결합 가능한 모듈형 4족 로봇의 설계 및 작업 계획에 대한 연구

This paper investigates dynamic gait planning optimization and balance control of quadruped robots under external disturbance forces. First, a platform of quadruped walking robot with fourteen active degrees of freedom is designed. Then, a forward kinematic model of joints is built for quadruped robots based on Denavit-Hartenberg(D-H) method. The inverse kinematic equations are solved to result in joint values when the desired position and orientation are specified. A dynamic gait planning algorithm is proposed and tested on the quadruped robot. The planning function is established to create some point-to-point trajectories. The angle values of the joints can be calculated by using the inverse kinematics equations for every moment. Considering the external distribution a balance control approach is proposed to stabilize the robot based on the information from the attitude sensors. The walking is stabilized by a feedback control that uses a three-axis acceleration sensor. Experiments have been performed on the quadruped robot. The results showed that the proposed methods work well in dynamic gait planning and external disturbances of a quadruped bionic robot.

In legged systems design an important question applies to: "What can be inferred from the performance of a legged robot of a similarly configured system, but scaled to a smaller or larger size?" Our work attempts to answer this question and set the basis for a systematic approach in sizing legged robots. This paper focuses on the influence of permanent magnet DC electric drives on the size of quadruped running robots. The reason is twofold. First, many of the existing legged machines have used such actuators for propulsion. The second, the performance of electric motors scales differently from torque- speed requirements of legged robots. Specifically, we show that there exists a particularly sized quadruped running robot that is superior according to desired performance criteria, and under the existing technologic limitations and economic restraints. Therefore, valuable information on legged systems design and insight for optimizing the size of a quadruped robot emerges.

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.

Quadrupedal animals leverage flexible spines to achieve agile locomotion, yet most robotic counterparts, like the Kemba robot, retain rigid or revolute spinal joints, limiting dynamic performance. While prior studies suggest that prismatic spines enhance acceleration in quadruped robots, these often neglect physical actuator constraints inherent in real-world systems. This study investigates spine morphology—rigid, revolute, and prismatic—on the steady-state bounding of Kemba using a dynamics-driven framework. Planar models for each spine configuration integrate actuator torque, velocity, and piston force constraints, alongside complementarity conditions for ground and valve contact interactions. A second-order Radau contact-implicit direct collocation method optimises trajectories using a bio-inspired contact sequence derived from cheetah and greyhound gaits. Results demonstrate the prismatic spine’s superiority in stride length and velocity over rigid and revolute designs, attributed to enhanced leg extension and ground reaction force alignment. The prismatic spine also exhibits energy storage potential through linear actuation, suggesting efficiency gains. These findings underscore the importance of co-optimising spine morphology and actuator constraints in bio-inspired robots. This work bridges simulation-based biomechanics with practical robotic design, advancing Kemba’s capabilities. Future efforts will experimentally validate these models through the physical integration of a prismatic spine.

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>

With biped and quadruped robots becoming more prevalent in society, there is a strong push for universities to update their robotics curriculum to teach students how to design, build, and program legged robotic systems. One of the challenges associated with developing legged robotics curriculum is finding opportunities for students to engage in hands-on legged robotic activities that complement theory and enhance the overall learning process. This challenge is particularly evident at smaller universities or universities with limited research budgets where the cost of purchasing a quadruped platform can be prohibitive. With the recent release of several open-source quadruped robot designs, there is now an opportunity to incorporate quadruped platforms into more university classrooms. This paper outlines our approach to modifying one of these open-source designs to support our department's education, outreach, and research goals. The development of our modified, opensource quadruped platform (OpenMutt) was conducted under the auspices of a capstone mechanical engineering design project to demonstrate how the platform builds on existing curriculum and to identify areas where additional robotic instruction may be warranted. Our results indicate that it is possible to use capstone engineering design projects to identify and address potential gaps in existing curriculum.

Ground reaction forces (GRFs) are the primary interaction forces that enable a legged robot to maintain balance and perform locomotion. Most quadruped robot controllers estimate GRFs indirectly using joint torques and a kinematic model, which depend on assumptions and are highly sensitive to modeling errors. In contrast, direct sensing of contact forces at the feet provides more accurate and immediate feedback. Beyond force magnitude, tactile sensing also enables richer contact interpretation, such as detecting force direction and surface properties. In this work, we show how tactile sensor information can be used inside the feedback of the control loop to achieve compliance of legged robots during ground contact. The three main contributions are (i) a fast and computationally efficient 3D force reconstruction method tailored for spherical tactile sensors, (ii) a tactile admittance controller that adjusts leg motions to achieve the desired GRFs and compliance, and (iii) experimental validation on a quadruped robot, demonstrating enhanced load distribution and balance during external perturbations and locomotion. The results show that the peak ground reaction forces were reduced by 55% while balancing on a beam. During a locomotion scenario involving sudden touchdown after a fall, the tactile admittance controller reduced oscillations and regained stability compared to proportional–derivative (PD) control.

This paper reports the algorithm of trajectory planning and the strategy of four-leg coordination for quasi-static stair climbing in a quadruped robot. This development is based on the geometrical interactions between robot legs and the stair, starting from single-leg analysis, followed by two-leg collaboration, and then four-leg coordination. In addition, a brief study on the robot’s locomotion stability is also included. Finally, simulation and experimental testing were executed to evaluate the performance of the algorithm.

Robotic technologies will continue to enter new applications in addition to automated manufacturing and logistics. Once mobile robots can also operate outside of today's special facilities, they have the potential to relieve us of dirty and dangerous labor in various areas. However, for this purpose, these machines will need to be able to navigate autonomously in complex natural, urban, and industrial settings. This thesis addresses the development of locomotion skills for legged robots in challenging environments. Our work focuses on perceptive locomotion where exteroceptive sensing of the surrounding is exploited to plan and control the robot’s motion. This enables quadrupedal robots to negotiate rough terrain through carefully selected contacts. In this work, we evaluate different sensing technologies and analyze their performance for local dense terrain mapping on a mobile robot. We include special conditions such as close range objects and the influence of ambient light as we find them in real-world applications. By modeling the error characteristics of the sensors, the robot can judge the quality of the resulting terrain reconstruction. As the robot moves, the surrounding is continuously mapped to capture new areas and update regions which have changed. We contribute with a mapping framework that models the terrain from a robot-centric perspective. To this end, we present a novel approach for the error propagation from the robot's state estimation to the representation of the map. This formulation allows for robust and high-rate local mapping that is independent of a global localization method. We introduce our approach to locomotion planning, which finds safe footholds along with collision-free swing-leg motions, leveraging the generated terrain map. A nonlinear optimization finds postures that respect kinematic and stability constraints. We experimentally verify this work with torque-controllable quadrupedal robots that autonomously traverse obstacles, such as rubble, steps, gaps, and stairs without prior knowledge of the scene or any additional equipment. The locomotion planner re-plans its motion at every step in real-time, to cope with disturbances and dynamic environments. For the control of the legged robot, we contribute architecturally to the versatile and task-oriented motion execution. This method enables the robust tracking of motion plans, even with significant mismatches between the models and reality. In addition to rough terrain locomotion, we demonstrate the integration of our method for applications, such as whole-body stair climbing, manipulation, jumping, docking, inspection, payload delivery, dancing, and more. Our approach is thoroughly validated with the quadrupedal robot ANYmal in realistic long-term missions for autonomous industrial inspection and search and rescue. Finally, we extend our work with the design and implementation of a collaborative navigation framework for ground and flying robots. The ground vehicle utilizes the data captured by the flying robot to navigate uncharted…

Several legged robots have been developed in recent years and proved that they are an effective transportation system on an uneven terrain. In particular, quadruped robots are regarded as a new trend in robotics due to their superior gait stability and robustness to disturbances. More recently, many robotics researchers are making their best efforts to improve the locomotion speed, as well as the stability and robustness, of quadruped robots. The high-speed locomotion creates various challenges in the development of actuators, mechanical design, and control algorithms of the robot. In this paper, a linear actuation system for the high-speed locomotion of a quadruped robot is introduced. The proposed actuator is designed based on the principle of brushed direct-current electric motor systems. For the minimal impedance and improved power capacity, the actuator is designed with dual layers of cores, which are aligned parallel to permanent magnets. The mechanical and electrical properties of the actuation system, such as back-drivability, controllability, and response time, are verified by experimental results. A robotic leg, which is the rear leg of a Cheetah-like robot, is designed with the proposed actuator, and experimental results for trajectory tracking performance are presented.

Chapter 7 - Legged Robots with Bioinspired Morphology

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.

For the design and application of quadruped robots, the movement stability is very important. In this paper, we proposed a movement stability criterion, which can be used for the design, gait planning and control of a quadruped robot. Such criterion considers the geometry and mass properties (mass and mass of center) and their variation during movement, hence overcoming the shortcomings of previous works. The dynamic model is derived and the effects of the weighty leg of the total gravity center is provided. Then, the relationship between stability margin and design parameter of robot is established. As a practical application, an effective gait planning method is addressed by using the criterion. It adjusts the center of gravity to vary along desired path. Finally, a quadruped turtle robot is developed. It is composed of four 3-DOF lightweight modularized legs, an elastic neck with continuous curvature, a shell opening and closing device, a carbon fiber torso frame and control system. Based on it, some experiments are implemented to verify the proposed method.

We exploit the complementary strengths of vision and pro-prioception to develop a point-goal navigation system for legged robots, called VP-Nav. Legged systems are capable of traversing more complex terrain than wheeled robots, but to fully utilize this capability, we need a high-level path planner in the navigation system to be aware of the walking capabilities of the low-level locomotion policy in varying environments. We achieve this by using proprioceptive feedback to ensure the safety of the planned path by sensing unexpected obstacles like glass walls, terrain properties like slipperiness or softness of the ground and robot properties like extra payload that are likely missed by vision. The navigation system uses onboard cameras to generate an occupancy map and a corresponding cost map to reach the goal. A fast marching planner then generates a target path. A velocity command generator takes this as input to generate the desired velocity for the walking policy. A safety advisor module adds sensed unexpected obstacles to the occupancy map and environment-determined speed limits to the velocity command generator. We show superior performance compared to wheeled robot baselines, and ablation studies which have disjoint high-level planning and low-level control. We also show the real-world deployment of VP-Nav on a quadruped robot with onboard sensors and computation. Videos at https://navigation-locomotion.github.io

In legged robot movement planning, the leg must be carefully design before trajectory analysis can be done. The objective of this paper is to develop a 3 DOF leg which will be used in quadruped robot. In addition, forward kinematic and comparison between real and simulation of the leg is presented. To achieve the objective, SolidWorks 2013 x64 Edition is used to develop the 3D modeling of the leg while SimMechanics with First Generation Format was applied to export the models to Simulink. For the comparison purposes, real model of 3 DOF leg with Arduino Pro Mini 328 - 5V/16MHz as a microcontroller to control the rotation of three servomotors was constructed. With MIT AI2 Companion software, android apps is developed to send signal to rotate each servomotor wirelessly. The zero position of the leg robot has been determined and the maximum rotation range of each servomotor. This is very important in determination of D-H Parameters which allow the resolving of kinematics problems. It is found that specific rotations of each servomotor provide the trajectory pattern of the leg which is compared through Simulink and real model. Nevertheless, there are errors between simulation and real position of the robot leg due to the open loop system.