Emergence of Diversity in a Group of Identical Bio-Robots

Learning capabilities, often guided by competition/cooperation, play a fundamental and ubiquitous role in living beings. Moreover, several behaviours, such as feeding and courtship, involve environmental exploration and exploitation, including local competition, and lead to a global benefit for the colony. This can be considered as a form of global cooperation, even if the individual agent is not aware of the overall effect. This paper aims to demonstrate that identical biorobots, endowed with simple neural controllers, can evolve diversified behaviours and roles when competing for the same resources in the same arena. These behaviours also produce a benefit in terms of time and energy spent by the whole group. The robots are tasked with a classical foraging task structured through the cyclic activation of resources. The result is that each individual robot, while competing to reach the maximum number of available targets, tends to prefer a specific sequence of subtasks. This indirectly leads to the global result of task partitioning, whereby the cumulative energy spent, in terms of the overall travelled distance and the time needed to complete the task, tends to be minimized. A series of simulation experiments is conducted using different numbers of robots and scenarios: the common emergent result obtained is the role specialization of each robot. The description of the neural controller and the specialization mechanisms are reported in detail and discussed

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

Embodied neuromorphic intelligence

The design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. We discuss why endowing robots with neuromorphic technologies – from perception to motor control – represents a promising approach for the creation of robots which can seamlessly integrate in society. We present initial attempts in this direction, highlight open challenges, and propose actions required to overcome current limitations

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

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

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

Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs

Theoretical and experimental investigations of intra- and inter-segmental control networks and their application to locomotion of insects and crustaceans

Movements of the walking legs in terrestrial animals have to be coordinated continuously in order to produce successful locomotion. Walking is a cyclic process: A single step consists of a stance phase and a swing phase. In the stance phase, the leg muscles provide propulsion of the animal’s body. During the swing phase, the leg is positioned to the starting position of the next stance phase. Sensory input, arising from sensory organs in the legs, modulates the rhythmic motoneuronal activity and therefore the rhythmic activity of the antagonistic muscles pairs in a leg. The coordination of leg joints, and thus of the respective muscle pairs, is called intra-segmental coordination. For coordinated walking not only the proper coordination of one leg is important, but also the coordination of contralateral and ipsilateral legs. The latter is called inter-segmental coordination and also strongly depends on sensory feedback. In this thesis I present three publications (Grabowska et al., 2012; Toth et al., 2013; Grabowska et al., in rev.) and results of an experimental study focusing on different aspects of intra- and inter-segmental coordination. Starting with experimental data on the stick insect Carausius morosus, a well studied model organism for locomotion, I analyzed inter-segmental coordination of legs during walking behavior of stick insects by video analysis. I also performed electrophysiological experiments that provide insight into the inter-segmental connections of different thoracic segments. Furthermore, experimental results were summarized in mathematical models in order to reproduce stick insect locomotion and to provide new hypotheses about so far unknown neuronal controlling processes. First, a study of the walking behavior of the stick insect is introduced (Grabowska et al., 2012). For this purpose, walking sequences of adult animals, walking straight on surfaces with increasing and decreasing slopes, were recorded. Depending on the slope, the animals used different coordination patterns. Subsequent, walking patterns of animals with amputated front, hind or middle legs were analyzed. It became evident that the resulting coordination patterns were regular or maladapted, depending on the amputated leg pairs. We therefore assumed that afferent information from walking front, middle, and hind legs contribute differently to coordination. The second part presents a neuromechanical model that describes starting and stopping of a stick insect leg during walking (Tóth et al., 2013). An existing model of the intra-segmental neuronal network of the stick insect leg was extended by a model of its musculo-skeletal system. The focus of the model was on the neuronal control of slow and fast muscle fiber activity of the three proximal leg muscle groups at start and stop of a leg within a stepping cycle. Using the effects of sensory signals that encode position and velocity of the leg joints like the temporal components of activated muscles during start and stop, observed in experiments, as well as the timing of starting and stopping processes within a step cycle, the simulation results were in good agreement with the observed data of the stick insect. Therefore, this model can be regarded as physiologically relevant and leads to hypotheses about the neuronal control of the musculo-skeletal system that can reveal details of stop and starting in the walking animals. In the third part of this thesis the above mentioned 3-CPG-MN network model, which has been developed based on stick insect data, was extended to serve as a basic module for eight-legged locomotion in walking crustaceans (Grabowska et al., in rev.). For this purpose, the existing 3-CPG-MN network model was extended by an additional segmental module. The basic properties of the 3-CPG-MN network modules remained unchanged. By testing two different network topologies of the new 4-CPG-MN network model, specific walking behavior (coordination patterns, stepping frequency, and transitions) of crustaceans could be replicated by only changing the timing of the inter-segmental excitatory sensory input on the influenced segment. Considering the topology of the 3-CPG-MN network model, namely a caudal-rostral inter-segmental connection connecting every second CPG, the 4-CPG-MN network model was able to reproduce all kinds of walking behavior of forward walking crabs and crayfish. This network stresses the importance of the timing of excitatory signals that are provided by inter-segmental pathways in animals with eight walking legs and four thoracic segments, and proposes possible inter-segmental sensory pathways. Finally, results of experimental data are introduced showing that the rhythm of protractor/retractor central pattern generating networks (thorax-coxa joint) in the prothoracic ganglion can be influenced by a stepping ipsilateral hind leg of the stick insect. This inter-segmental pathway was hypothesized in the 3-CPG-MN network model of Daun-Gruhn and Tóth (2011) for stick insect walking. The experiments showed that a pilocarpine-induced rhythm in the prothoracic protractor and retractor motoneurons could be entrained by an intact forward or backward walking hind leg. In stick insects, this is the evidence for a long range ipsilateral inter-segmental connection that mediates sensory information from a stepping hind leg to the prothoracic CPGs