Trends in the control of hexapod robots: a survey

The static stability of hexapods motivates their design for tasks in which stable locomotion is required, such as navigation across complex environments. This task is of high interest due to the possibility of replacing human beings in exploration, surveillance and rescue missions. For this application, the control system must adapt the actuation of the limbs according to their surroundings to ensure that the hexapod does not tumble during locomotion. The most traditional approach considers their limbs as robotic manipulators and relies on mechanical models to actuate them. However, the increasing interest in model-free models for the control of these systems has led to the design of novel solutions. Through a systematic literature review, this paper intends to overview the trends in this field of research and determine in which stage the design of autonomous and adaptable controllers for hexapods is.The first author received funding through a doctoral scholarship from the Portuguese Foundation for Science and Technology (FCT) (Grant No. SFRH/BD/145818/2019), with funds from the Portuguese Ministry of Science, Technology and Higher Education and the European Social Fund through the Programa Operacional Regional Norte. This work has been supported by the FCT national funds, under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020

Locomotion performance of hexapod robots on rough substrates and the influence of leg compliance

ABSTRACT OF THE THESISLocomotion performance of hexapod robots on rough substrates and the influence of leg compliancebyAmartya Bhattacharyya Master of Science in Engineering Sciences (Mechanical Engineering)University of California San Diego, 2019Professor Nicholas Gravish, ChairHexapod Robots are a complex system where six legs are connected to the main body which acts as a support frame. A lot of research has been performed in this field from the study of six legged insects to present day implementations where the robot uses its own decision making network. The motivation for this field are the various advantages that hexapedal robots provide like; Obstacle climbing capability, omnidirectional motion, variable geometry, stability, access to uneven terrain etc. At the same time they also have many disadvantages like low energy efficiency, low speeds, complexity of operation and design and especially a lot of attention has to be given to path and gait planning. Therefore, in this paper wexiiuse an open loop platform for our robot and test the performance on simulated rough substrates. Using the results we propose a compliant leg design which will improve the performance while maintaining the stability. We compare the new design with solid legs to quantify the gain. And also test for the shear force limits to make sure the design is ready to be tested on a robot for full length runs. With a goal to utilize the new design and simplify the requirements of complicated neural networks for gait planning

Fast biped walking with a neuronal controller and physical computation

Biped walking remains a difficult problem and robot models can greatly {facilitate} our understanding of the underlying biomechanical principles as well as their neuronal control. The goal of this study is to specifically demonstrate that stable biped walking can be achieved by combining the physical properties of the walking robot with a small, reflex-based neuronal network, which is governed mainly by local sensor signals. This study shows that human-like gaits emerge without {specific} position or trajectory control and that the walker is able to compensate small disturbances through its own dynamical properties. The reflexive controller used here has the following characteristics, which are different from earlier approaches: (1) Control is mainly local. Hence, it uses only two signals (AEA=Anterior Extreme Angle and GC=Ground Contact) which operate at the inter-joint level. All other signals operate only at single joints. (2) Neither position control nor trajectory tracking control is used. Instead, the approximate nature of the local reflexes on each joint allows the robot mechanics itself (e.g., its passive dynamics) to contribute substantially to the overall gait trajectory computation. (3) The motor control scheme used in the local reflexes of our robot is more straightforward and has more biological plausibility than that of other robots, because the outputs of the motorneurons in our reflexive controller are directly driving the motors of the joints, rather than working as references for position or velocity control. As a consequence, the neural controller and the robot mechanics are closely coupled as a neuro-mechanical system and this study emphasises that dynamically stable biped walking gaits emerge from the coupling between neural computation and physical computation. This is demonstrated by different walking experiments using two real robot as well as by a Poincar\'{e} map analysis applied on a model of the robot in order to assess its stability. In addition, this neuronal control structure allows the use of a policy gradient reinforcement learning algorithm to tune the parameters of the neurons in real-time, during walking. This way the robot can reach a record-breaking walking speed of 3.5 leg-lengths per second after only a few minutes of online learning, which is even comparable to the fastest relative speed of human walking

Towards Fault Diagnosis in Reconfigurable Robot

東京電機大学201

Self-Organizing Neural Gait Generator for Multi-Legged Walking Robot

Vzory chůze popisují periodicky se opakující kráčivý pohyb vícenohého robotu určením fáze pohybu jednotlivých nohou. Aby mohl robot autonomně vykonávat úkoly ve špatně přístupném měnícím se prostředí, je nutné proces lokomoce automatizovat. Během lokomoce probíhá v neurálním systému mnoho komplexních procesů, jejichž některé principy jsou popsány díky probíhajícímu výzkumu lokomoce vícenohých organismů. Některé z těchto principů, jako například Centrální Generátory Vzorů (CGV) a pravidla určující vzájemnou koordinaci nohou, jsou v této práci využity. CGV je neurální oscilátor, který v živých organismech produkuje rytmus pro lokomoci. Koordinační pravidla určují, jak jsou pohyby nohou mezi sebou v rámci fáze koordinovány. Řídící systémy navržené pro řízení lokomoce často vyžadují proces manuálního zadávání velkého množství hyperparametrů určujících konkrétní vzor chůze, což je proces, který se tato práce snaží automatizovat. V této práci jsou představeny dvě metody, které se různým způsobem vypořádávají s neznámým vztahem mezi fází CGV a pohybovými akcemi nohou. První z metod využívá aproximace vztahu mezi vzdáleností stavů CGV ve stavovém prostoru a jejich vzájemným fázovým posunem. Druhá metoda odhaduje neznámou fázi CGV a hledá vztah mezi fází CGV a jeho stavy. Obě metody úspěšně generují všechny tři požadované vzory chůze, což je demonstrováno simulacemi šestinohého kráčejícího robotu v simulátoru CoppeliaSim.The gait patterns describe periodically repeating motion of a legged robot by determining a phase of its legs' movement. If a robot on a long-term mission in an inaccessible unknown dynamic environment should function autonomously, it is crucial to automatize the locomotion process. The ongoing research of legged organisms' locomotion describes some principles of complex neural system processes, such as Central Pattern Generators (CPGs) and inter-leg coordination rules used in this thesis. The CPG is a neural oscillator producing rhythm for locomotion in living organisms. The coordination rules determine how legs' actions are coordinated within the CPG's phase. Many locomotion controllers require a process of hand-setting many gait-pattern-determining hyperparameters, which this thesis aims to automatize. Two different methods are proposed in this work, dealing with the unknown relation between the CPG's phase and the legs' actions. The first method uses an approximation of a relation between a distance of CPG's states in its state space and the phase offset of the CPG's states. The second method estimates CPG's unknown phase and finds the phase's relation to CPG's states. Both methods successfully generate all three desired gait patterns, which is demonstrated by running simulations on a hexapod walking robot in the CoppeliaSim simulator

Integrative Biomimetics of Autonomous Hexapedal Locomotion

Dürr V, Arena PP, Cruse H, et al. Integrative Biomimetics of Autonomous Hexapedal Locomotion. Frontiers in Neurorobotics. 2019;13: 88.Despite substantial advances in many different fields of neurorobotics in general, and biomimetic robots in particular, a key challenge is the integration of concepts: to collate and combine research on disparate and conceptually disjunct research areas in the neurosciences and engineering sciences. We claim that the development of suitable robotic integration platforms is of particular relevance to make such integration of concepts work in practice. Here, we provide an example for a hexapod robotic integration platform for autonomous locomotion. In a sequence of six focus sections dealing with aspects of intelligent, embodied motor control in insects and multipedal robots—ranging from compliant actuation, distributed proprioception and control of multiple legs, the formation of internal representations to the use of an internal body model—we introduce the walking robot HECTOR as a research platform for integrative biomimetics of hexapedal locomotion. Owing to its 18 highly sensorized, compliant actuators, light-weight exoskeleton, distributed and expandable hardware architecture, and an appropriate dynamic simulation framework, HECTOR offers many opportunities to integrate research effort across biomimetics research on actuation, sensory-motor feedback, inter-leg coordination, and cognitive abilities such as motion planning and learning of its own body size