Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation

An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot's locomotion control as a central pattern generator (CPG), sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs' oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation.Comment: 48 pages, 16 figures, Information Sciences 201

Multistable Phase Regulation for Robust Steady and Transitional Legged Gaits

We develop robust methods that allow specification, control, and transition of a multi-legged robot’s stepping pattern—its gait—during active locomotion over natural terrain. Resulting gaits emerge through the introduction of controllers that impose appropriately-placed repellors within the space of gaits, the torus of relative leg phases, thereby mitigating against dangerous patterns of leg timing. Moreover, these repellors are organized with respect to a natural cellular decomposition of gait space and result in limit cycles with associated basins that are well characterized by these cells, thus conferring a symbolic character upon the overall behavioral repertoire. These ideas are particularly applicable to four- and six-legged robots, for which a large variety of interesting and useful (and, in many cases, familiar) gaits exist, and whose tradeoffs between speed and reliability motivate the desire for transitioning between them during active locomotion. We provide an empirical instance of this gait regulation scheme by application to a climbing hexapod, whose “physical layer” sensor-feedback control requires adequate grasp of a climbing surface but whose closed loop control perturbs the robot from its desired gait. We document how the regulation scheme secures the desired gait and permits operator selection of different gaits as required during active climbing on challenging surfaces

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

MOTION CONTROL SIMULATION OF A HEXAPOD ROBOT

This thesis addresses hexapod robot motion control. Insect morphology and locomotion patterns inform the design of a robotic model, and motion control is achieved via trajectory planning and bio-inspired principles. Additionally, deep learning and multi-agent reinforcement learning are employed to train the robot motion control strategy with leg coordination achieves using a multi-agent deep reinforcement learning framework. The thesis makes the following contributions: First, research on legged robots is synthesized, with a focus on hexapod robot motion control. Insect anatomy analysis informs the hexagonal robot body and three-joint single robotic leg design, which is assembled using SolidWorks. Different gaits are studied and compared, and robot leg kinematics are derived and experimentally verified, culminating in a three-legged gait for motion control. Second, an animal-inspired approach employs a central pattern generator (CPG) control unit based on the Hopf oscillator, facilitating robot motion control in complex environments such as stable walking and climbing. The robot\u27s motion process is quantitatively evaluated in terms of displacement change and body pitch angle. Third, a value function decomposition algorithm, QPLEX, is applied to hexapod robot motion control. The QPLEX architecture treats each leg as a separate agent with local control modules, that are trained using reinforcement learning. QPLEX outperforms decentralized approaches, achieving coordinated rhythmic gaits and increased robustness on uneven terrain. The significant of terrain curriculum learning is assessed, with QPLEX demonstrating superior stability and faster consequence. The foot-end trajectory planning method enables robot motion control through inverse kinematic solutions but has limited generalization capabilities for diverse terrains. The animal-inspired CPG-based method offers a versatile control strategy but is constrained to core aspects. In contrast, the multi-agent deep reinforcement learning-based approach affords adaptable motion strategy adjustments, rendering it a superior control policy. These methods can be combined to develop a customized robot motion control policy for specific scenarios

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

Prescription of rhythmic patterns for legged locomotion

As the engine behind many life phenomena, motor information generated by the central nervous system (CNS) plays a critical role in the activities of all animals. In this work, a novel, macroscopic and model-independent approach is presented for creating different patterns of coupled neural oscillations observed in biological central pattern generators (CPG) during the control of legged locomotion. Based on a simple distributed state machine, which consists of two nodes sharing pre-defined number of resources, the concept of oscillatory building blocks (OBBs) is summarised for the production of elaborated rhythmic patterns. Various types of OBBs can be designed to construct a motion joint of one degree-of-freedom (DOF) with adjustable oscillatory frequencies and duty cycles. An OBBs network can thus be potentially built to generate a full range of locomotion patterns of a legged animal with controlled transitions between different rhythmic patterns. It is shown that gait pattern transition can be achieved by simply changing a single parameter of an OBB module. Essentially this simple mechanism allows for the consolidation of a methodology for the construction of artificial CPG architectures behaving as an asymmetric Hopfield neural network. Moreover, the proposed CPG model introduced here is amenable to analogue and/or digital circuit integration