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

A hexapod walker using a heterarchical architecture for action selection

Schilling M, Paskarbeit J, Hoinville T, et al. A hexapod walker using a heterarchical architecture for action selection. Frontiers in Computational Neuroscience. 2013;7:126.Moving in a cluttered environment with a six-legged walking machine that has additional body actuators, therefore controlling 22 DoFs, is not a trivial task. Already simple forward walking on a flat plane requires the system to select between different internal states. The orchestration of these states depends on walking velocity and on external disturbances. Such disturbances occur continuously, for example due to irregular up-and-down movements of the body or slipping of the legs, even on flat surfaces, in particular when negotiating tight curves. The number of possible states is further increased when the system is allowed to walk backward or when front legs are used as grippers and cannot contribute to walking. Further states are necessary for expansion that allow for navigation. Here we demonstrate a solution for the selection and sequencing of different (attractor) states required to control different behaviors as are forward walking at different speeds, backward walking, as well as negotiation of tight curves. This selection is made by a recurrent neural network (RNN) of motivation units, controlling a bank of decentralized memory elements in combination with the feedback through the environment. The underlying heterarchical architecture of the network allows to select various combinations of these elements. This modular approach representing an example of neural reuse of a limited number of procedures allows for adaptation to different internal and external conditions. A way is sketched as to how this approach may be expanded to form a cognitive system being able to plan ahead. This architecture is characterized by different types of modules being arranged in layers and columns, but the complete network can also be considered as a holistic system showing emergent properties which cannot be attributed to a specific module

Lose a Leg but not Your Head – A Cognitive Extension of a Biologically-inspired Walking Architecture

Schilling M. Lose a Leg but not Your Head – A Cognitive Extension of a Biologically-inspired Walking Architecture. Procedia Computer Science. 2016;88:102-106

Hexapod locomotion : a nonlinear dynamical systems approach

The ability of walking in a wide variety of terrains is one of the most important features of hexapod insects. In this paper we describe a bio-inspired controller able to generate locomotion and switch between different type of gaits for an hexapod robot. Motor patterns are generated by coupled Central Pattern Generators formulated as nonlinear oscillators. These patterns are modulated by a drive signal, proportionally changing the oscillators frequency, amplitude and the coupling parameters among the oscillators. Locomotion initiation, stopping and smooth gait switching is achieved by changing the drive signal. We also demonstrate a posture controller for hexapod robots using the dynamical systems approach. Results from simulation using a model of the Chiara hexapod robot demonstrate the capability of the controller both to locomotion generation and smooth gait transition. The postural controller is also tested in different situations in which the hexapod robot is expected to maintain balance. The presented results prove its reliability

Consider the robot - Abstraction of bioinspired leg coordination and its application to a hexapod robot under consideration of technical constraints

Paskarbeit J. Consider the robot - Abstraction of bioinspired leg coordination and its application to a hexapod robot under consideration of technical constraints. Bielefeld: Universität Bielefeld; 2017.To emulate the movement agility and adaptiveness of stick insects in technical systems such as piezo actuators (Szufnarowski et al. 2014) or hexapod robots (Schneider, Cruse et al. 2006), a direct adaptation of bioinspired walking controllers like WALKNET has often been suggested. However, stick insects have very specific features such as adhesive foot pads that allow them to cling to the ground. Typically, robots do not possess such features. Besides, robots tend to be bigger and heavier than their biological models, usually possessing a different mass distribution as well. This leads to different mechanical and functional properties that need to be addressed in control. Based on the model of the stick insect *Carausius morosus*, the six-legged robot HECTOR was developed in this work to test and evaluate bioinspired controllers. The robot's geometrical layout corresponds to that of the stick insect, scaled up by a factor of 20. Moreover, like the stick insect, the robot features an inherent compliance in its joints. This compliance facilitates walking in uneven terrain since small irregularities can be compensated passively without controller intervention. However, the robot differs from the biological model, e.g., in terms of its size, mass, and mass distribution. Also, it does not possess any means to cling to the ground and therefore must maintain static stability to avoid tilting. To evaluate the ability of stick insects to maintain static stability, experimental data (published by Theunissen et al. (2014)) was examined. It can be shown that stick insects do not maintain static stability at all times. Still, due to their adhesive foot pads, they do not tumble. Therefore, a direct replication of the biological walking controller would not be suitable for the control of HECTOR. In a next step, the bioinspired walking controller WALKNET (Cruse, Kindermann, et al. 1998) was evaluated regarding its applicability for the control of HECTOR. For this purpose, different parametrizations of WALKNET were tested in a simulation environment. For forward walking, parameter sets were found that achieve a high, although not permanent stability. Thus, for the control of HECTOR, which requires continuous stability, a more abstract adaption of the bioinspired coordination had to be found. Based on the original coordination concepts of WALKNET, new coordination mechanisms were developed that incorporate the technical requirements (static stability, angular joint limits, torque constraints, etc.). The ability of the resulting controller to generate insect-like gaits is demonstrated for different walking scenarios in simulation. Moreover, locomotion that is unlikely for insects such as backwards and sidewards walking is shown to be feasible using the novel control approach. At the end of this work the applicability of the approach for the control of the real robot is proved in experiments on visual collision avoidance and basic climbing ability

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

ReaCog, a Minimal Cognitive Controller Based on Recruitment of Reactive Systems

Schilling M, Cruse H. ReaCog, a Minimal Cognitive Controller Based on Recruitment of Reactive Systems. Frontiers in Neurorobotics. 2017;11: 3.It has often been stated that for a neuronal system to become a cognitive one, it has to be large enough. In contrast, we argue that a basic property of a cognitive system, namely the ability to plan ahead, can already be fulfilled by small neuronal systems. As a proof of concept, we propose an artificial neural network, termed reaCog, that, first, is able to deal with a specific domain of behavior (six-legged-walking). Second, we show how a minor expansion of this system enables the system to plan ahead and deploy existing behavioral elements in novel contexts in order to solve current problems. To this end, the system invents new solutions that are not possible for the reactive network. Rather these solutions result from new combinations of given memory elements. This faculty does not rely on a dedicated system being more or less independent of the reactive basis, but results from exploitation of the reactive basis by recruiting the lower-level control structures in a way that motor planning becomes possible as an internal simulation relying on internal representation being grounded in embodied experiences

Control of rhythmic behavior: Central and Peripheral Influences to pattern Generation

Hoinville T, Schilling M, Cruse H. Control of rhythmic behavior: Central and Peripheral Influences to pattern Generation. In: ICRA 2015 CPG Workshop : CPGs for Locomotion Control: Pros, Cons & Alternatives. 2015