Analysis of the research status of quadruped robot

Motion planning and control issue of quadruped robots is a challenging topic because it requires a lot of professional knowledge and tedious manual design based on traditional control methods. This paper proposes a novel method of training quadruped robots to generate locomotion gaits by combining the deep reinforcement learning. The control problem of quadruped robots is modeled as a Markov Decision Process, and a universal quadruped robot motion control reward mechanism is developed. A variety of gaits for the quadruped robot is generated by adding a gait reference frame. Two deep reinforcement learning algorithms are used for training and comparison. The desired gait locomotion control strategy is generated through training so that the robot can realize stable walking with the desired gait. Finally, the proposed method is tested and evaluated in a simulation environment using the A1 quadruped robot model.

Quadruped robots have great agility, flexibility and stability, which enables them to walk through uneven terrain. Motion control of legged robots is always a difficult problem. Previous approaches mostly either use a predefined gait that results in clumsy and unnatural behavior, or use reinforcement learning approach to generate a gait strategy that needs longtime computation and elegant network design. In this paper, we present an effective approach that uses deep reinforcement learning with prior knowledge to optimize the gait of quadruped robot. By using a specific quadruped robot walking gait as a priori knowledge, this approach adopts the technique of distributed proximal policy optimization to optimize the search for better gait. The proposed approach does not require modelling of complex robots, and has good network convergence speed and learning effect. Simulation results demonstrate that our proposed approach converges faster than other deep reinforcement learning methods without prior knowledge. Besides, our achieved gait has higher speed that is 50% faster than the trot gait without optimization.

Recently, high performance quadruped robots have obtained extensive attention at home and broad, the related research work was developed in view of how to improve the highly dynamic characteristics, high environmental adaptability and heavy payload capacity of quadruped robots and has achieved certain achievements. In order to improve the highly dynamic characteristics of quadruped robots and through analyzing the skeletal anatomy and motion mechanism of the cheetah, many researchers designed the bionic quadruped robots with articulated spine based on imitating the quadrupedal mammal cheetah with torso degrees of freedom and high speed ability, which can improve the maneuverability and stability of the quadruped robots with articulated torso by means of the coordination of torso and legs. Therefore, this paper focuses on the quadruped robots with articulated torso. In a brief summary of previous research results of quadruped robots with highly dynamic characteristics, this paper reviews the state-of-the-art of quadruped robots with articulated torso, and based on the analysis of the research status of the quadruped robots with articulated torso, the research focus and developing trend of them are given.

In unpredictable scenarios, quadruped robots with behavior-controllable capabilities can often improve their adaptability through interaction with users. In this paper, we propose a behavior-controllable motion control method, integrating user commands with adversarial motion priors, enabling the quadruped robot to achieve behavior-controllable capabilities. Firstly, a motion trajectory library is constructed to provide motion prior knowledge. To obtain stable trajectory data for various motions, optimal control methods are used to generate dynamic trajectories with whole-body dynamic constraints. These trajectory data are then standardized and assigned different weights, resulting in the construction of a motion trajectory library for the quadruped robot. Secondly, an adversarial motion prior network structure combined with user commands is proposed. Reward functions tailored to different motion behaviors are designed to achieve behavior control. This network structure acts as a single-motion prior discriminator, which, compared to a multi-motion prior discriminator, avoids complex architectures. Furthermore, the incorporation of user commands effectively addresses the issue where the single-motion prior discriminator struggles to clearly select actions as the dataset expands. Finally, simulations and comparative experiments are conducted to evaluate the effectiveness of the proposed method.

Nowadays, design and development of legged quadruped robots is a quite active area of scientific research. In fact, the legged robots have become popular due to their capabilities to adapt to harsh terrains and diverse environmental conditions in comparison to other mobile robots. With the higher demand for legged robot experiments, more researches and engineers need an affordable and quick way of locomotion algorithm development. In this paper, we present a new open source quadruped robot HyperDog platform, which features 12 RC servo motors, onboard NVIDIA Jetson nano computer and STM32F4 Discovery board. HyperDog is an open-source platform for quadruped robotic software development, which is based on Robot Operating System 2 (ROS2) and micro-ROS. Moreover, the HyperDog is a quadrupedal robotic dog entirely built from 3D printed parts and carbon fiber, which allows the robot to have light weight and good strength. The idea of this work is to demonstrate an affordable and customizable way of robot development and provide researches and engineers with the legged robot platform, where different algorithms can be tested and validated in simulation and real environment. The developed project with code is available on GitHub. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

Computational methods have been successfully applied in structural analysis and drawings of large spatial structures. However, there are still huge challenges in performing conceptual design of structures using computers as what human brain is able to do. A computational method, named performance-based structural synthesis has been proposed and validated to generate and realize planar truss structures. However, it is insufficient for more general spatial truss structures whose geometries and load-carrying requirements are more complex compared with those of planar truss. Therefore, this computational synthesis method based on planar shape grammar was extended and presented in this paper to generate three-dimensional spatial truss structures through building 3D shape grammars and multi-objective hybridized optimization algorithm. Meanwhile techniques of load-generation were employed in the structural synthesis method to realize its engineering application. In the aspect of structural configuration, the method integrates structural topology, geometry and section size allocation; in the aspect of structural design and optimization, it takes into account the combining effects of safety, economy, construction and architectural elegance. Thus the structure generated by this synthesis method must be the best design in terms of the overall structural performance and cost compared with that obtained by traditional design method.

Barriers for big snakes: Incorporating animal behaviour and morphology into road mortality mitigation design

8 - Design for optimum body-structural and running-gear performance efficiency

Chemical energy conversion strategies by photocatalysis and electrocatalysis are promising approaches to alleviating our energy shortages and environmental issues. Due to the 2D layer structure, adjustable composition, unique thermal decomposition and memory properties, abundant surface hydroxyl, and low cost, layered double hydroxides (LDHs) have attracted extensive attention in electrocatalysis, photocatalysis, and photoelectrocatalysis. This review summarizes the main structural characteristics of LDHs, including tunable composition, thermal decomposition and memory properties, delaminated layer, and surface hydroxyl. Next, the influences of the structural characteristics on the photo(electro)catalytic process are briefly introduced to understand the structure–performance correlations of LDHs materials. Recent progress and advances of LDHs in photocatalysis and photoelectrocatalysis applications are summarized. Finally, the challenges and future development of LDHs are prospected from the aspect of structural design and exploring structure-activity relationships in the photo(electro)catalysis applications.

Background: The trachea is an important part of the respiratory system. Long-term airway diseases and the effects of human physiological conditions can cause trachea stenosis. This will seriously affect patients’ physical health. Tracheal stents can provide durable support by means of dilating the narrowed area; they can effectively relieve the patient's breathing difficulties, which is of value that is more practical. Objective: To meet the vital requirements for better adaptation to the human airway environment and prevent complications associated with stent placement, the construction and materials of tracheal stents have been improved constantly. Methods: This paper investigates various representative patents related to the tracheal stent. The structure type, placement method and applications situation of these tracheal stents are discussed. Results: The characteristics of different types of tracheal stents are analyzed. This paper analyzes the main problems in its development. The solutions to the issues and the current and future research on tracheal stents are discussed. Conclusion: The tracheal stents are classified into metal stent, silicone stent, biodegradable stent and hybrid material stent. Further improvements are needed in the aspects of structural design, safety, applicability, biocompatibility, algorithm of drive model of tracheal stent. More related patents about tracheal stents need to be developed.

Quadruped locomotion is challenging for many learning-based algorithms. This is because it requires tedious manual tuning to cope with different types of terrains and is difficult to deploy in reality due to the sim-to-real gap between the training and the testing scenarios. This paper proposes a quadruped robot learning system for agile locomotion which does not require any pre-training and works well in various terrains. We introduce a hierarchical framework that uses reinforcement learning as the high-level policy to adjust the low-level trajectory generator for a better adaptability to various terrains. We compact the observation and the action spaces of reinforcement learning to deploy the proposed framework on a host computer interfaced with the robot. Besides, we design an omnidirectional trajectory generator guided by robot posture, which generates omnidirectional foot trajectories to interact with the environment. Experimental results and the supplementary video demonstrate that our hierarchical framework only trained in simulation can be easily deployed in the real world, and also has the advantages of fast convergence and good terrain adaptability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper presents a hierarchical framework for quadruped robots. It combines a high-level reinforcement learning controller with a posture-guided trajectory generator to adaptively generate omnidirectional motions. Our method is easy to train as it converges fast and does not need to adjust a dozen or so of rewards. The quadruped robot can be deployed in a real environment directly after being trained in simulation. With the trained hierarchical framework deployed on a remote host computer, the robot works well in a variety of real-world environments unseen in the simulation.

Flow sensing is fundamental to both animals and robots for navigation, stability, and interaction with the environment. While animals achieve this through specialised mechanosensory structures, flow-guided autonomy in robotics remains underdeveloped due to the limitations of existing sensors, which often lack directional sensitivity, robustness, or scalability. To address this gap, we present a bioinspired, vision-based airflow sensing architecture that employs vertically suspended flexible filaments that respond passively to airflow, from which flow information is inferred using onboard computer vision. The sensor's utility was demonstrated on a quadruped robot, where it reliably detected flow speed and direction during both static and locomotion experiments. A mathematical model describing the aerodynamic and gravitational forces acting on the filaments was developed and experimentally validated, showing strong agreement with empirical data. The results highlight the potential of this novel sensing architecture in robotic applications. Beyond robotics, the sensor's low-cost and modular design makes it broadly applicable from scientific research and environmental monitoring to education and public engagement in flow-sensing principles.

Self-reconfigurable robot is a kind of robot that is able to recover its original function after partial damage of the robotic system. This is achieved by making changes on the partially damaged robot. This paper put forward a kind of self-reconfigurable quadruped robot based on the resilient theory. First, the concept of the self-reconfigurable robot and the recovery strategy were introduced. Secondly, a novel architecture of modular quadruped robot made by LEGO material was proposed. The connector of interfaces was taken into consideration. At last, the overall mode of the motion and control method with the help of NXT G program were proposed. The analysis of the overall gait ensures the stability of motion in this phase. As for the uncertain environmental damage to the robot, the robot is able to repair itself by changing the movement type automatically to ensure it continues to work. The result shows that the quadruped robot meets the requirements of resilience and self-reconfiguration.

In this study, we develop a quadruped walking robot, assuming, such as, a human cooperative tasks in a construction building or a rescue activity in a disaster environment. Pneumatic cylinders are employed as driving actuators since it has explosion proof and inherent compliance. Owing to the compliance feature external force is easily estimated based on a inner pressure of cylinder. We compose a compliance control system on each leg based on the estimated floor reaction force with a disturbance observer. After mentioning a overview of the developed quadruped walking robot, we describe a kinematics, a statics and a control system of each leg. We propose a motion control strategy based on the floor reaction force estimated using a disturbance observer. The validity of the proposed control system is verified through some experiments.

This work concerns biped adaptive walking control on slope terrains with online trajectory generation. In terms of the lack of satisfactory performances of the traditional simplified single-layered Central Pattern Generator (CPG) model in engineering applications where robots face unknown environments and access feedback, this paper presents a Multi-Layered CPG (ML-CPG) model based on a half-center CPG model. The proposed ML-CPG model is used as the underlying low-level controller for a quadruped robot to generate adaptive walking patterns. Rhythm-generation and pattern formation interneurons are modeled to promptly generate motion rhythm and patterns for motion sequence control, while motoneurons are modeled to control the output strength of the joint in real time according to feedback. Referring to the motion control mechanisms of animals, a control structure is built for a quadruped robot. Multi-sensor models abstracted from the neural reflexes of animals are involved in all the layers of neurons through various feedback paths to achieve adaptability as well as the coordinated motion control of a robot’s limbs. The simulation experiments verify the effectiveness of the presented ML-CPG and multi-layered reflexes strategy.