design. By using user-provided prompts, LLMs generate and optimize code, reducing the need for manual intervention. Our approach was validated through simulations in Unity, demonstrating that LLMs can achieve human-level performance in humanoid robot control. The results indicate that LLMs can simplify and enhance the development of advanced locomotion control systems for humanoid robots.

Modeling and motion analysis of flexible legged robots using the finite particle method

Robotics with flexible legs have attracted significant attention. Engineers often design and analyze the motion of legged robots from kinematic and biomimetic perspective. However, the influence of flexibility of the feet on robot locomotion is often not given sufficient considerations, which is also very crucial to the motion posture of the flexible legged robots, especially as the soft robots design becomes increasingly popular. The mainly difficulties lies in the traditional numerical methods in handling the dynamic motion analysis with both large rigid motion and large deformation. In this paper, the finite particle method (FPM) is used to simulate the motion and deformation coupled problems of the flexible six-legged robot. A shell-based particle model of a six-leg robot and the contact model between legs and ground are built. Without iterative and modification of the FPM analytical framework, structural nonlinearity is efficiently handled after eliminating rigid body motions by a fictitious reverse motion. The motion and deformation of a single leg with varying leg thickness, locomotion speed, and leg-to-ground friction coefficients were simulated. By analyzing the stress distribution in the leg and the number of contact points with the ground, the mechanical leg was optimized in design. Furthermore, the motion and deformation of the entire six-legged robot were simulated using the FPM. The numerical results' feasibility was validated through comparison with experimental data obtained from robot walking tests. The proposed method effectively simulates the motion and deformation of flexible robots, providing significant insights for the design of soft robots.

Dual-Layer Reinforcement Learning for Quadruped Robot Locomotion and Speed Control in Complex Environments

Dual-Layer Reinforcement Learning for Quadruped Robot Locomotion and Speed Control in Complex Environments

Electrohydraulic musculoskeletal robotic leg for agile, adaptive, yet energy-efficient locomotion

Robotic locomotion in unstructured terrain demands an agile, adaptive, and energy-efficient architecture. To traverse such terrains, legged robots use rigid electromagnetic motors and sensorized drivetrains to adapt to the environment actively. These systems struggle to compete with animals that excel through their agile and effortless motion in natural environments. We propose a bio-inspired musculoskeletal leg architecture driven by antagonistic pairs of electrohydraulic artificial muscles. Our leg is mounted on a boom arm and can adaptively hop on varying terrain in an energy-efficient yet agile manner. It can also detect obstacles through capacitive self-sensing. The leg performs powerful and agile gait motions beyond 5 Hz and high jumps up to 40 % of the leg height. Our leg’s tunable stiffness and inherent adaptability allow it to hop over grass, sand, gravel, pebbles, and large rocks using only open-loop force control. The electrohydraulic leg features a low cost of transport (0.73), and while squatting, it consumes only a fraction of the energy (1.2 %) compared to its conventional electromagnetic counterpart. Its agile, adaptive, and energy-efficient properties would open a roadmap toward a new class of musculoskeletal robots for versatile locomotion and operation in unstructured natural environments.

Modeling and rolling gaits of a strut-actuated 6-strut locomotive tensegrity robot with membranes

Modeling and rolling gaits of a strut-actuated 6-strut locomotive tensegrity robot with membranes

Embedded ML-based locomotion control for a 12-joint four-legged robot

Reinforcement learning has developed as a promising approach for robot locomotion control, which can save engineering effort compared to conventional approaches. This article presents the implementation of Reinforcement learning on a low-cost, 12 degree of freedom robot known as a quadruped (spider) to optimize locomotion control, enabling the robot to move on different surfaces such as flat surfaces, ramps, speed bumps, and rough terrain. A MATLAB Simulink model is developed as a digital twin of the spider robot. The dynamics of the model were studied and validated using an open-loop algorithm. Then, the model is utilized in a training simulation environment to apply the reinforcement learning algorithm, showing its ability to move along a predefined path as a replacement for conventional motion control systems. Moreover, the work compares the performance and effectiveness of machine learning-based locomotion control with traditional motion control systems regarding navigation accuracy, speed, and adaptability in challenging environments. The minimum hardware requirements are also studied to move the experiment from simulation to reality.

Brain-inspired biomimetic robot control: a review.

Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven methods such as artificial neural networks excel at representing high-dimensional data but lack robustness, generalization, and real-time adaptiveness. In response to these challenges, researchers are directing their focus to biological paradigms, drawing inspiration from the remarkable control capabilities inherent in the human body. This has motivated the exploration of new control methods aimed at closely emulating the motor functions of the brain given the current insights in neuroscience. Recent investigation into these Brain-Inspired control techniques have yielded promising results, notably in tasks involving trajectory tracking and robot locomotion. This paper presents a comprehensive review of the foremost trends in biomimetic brain-inspired control methods to tackle the intricacies associated with controlling complex robotic systems.

Apple-Harvesting Robot Based on the YOLOv5-RACF Model.

To address the issue of automated apple harvesting in orchards, we propose a YOLOv5-RACF algorithm for identifying apples and calculating apple diameters. This algorithm employs the robot operating dystem (ROS) to control the robot's locomotion system, Lidar mapping, and navigation, as well as the robotic arm's posture and grasping operations, achieving automated apple harvesting and placement. The tests were conducted in an actual orchard environment. The algorithm model achieved an average apple detection accuracy (mAP@0.5) of 98.748% and a (mAP@0.5:0.95) of 90.02%. The time to calculate the diameter of one apple was 0.13 s, with a measurement accuracy within an error range of 1-3 mm. The robot takes an average of 9 s to pick an apple and return to the initial pose. These results demonstrate the system's efficiency and reliability in real agricultural environments.

Variable-stiffness-morphing wheel inspired by the surface tension of a liquid droplet.

Wheels have been commonly used for locomotion in mobile robots and transportation systems because of their simple structure and energy efficiency. However, the performance of wheels in overcoming obstacles is limited compared with their advantages in driving on normal flat ground. Here, we present a variable-stiffness wheel inspired by the surface tension of a liquid droplet. In a liquid droplet, as the cohesive force of the outermost liquid molecules increases, the net force pulling the liquid molecules inward also increases. This leads to high surface tension, resulting in the liquid droplet reverting to a circular shape from its distorted shape induced by gravitational forces. Similarly, the shape and stiffness of a wheel were controlled by changing the traction force at the outermost smart chain block. As the tension of the wire spokes connected to each chain block increased, the wheel characteristics reflected those of a general circular-rigid wheel, which has an advantage in high-speed locomotion on normal flat ground. Conversely, the modulus of the wheel decreased as the tension of the wire spoke decreased, and the wheel was easily deformed according to the shape of obstacles. This makes the wheel suitable for overcoming obstacles without requiring complex control or sensing systems. On the basis of this mechanism, a wheel was applied to a two-wheeled wheelchair system weighing 120 kilograms, and the state transition between a circular high-modulus state and a deformable low-modulus state was realized in real time when the wheelchair was driven in an outdoor environment.

A Multimode Two-Wheel-Legged Land-Air Locomotion Robot and Its Cooperative Control

A Multimode Two-Wheel-Legged Land-Air Locomotion Robot and Its Cooperative Control

3D printing of reprogrammable liquid crystal elastomers with exchangeable boronic ester bonds

Liquid crystal elastomers (LCEs) are a kind of soft actuating materials with large reversible deformation ability, which can work as the “motor” to exhibit complex deformations and drive the locomotion of soft robots. The deformation of LCEs depends on the three-dimensional (3D) shape of whole structure and alignment patterns of mesogens. Various methods have been employed to fabricate the LCE structure with desired shapes and mesogen alignments. However, conventional 3D printed LCEs require continuous thermal energy input to maintain their actuated shapes. The LCEs cannot be reprocessed and reprogrammed once cured. Herein, we introduce dynamic boronic ester bonds into the ink, with which the printed LCE structures are capable of being reprogrammed from polydomain into monodomain state and vice versa. We further explore the effects of printing parameters and content of dynamic covalent bonds on the actuation performance and reprogramming ability. The actuated shape could be well predicted with finite element method. The dynamic printable LCEs developed here offer new strategy and large design space for LCE structures.

Deep neural data-driven Koopman fractional control of a worm robot

This paper proposes an innovative approach for enhancing the locomotion control of a worm robot by integrating deep neural networks with Koopman operator theory and fractional order control techniques. Traditional control methods often struggle with the nonlinear dynamics and uncertainties inherent in such systems. The Koopman operator framework facilitates the representation of these dynamics in a linear and infinite-dimensional space, thereby enabling advanced control strategies. By leveraging deep neural networks, we approximate the Koopman operator efficiently, enhancing the system’s adaptability and performance. Additionally, fractional order control is introduced to provide robustness against uncertainties and disturbances, offering greater flexibility in capturing the complex dynamics of the worm robot. Extensive simulations validations demonstrate the effectiveness and efficiency of the proposed approach across various environmental conditions. Results show superior performance compared to traditional methods, affirming the viability of integrating deep neural networks with fractional order control for advanced robotic applications. This study contributes to the field by showcasing a novel methodology that not only achieves precise and robust control of worm robot locomotion but also sets a foundation for future research in adaptive and intelligent robotic systems.

Computational synthesis of locomotive soft robots by topology optimization.

Locomotive soft robots (SoRos) have gained prominence due to their adaptability. Traditional locomotive SoRo design is based on limb structures inspired by biological organisms and requires human intervention. Evolutionary robotics, designed using evolutionary algorithms (EAs), have shown potential for automatic design. However, EA-based methods face the challenge of high computational cost when considering multiphysics in locomotion, including materials, actuations, and interactions with environments. Here, we present a design approach for pneumatic SoRos that integrates gradient-based topology optimization with multiphysics material point method (MPM) simulations. This approach starts with a simple initial shape (a cube with a central cavity). The topology optimization with MPM then automatically and iteratively designs the SoRo shape. We design two SoRos, one for walking and one for climbing. These SoRos are 3D printed and exhibit the same locomotion features as in the simulations. This study presents an efficient strategy for designing SoRos, demonstrating that a purely mathematical process can produce limb-like structures seen in biological organisms.

A review of locomotion of snake robots

In this review paper, we demonstrate the high structural flexibility and motion diversity of snake robots, which are important indicators to measure the performance of the mechanism. And animals in nature, especially reptiles, often have a high degree of flexibility and a variety of modes of movement in order to be able to shift through various terrains. Thus, making a robot with excellent performance, a bionic robot is a suitable choice. Currently, we have reviewed the state of the act in the snake-like machine. In the end, we conclude that with the simplicity of design and practice, it can be designed easily with unique constructures to complete modes of locomotion. Furthermore, we also provide the perspectives of future work.