Chapter 7 - Legged Robots with Bioinspired Morphology

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.

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.

Fundamental principles and recent methods for investigating the nonlinear dynamics of legged robot motions with respect to control, stability and design are discussed. One of them is the still challenging problem of producing dynamically stable gaits. The generation of fast walking or running motions require methods and algorithms adept at handling the nonlinear dynamical effects and stability issues which arise. Reduced, recursive multibody algorithms, a numerical optimal control package, and new stability and energy performance indices are presented which are well-suited for this purpose. Difficulties and open problems are discussed along with numerical investigations into the proposed gait generation scheme. Our analysis considers both biped and quadrupedal gaits with particular reference to the problems arising in soccer-playing tasks encountered at the RoboCup where our team, the Darmstadt Dribbling Dackels, participates as part of the German Team in the Sony Legged Robot League.KeywordsOptimal Control ProblemMultibody SystemGait CycleHumanoid RobotGear RatioThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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…

최근 다양한 환경에서 지능형 로봇의 안정된 이동과 작업계획에 대한 연구가 이루어지고 있다. 본 논문에서는 상하 결합가능한 구조의 4족 로봇의 설계 및 작업 계획방법을 제안한다. 제안하는 4족 로봇은 리니어 모터를 이용하여 다리 길이를 조절하고, 팔각뿔 형태의 도킹모듈을 이용하여 상하 결합과 분리가 가능하도록 설계하였다. 또한 로봇이 다양한 환경에서 안정된 이동과 정보 수집을 위하여 지자기 센서, PSD 센서, LRF 센서와 카메라를 사용하였다. 리니어 모터를 이용한 장애물 회피 동작방법과 상하 결합 동작방법을 제안하고 구현하였다. 로봇은 다리 길이를 조절하여 장애물을 극복하고, 두 대의 로봇이 상하 결합을 통하여 협력 작업방법을 제안하였다. 두 대의 4족 로봇이 상하 결합을 통하여 4족과 6족 보행을 하고, 상부 결합 로봇의 4개의 다리를 4개의 팔 또는 2개의 팔로 사용할 수 있으며, 결합된 로봇을 이용하여 물건 옮기기 작업을 구현하고, 각 동작들을 실제 실험으로 기능을 검증하였다. There are many researches to develop robots that improve its mobility and task planning to adapt in various uneven environments. In this paper, we propose the design method and task planning of quadruped robot which can have top-bottom docking structure. The proposed quadruped robot is designed to adjust leg length using linear actuators and perform top-bottom docking and undocking using octagonal cone shaped docking module. Also, to stable walking and information gathering in the various environments, a geomagnetic sensor, PSD sensor, LRF sensor and camera. We propose an obstacle avoidance method and the topbottom docking algorithm of the two quadruped robots using linear actuator. The robot can overcome obstacles using adjusting leg length and activate the top-bottom docking function. The top-bottom docking robots of two quadruped robot can walk 4 legged walking and 6 legged walking, and use 4 arms or 2 arms the upper. We verified that the docking robots can carry objects using 4 leg of the upper robot.

Recent work has shown results on learning navigation policies for idealized cylinder agents in simulation and transferring them to real wheeled robots. Deploying such navigation policies on legged robots can be challenging due to their complex dynamics, and the large dynamical difference between cylinder agents and legged systems. In this work, we learn hierarchical navigation policies that account for the low-level dynamics of legged robots, such as maximum speed, slipping, contacts, and learn to successfully navigate cluttered indoor environments. To enable transfer of policies learned in simulation to new legged robots and hardware, we learn dynamics-aware navigation policies across multiple robots with robot-specific embeddings. The learned embedding is optimized on new robots, while the rest of the policy is kept fixed, allowing for quick adaptation. We train our policies across three legged robots in simulation - 2 quadrupeds (A1, AlienGo) and a hexapod (Daisy). At test time, we study the performance of our learned policy on two new legged robots in simulation (Laikago, 4-legged Daisy), and one real-world quadrupedal robot (A1). Our experiments show that our learned policy can sample-efficiently generalize to previously unseen robots, and enable sim-to-real transfer of navigation policies for legged robots.

We discuss challenges a sprawling posture legged robot faces when moving in a complex environment. These kind of robots can be used for search and rescue applications when proper sensors are used and placed correctly. Finding an adequate position for placing sensors on legged robots with a segmented spine to maximize the information retrieval is not trivial. In this paper, we talk about equipping the salamanderlike robot Pleurobot with necessary sensors to understand its challenges. Our setup gathers visual and inertial information for 3D mapping and localization. To do so, we have come up with three different experimental terrains to analyze the sensing behavior. The results are examples to understanding the behavior of quadrupedal legged robots similar to Pleurobot in search and rescue scenarios.

Typically, control strategies for legged robots have been developed to adapt their leg movements to deal with complex terrain. When the legs are extended in search of ground contact to support the robot body, this can result in the center of gravity (CoG) being raised higher from the ground and can lead to unstable locomotion if it deviates from the support polygon. An alternative approach is body adaptation, inspired by millipede/centipede locomotion behavior, which can result in low ground clearance and stable locomotion. In this study, we propose novel proactive neural control with online unsupervised learning, allowing multi-segmented, legged robots to proactively adapt their body to follow the surface contour and maintain efficient ground contact. Our approach requires neither kinematics nor environmental models. It relies solely on proprioceptive sensory feedback and short-term memory, enabling the robots to deal with complex 3D terrains. In comparison to traditional reflex-based control, our approach results in smoother and more energy-efficient robot locomotion on terrains with concave and convex curves or slopes of varying degrees in both simulation and real-world implementation.

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.

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.

Animals adjust their leg stiffness and stride angle in response to changing ground conditions and gait parameters, resulting in improved stability and reduced energy consumption. This paper presents an online learning algorithm that attempts to mimic such animal behavior by maximizing energy efficiency on the fly or equivalently, minimizing the cost of transport of legged robots by adaptively changing the leg stiffness and stride angle while the robot is traversing on grounds with unknown characteristics. The algorithm employs an approximate stochastic gradient method to change the parameters in real-time, and has the following advantages: (1) the algorithm is computationally efficient and suitable for real-time operation; (2) it does not require training; (3) it is model-free, implying that precise modeling of the robot is not required for good performance; and (4) the algorithm is generally applicable and can be easily incorporated into a variety of legged robots with adaptable parameters and gaits beyond those implemented in this paper. Results of exhaustive performance assessment through numerical simulations and experiments on an under-actuated quadruped robot with compliant legs are included in the paper. The robot platform used a pneumatic piston in each leg as a variable, passive compliant element. Performance evaluation using simulations and experiments indicated that the algorithm was capable of converging to near-optimal values of the cost of transport for given operating conditions, terrain properties, and gait characteristics with no prior knowledge of the terrain and gait conditions. The simplicity of the algorithm and its demonstrably improved performance make the approach of this paper an excellent candidate for adaptively controlling tunable parameters of compliant, legged robots.