Robot Locomotion Research Articles

Adaptive control algorithm for quadruped robots in unknown high-slope terrain

The capability to navigate high-slope terrains is crucial for quadruped robots engaged in field operations. We propose a novel terrain estimation and adaptation strategy tailored for facilitating quadruped robot locomotion on complex slope terrains. Our approach involves predicting terrain slopes by analyzing foot positions and IMU data, subsequently adjusting the robot’s body orientation and height in real-time to accommodate varying slope conditions. To effectively control the motion of quadruped robots on such challenging terrains, we introduce a multimodal motion control algorithm that integrates Model Predictive Control (MPC) with Quadratic Programming (QP) torque control. This combined approach ensures stable and efficient locomotion even on steep slopes. Furthermore, we present a state estimation method based on Kalman filtering, enabling accurate self-assessment by the robot without heavy reliance on visual sensors. In simulation validation, our quadruped robot achieved notable performance metrics. It achieved a forward speed of 0.7 m/s on slopes with angles up to 43∘ and demonstrated stable rotational capability at a speed of 2 rad/s on a 32∘ slope. When subjected to external force interference, the robot exhibited resilience, withstanding constant external forces of up to 60Nm and external torques of up to 35Nm on flat ground. Moreover, on a 30∘ slope, the robot maintained stable locomotion in the face of impulses reaching 64Nm ⋅ s along the x and y directions of its body coordinate system and 7Nm ⋅ s along the z-axis. This comprehensive strategy and experimental validation highlight the efficacy and robustness of our adaptive control algorithm, paving the way for enhanced performance of quadruped robots navigating high-slope terrains with unpredictable characteristics.

A multi-directional magnetic-driven multifunctional soft crawling robot

Multifunctional and multi-freedom integrated magnetic-driven soft robots have received widespread attention in the past decade. However, the current magnetic-driven soft crawling robots with multiple functional modules focus on the development of new materials or innovation of driving component. Herein, we propose a soft crawling robot that materializes multi-directional motion driven only through a unidirectional magnetic field (MF) while possessing the ability to communication, loading, and positioning. The locomotion of robot relies on ingenious leg design to generate differences in friction between each leg and the ground by controlling the MF intensity. The integrated wireless communication modules on the robot's legs serve as a medium for interacting with the external world, enabling the robot to effectively perceive the environment via electromagnetic coupling. In addition, the magnetic-driven soft crawling robot carries loads (3.37 g) larger than twice its own weight for motion. Overall, this work showcases a new paradigm for robot applications, providing a practicable idea for developing miniaturization, multi-directional soft crawling robots.

Learning robust autonomous navigation and locomotion for wheeled-legged robots.

Autonomous wheeled-legged robots have the potential to transform logistics systems, improving operational efficiency and adaptability in urban environments. Navigating urban environments, however, poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. These challenges include the need for adaptive locomotion across varied terrains and the ability to navigate efficiently around complex dynamic obstacles. This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city. Using model-free reinforcement learning (RL) techniques and privileged learning, we developed a versatile locomotion controller. This controller achieves efficient and robust locomotion over various rough terrains, facilitated by smooth transitions between walking and driving modes. It is tightly integrated with a learned navigation controller through a hierarchical RL framework, enabling effective navigation through challenging terrain and various obstacles at high speed. Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain. These missions demonstrate the system's robustness and adaptability, underscoring the importance of integrated control systems in achieving seamless navigation in complex environments. Our findings support the feasibility of wheeled-legged robots and hierarchical RL for autonomous navigation, with implications for last-mile delivery and beyond.

Design of a Quadruped Robot with Morphological Adaptation through Reconfigurable Sprawling Structure and Method

Morphological adaptation is crucial for animals and robots in navigating unstructured environments. In this article, a quadruped robot with a reconfigurable sprawl posture and posture transformation strategy is proposed, which can transform between different sprawl postures to cope with complex environments and adapt to a dorsal downward fall posture through reverse sprawling. First, the function and structure of the robot are described, including an analysis of the Hawken mechanism's endpoint trajectories and a regional examination of the robot leg's fundamental components, establishing a relationship between the trajectory characteristics and linkage length. Second, the robot posture transformation strategy is analysed, obtaining the geometric dimensional constraints and feasibility regions of the posture transformation. The posture transformation process is quantified, and the robot transformation gait and drive functions are designed. The robot gait and velocity regulation are implemented based on a neural control architecture. Finally, the robot's locomotion and posture transformation are tested using four high‐speed cameras. The results show that the robot can crawl on an inclined surface and continue crawling in a dorsal downward posture post‐fall. Additionally, the robot effectively navigates obstacles and narrow spaces after posture transformation.

Trajectory Planning on Rolling Locomotion of Spherical Movable Tensegrity Robots with Multi-Gait Patterns.

Spherical movable tensegrity robots, resorting to the intrinsic hallmark of being lightweight and resilient, have exhibited tremendous potential in exploring unpredictable terrains and extreme environments where traditional robots often struggle. The geometry of spherical tensegrities is suitable for rolling locomotion, which guarantees the system to react to changing demands, navigate unexplored terrain, and perform missions even after suffering massive damage. The objective of this article is to enrich the type of spherical movable tensegrity robots with multiple kinematic gait patterns and to gain superior motion paths that are in conformity with the intrinsic features of structural rolling locomotion. Aiming at this purpose, three 12-rod spherical tensegrities with multi-gait patterns are investigated, and the dynamic simulation on independent (or evolutionary) gait patterns is conducted and testified on ADAMS. The routing spaces and the blind zones formed by single kinematic gait are compared to assess the suitability of the assigned kinematic gait pattern. Accordingly, we develop a trajectory planning method with the embedding of the steering control strategy into a modified rapidly exploring random tree (MRRT) algorithm to produce qualified marching routes. In the meantime, two momentous evaluation indictors, applicable to multi-gaits tensegrities, are introduced in searching the corresponding optimal gait patterns that conform to specified needs. The techniques are illustrated and validated in simulation with comparisons on several prototypes of tensegrity robots, indicating that the proposed method is a viable means of attaining marching routes on rolling locomotion of spherical movable tensegrity robots.

Locomotion as manipulation with ReachBot.

Caves and lava tubes on the Moon and Mars are sites of geological and astrobiological interest but consist of terrain that is inaccessible with traditional robot locomotion. To support the exploration of these sites, we present ReachBot, a robot that uses extendable booms as appendages to manipulate itself with respect to irregular rock surfaces. The booms terminate in grippers equipped with microspines and provide ReachBot with a large workspace, allowing it to achieve force closure in enclosed spaces, such as the walls of a lava tube. To propel ReachBot, we present a contact-before-motion planner for nongaited legged locomotion that uses internal force control, similar to a multifingered hand, to keep its long, slender booms in tension. Motion planning also depends on finding and executing secure grips on rock features. We used a Monte Carlo simulation to inform gripper design and predict grasp strength and variability. In addition, we used a two-step perception system to identify possible grasp locations. To validate our approach and mechanisms under realistic conditions, we deployed a single ReachBot arm and gripper in a lava tube in the Mojave Desert. The field test confirmed that ReachBot will find many targets for secure grasps with the proposed kinematic design.

Multibody dynamics in robotics with focus on contact events

Abstract Multibody dynamics methodologies have been fundamental tools utilized to model and simulate robotic systems that experience contact conditions with the surrounding environment, such as in the case of feet and ground interactions. In addressing such problems, it is of paramount importance to accurately and efficiently handle the large body displacement associated with locomotion of robots, as well as the dynamic response related to contact-impact events. Thus, a generic computational approach, based on the Newton–Euler formulation, to represent the gross motion of robotic systems, is revisited in this work. The main kinematic and dynamic features, necessary to obtain the equations of motion, are discussed. A numerical procedure suitable to solve the equations of motion is also presented. The problem of modeling contacts in dynamical systems involves two main tasks, namely, the contact detection and the contact resolution, which take into account for the kinematics and dynamics of the contacting bodies, constituting the general framework for the process of modeling and simulating complex contact scenarios. In order to properly model the contact interactions, the contact kinematic properties are established based on the geometry of contacting bodies, which allow to perform the contact detection task. The contact dynamics is represented by continuous contact force models, both in terms of normal and tangential contact directions. Finally, the presented formulations are demonstrated by the application to several robotics systems that involve contact and impact events with surrounding environment. Special emphasis is put on the systems’ dynamic behavior, in terms of performance and stability.

An Underwater Biomimetic Robot that can Swim, Bipedal Walk and Grasp

In developing and exploring extreme and harsh underwater environments, underwater robots can effectively replace humans to complete tasks. To meet the requirements of underwater flexible motion and comprehensive subsea operation, a novel octopus-inspired robot with eight soft limbs was designed and developed. This robot possesses the capabilities of underwater bipedal walking, multi-arm swimming, and grasping objects. To closely interact with the underwater seabed environment and minimize disturbance, the robot employs a cable-driven flexible arm for its walking in underwater floor through a bipedal walking mode. The multi-arm swimming offers a means of three-dimensional spatial movement, allowing the robot to swiftly explore and navigate over large areas, thereby enhancing its flexibility. Furthermore, the robot’s walking arm enables it to grasp and transport objects underwater, thereby enhancing its practicality in underwater environments. A simplified motion models and gait generation strategies were proposed for two modes of robot locomotion: swimming and walking, inspired by the movement characteristics of octopus-inspired multi-arm swimming and bipedal walking. Through experimental verification, the robot’s average speed of underwater bipedal walking reaches 7.26 cm/s, while the horizontal movement speed for multi-arm swimming is 8.6 cm/s.

Optimizing terrestrial locomotion of undulating-fin amphibious robots: Asynchronous control and phase-difference optimization

This paper presents an asynchronous motion-control approach for undulating-fin amphibious robots, with emphasis on optimizing the phase differences of bilateral fin-surface waves to mitigate the pitch-angle instability typically observed in synchronous control. A comprehensive ground dynamics model is used in this study, which facilitates the meticulous analysis of pitch-angle variations. Using simulation software, a virtual prototype is developed to validate the control stability. This approach is further substantiated via experimental tests conducted on a physical prototype in diverse terrestrial environments. Simulation and experimental results indicate that asynchronous motion control methods significantly enhancing postural stability undulating-fin robots during terrestrial locomotion. Experimental findings demonstrate that adopting asynchronous motion control reduces the maximum range of pitch angles by 38.9%–42.3%. This method significantly enhancing postural stability of robot terrestrial locomotion. This study provides new insights for the design of undulating-fin amphibious robots.

Integrated Modular Neural Control for Versatile Locomotion and Object Transportation of a Dung Beetle-Like Robot.

Dung beetles can effectively transport dung pallets of various sizes in any direction across uneven terrain. While this impressive ability can inspire new locomotion and object transportation solutions in multilegged (insect-like) robots, to date, most existing robots use their legs primarily to perform locomotion. Only a few robots can use their legs to achieve both locomotion and object transportation, although they are limited to specific object types/sizes (10%-65% of leg length) on flat terrain. Accordingly, we proposed a novel integrated neural control approach that, like dung beetles, pushes state-of-the-art insect-like robots beyond their current limits toward versatile locomotion and object transportation with different object types/sizes and terrains (flat and uneven). The control method is synthesized based on modular neural mechanisms, integrating central pattern generator (CPG)-based control, adaptive local leg control, descending modulation control, and object manipulation control. We also introduced an object transportation strategy combining walking and periodic hind leg lifting for soft object transportation. We validated our method on a dung beetle-like robot. Our results show that the robot can perform versatile locomotion and use its legs to transport hard and soft objects of various sizes (60%-70% of leg length) and weights (approximately 3%-115% of robot weight) on flat and uneven terrains. The study also suggests possible neural control mechanisms underlying the dung beetle Scarabaeus galenus' versatile locomotion and small dung pallet transportation.

Utilizing Bioinspired Soft Modular Appendages for Grasping and Locomotion in Multi-Legged Robots on Ground and Underwater

Utilizing Bioinspired Soft Modular Appendages for Grasping and Locomotion in Multi-Legged Robots on Ground and Underwater

Amphibious Robots Locomotion Strategies in Unstructured Complex Environments: A Review

In the previous literature, amphibious robots focused mainly on locomotion in underwater and flat land surface manoeuvring. Few amphibious robots focused on unstructured land environments. The amphibious robot designs were more emphasised in academics, leading to more work done in building amphibious robots that mimic biological amphibians, imitating the geometry and overall functionality of the amphibious robots. Developing amphibious robots with propulsive mechanisms for manoeuvring in a water environment received more attention than other functionalities like adaptability on rough natural terrain and obstacle repositioning capability. However, practical applications like reconnaissance and surveying posed challenges in the ground environment, which had unstructured and complex terrain profiles, especially in the transition area. Therefore, reviewing the amphibious robots focused on manoeuvring complex uneven surfaces was essential. The literature had comprehensive review papers on navigation strategies encompassing manoeuvring on flat ground surfaces and underwater locomotion. There was a need for a focused study that highlighted the amphibious robot that manoeuvred in an unstructured land environment. The open challenges and recent solutions by designing new mechanisms and deployment issues were highlighted and reviewed. Hence, the paper addressed a more specific review of amphibious robot locomotion in an unstructured environment. The paper also discussed a case study of an amphibious robot capable of locomotion in unstructured environments. It was envisaged that the review would provide directions and insights to researchers and robotic system designers on developing robust propulsive mechanisms for amphibious robots capable of locomotion in unstructured environments.Keywords: manoeuvrability, mobility, terrain, unified locomotion, mechanism, propulsion

Form-Finding and Evaluation of Spherical Tensegrity Toward Applying in Locomotive Robots

Abstract A tensegrity-based robot is a locomotive robot that operates on the principle of tensegrity, allowing it to change its shape by adjusting its internal prestress. Tensegrity-based robots can be categorized into different types based on their shape, with the spherical tensegrity-based robot garnering the most attention. However, existing designs for spherical tensegrity-based robots tend to be relatively simple and lack standardized criteria for evaluating their performance. This paper proposes an optimization approach using the force density method to design new spherical regular tensegrity configurations. This is achieved by parameterizing the topology and configuration of the structure, taking into account structural symmetry and the even distribution of internal forces. The proposed approach generates not only classical tensegrities but also novel configurations suitable for locomotive robots. To preliminarily evaluate the suitability of classical tensegrities and novel tensegrities to be used as a rolling robot, a set of performance indexes, including inner space, compactability, prestress evenness, gait repeatability, tilt stability ratio, stride length, and path efficiency, are proposed. The proposed indexes can be quickly determined based on the geometry of the tensegrity and thus are useful in the conceptual selection of the spherical tensegrities for rolling robots. They are used to evaluate a set of six spherical tensegrities. Numerical simulations are carried out to verify the feasibility of geometry-based approximating the gait-dependent indexes. Through the evaluation, a novel spherical tensegrity consisting of 15 struts and 60 tendons is identified as a promising candidate for rolling robots.

AI‐enabled bumpless transfer control strategy for legged robot with hybrid energy storage system

AbstractDesigning Hybrid energy storage system (HESS) for a legged robot is significant to improve the motion performance and energy efficiency of the robot. However, switching between the driving mode and regenerative braking mode in the HESS may generate a torque bump, which has brought significant challenges to the stability of the robot locomotion. To address this issue, an AI‐enabled control strategy for bumpless transfer switching is proposed, which is composed of a Proximal Policy Optimisation (PPO)‐based non‐linear active disturbance rejection controller and Deep neural network (DNN)‐based bumpless transfer strategy. We indicate that the proposed intelligent strategy solves bumpless transfer by reducing the torque bump during the system switching process. Meanwhile, the authors analyse the energy driving subsystem and the energy regenerating subsystem based on operational modes and the energy flow. Via the parameters tuning criterion, the authors adopt a PPO algorithm to adaptively tune the parameters of the non‐linear active disturbance rejection controller, which can improve the performance of the primary torque control. A DNN‐based intelligent bumpless transfer strategy is proposed to set the initial value of two switching controllers at a switching moment. Simulations and experimental results validate the effectiveness of the proposed control strategy.

Mathematical Modeling of Robotic Locomotion Systems

This article deals with the presentation of an alternative approach that uses methods of geometric mechanics, which allow one to see into the geometrical structure of the equations and can be useful not only for modeling but also during the design of symmetrical locomotion systems and their control and motion planning. These methods are based on extracting the symmetries of Lie groups from the locomotion system in order to simplify the resulting equations. In the second section, the special two-dimensional Euclidean group SE2 and its splitting into right and left actions are derived. The physical interpretation of the local group and spatial velocities is investigated, and by virtue of the fact that both of these velocities represent the same velocity from a physical point of view, the dependence between them can be found by means of the adjoint action. The third section is devoted to the modeling and analysis of the planar locomotion of the symmetrical serpentine robot; the positions and local group velocities of its links are derived, the vector fields for the local connections are given, and the trajectories of the individual variables in the lateral movement of the kinematic snake are shown. At the end of the article, the overall benefits of the scientific study are summarized, as is the comparison of the results from the simulation phase, while the most significant novelty compared to alternative publications in the field can be considered the realization of this study with a description of the relevant methodology at a detailed level; that is, the locomotion results confirm the suitability of the use of geometric mechanics for these symmetrical locomotion systems with nonholonomic constraints.

Cascade Antidisturbance Control of Hydraulically Driven Bipedal Robots for High Dynamic Locomotion by Using Model Prediction and Task Hierarchical Optimization

Cascade Antidisturbance Control of Hydraulically Driven Bipedal Robots for High Dynamic Locomotion by Using Model Prediction and Task Hierarchical Optimization

Dynamic modeling for continuous locomotion of bionic soft worm-like robot

In the nature, worm-like robot can bend its slender body into an arch bridge when it moves, and it will move quickly in this deformation mode to nimbly escape from enemies once encountering danger. Inspired by this locomotion mechanism, a plenty of bionic soft worm-like robots have been devised and studied adopting quasi-static method. Nevertheless, how to describe the stick-slip transition and the continuity of the entire locomotion process of soft worm-like robots is still a challenging problem. To settle the above issue, a simplified curved beam model is developed to depict a type of soft worm-like robot move forward continuously by controlling its initial curvature in this paper. Based on the curved beam model, a novel dynamic modeling method is further proposed to solve stick-slip nonlinear switchover problem of the soft robot. Since the curved beam is divided into finite segments, an angular variable of an arbitrary point is introduced to elaborate the deformed shape of the curved beam, and then the dimensionless discretized equations of motion can be established via theoretical derivation and solved by implicit integration method. Through dynamic simulation and analysis, a continuous forward locomotion and net displacement of the robot can be easily observed, and several laws of the locomotion are uncovered, which reveals the stick-slip dynamic behaviors. Distinguished from the normal quasi-static modeling approach, the law of motion of the soft worm-like robot is clarified from the dynamic point of view, which will provide important reference for structural design and optimization control of similar soft robots.

Selective-collective scalable locomotion control of helical magnetic swimmers within confined channels using global magnetic fields

Achieving selective-individual and collective control over multiple magnetic microrobots remains a formidable challenge. Existing techniques for controlling identical magnetic swimmers are often limited to two-dimensional, small working spaces, while heterogeneous swimmers can only be controlled in small numbers without the option for collective control. In this study, we present a purely magnetic control methodology for the selective-scalable locomotion of helical magnetic swimmers within confined channels. By employing a unique gradient field distribution, referred to as the "selection field," along with space-uniform rotating fields, we establish a "selection volume" that allows swimmers within it to fully rotate, while those outside remain stationary. We explore various conditions of the selection volume to analyze the locomotion dynamics of the swimmers in relation to the position of the selection volume. Experimental results demonstrate the selective-scalable locomotion of identical robots, ranging from one to an arbitrary number (N) of robots, as well as their selective-individual manipulation for multitasking applications. Furthermore, compatibility of the proposed method with different magnetic torque-driven robots is exemplified through the selective control of rolling robots. Moreover, three-dimensional selective-scalable control is also demonstrated for four swimmers in a quasi-three-dimensional channel, as well as for two swimmers within a true three-dimensional channel.

Motorizing the buckled blister for rotary actuation.

Snap-through bistability was widely exploited for rapid hopping in micro-electro-mechanical systems and soft robots. However, considerable energy input was required to trigger the transition between discrete buckling states blocked by potential wells. Here a dynamic buckling mechanism of a buckled blister constrained inside an outer ring is explored for eliciting rotary actuation via a localized change of curvature in the blister. Due to rotational invariance of the buckled blister, lower energy supply is required to initiate the snap-through of buckling compared to conventional bistable mechanism. The controllability in rotational speed and output torque of the bimetallic blister-based rotator inside a rigid stator is exhibited, and the locomotion is demonstrated with two elastic rings via localized pneumatic actuators. With broad choices of stimulus and material for rings, the findings illustrate the promising potential of two nested rings to create active motions for diverse applications including gearless motors, peristaltic pumps, and locomotive robots.

GestureMoRo: an algorithm for autonomous mobile robot teleoperation based on gesture recognition

Gestures are a common way people communicate. Gesture-based teleoperation control systems tend to be simple to operate and suitable for most people’s daily use. This paper employed a LeapMotion sensor to develop a mobile robot control system based on gesture recognition, which mainly established connections through a client/server structure. The principles of gesture recognition in the system were studied and the relevant self-investigated algorithms—GestureMoRo, for the association between gestures and mobile robots were designed. Moreover, in order to avoid the unstably fluctuated movement of the mobile robot caused by palm shaking, the Gaussian filter algorithm was used to smooth and denoise the collected gesture data, which effectively improved the robustness and stability of the mobile robot’s locomotion. Finally, the teleoperation control strategy of the gesture to the WATER2 mobile robot was realized, and the effectiveness and practicability of the designed system were verified through multiple experiments.