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

Vision for navigation: what can we learn from ants?

The visual systems of all animals are used to provide information that can guide behaviour. In some cases insects demonstrate particularly impressive visually-guided behaviour and then we might reasonably ask how the low-resolution vision and limited neural resources of insects are tuned to particular behavioural strategies. Such questions are of interest to both biologists and to engineers seeking to emulate insectlevel performance with lightweight hardware. One behaviour that insects share with many animals is the use of learnt visual information for navigation. Desert ants, in particular, are expert visual navigators. Across their foraging life, ants can learn long idiosyncratic foraging routes. What's more, these routes are learnt quickly and the visual cues that define them can be implemented for guidance independently of other social or personal information. Here we review the style of visual navigation in solitary foraging ants and consider the physiological mechanisms that underpin it. Our perspective is to consider that robust navigation comes from the optimal interaction between behavioural strategy, visual mechanisms and neural hardware.We consider each of these in turn, highlighting the value of ant-like mechanisms in biomimetic endeavours

The Evolution of Reaction-diffusion Controllers for Minimally Cognitive Agents

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No proof for subjective experience in insects

Cruse H, Schilling M. No proof for subjective experience in insects. Animal Sentience. 2016;1(9): 123

Cooperative Behaviours with Swarm Intelligence in Multirobot Systems for Safety Inspections in Underground Terrains

Underground mining operations are carried out in hazardous environments. To prevent disasters from occurring, as often as they do in underground mines, and to prevent safety routine checkers from disasters during safety inspection checks, multirobots are suggested to do the job of safety inspection rather than human beings and single robots. Multirobots are preferred because the inspection task will be done in the minimum amount of time. This paper proposes a cooperative behaviour for a multirobot system (MRS) to achieve a preentry safety inspection in underground terrains. A hybrid QLACS swarm intelligent model based on Q-Learning (QL) and the Ant Colony System (ACS) was proposed to achieve this cooperative behaviour in MRS. The intelligent model was developed by harnessing the strengths of both QL and ACS algorithms. The ACS optimizes the routes used for each robot while the QL algorithm enhances the cooperation between the autonomous robots. A description of a communicating variation within the QLACS model for cooperative behavioural purposes is presented. The performance of the algorithms in terms of without communication, with communication, computation time, path costs, and the number of robots used was evaluated by using a simulation approach. Simulation results show achieved cooperative behaviour between robots

Collective Construction by Termite-Inspired Robots

Construction usually involves careful preplanning and direct human operation of tools and material. Bringing automation to construction has the potential to improve its speed and efficiency, and to enable building in settings where it is difficult or dangerous for humans to work, e.g., in extraterrestrial environments or disaster areas. Nature provides us with impressive examples of animal construction: in particular, many species of termites build complex mounds several orders of magnitude larger than themselves. Inspired by termites and their building activities, our goal is to develop systems in which large numbers of robots collectively construct human-scale structures autonomously. In this thesis I present TERMES, a system comprised of (1) A high-level control algorithm for decentralized construction of 3D user-specified structures using stigmergy, exploiting implicit rather than explicit communication; and (2) A complete physical implementation where three robots reliably assemble such structures using only local sensing, limited locomotion, and simple control, exploiting embodied rather than explicit intelligence. A major contribution of this work is the translation from abstract models to a real robotic system. I achieved this through careful co-design of algorithms and physical systems and of robots and building material, allowing passive mechanical features to minimize control complexity. To attain reliable performance without relying on costly high-precision sensors and actuators, I put an emphasis on error-tolerant control, making robots able to autonomously detect and recover from small errors. This work advances the aim of engineering collectives of robots that achieve human-specified goals, using biologically-inspired principles for robustness and scalability. While our work is inspired by models of termite construction from the 1970s and 1980s, much is still unknown about how individual termites coordinate and respond to different environmental factors. To address this issue I developed methods and tools to enable high-resolution quantitative data collection on the behavior of individual termites engaged in collective construction in confined experimental arenas. This work advances our ability to study the termites which will hopefully lead to new insights on the design of robust autonomous systems for collective construction.Engineering and Applied Science