In this paper, we present a novel robot model of touch, and its representation in an artificial cortex, that aims to capture some of the complexity of human touch. In particular, our approach integrates artificial mechanoception and nociception in an adaptive sensory field (the robot's "sensory body"), allowing for a more comprehensive simulation of tactile sensations. The robot's sensory field is then processed by a biologically plausible neural network in a way akin to sensory processing in the somatosensory and anterior cingulate cortex. Findings from our experimental results show our model's ability to integrate complex data from infrared sensors, leading to the emergence of a spatial sensory body representation in our neural network, with potentially significant implications for robot perception and interaction.
Observations in biologically inspired swarm formations from nature, like flocks of birds, herds of mammals, and packs of wolves, have inspired the innovation of various multi-robotic architectures. This work presents a robotic system that mimics leader-follower behaviors in the navigation and formation of sparse and dense environments. This work extends the original work by Weitzenfeld et al. to evaluate new swarm-based multi-robot architectures with obstacle avoidance and variations in group formations. The multiple robot architecture is based on a wolf pack with a defined 'alpha wolf,' which acts as the leader, and defines'betas wolves,' which act as followers. The 'alpha wolf' leads multiple 'beta wolves' that follow in formation behind the lead wolf, keeping track of a group member and maintaining a set angle and distance while performing obstacle avoidance, staying in formation, and performing speed adjustment. Variations in swarm formation behaviors being analyzed with robots include (1) beta robots following the alpha robot, (2) beta robots following the closest neighboring robot, and (3) robots following the same robot identified since the beginning. Experiments are performed in simulation, using Webots, to analyze robot formations.
The collective escape of predators by prey is a classic example of adaptive behavior in animal groups. Across species, prey has evolved a large repertoire of individual evasive maneuvers they can use to evade predators. With recent technological advances, more empirical data of collective escape is becoming available, and a large variation in the collective dynamics of different species is apparent. However, given the complexity of patterns of collective escape, we are still lacking the tools to understand their emergence. Computational models that can link rules of individual behavior to patterns of collective escape are needed, but species-specific motion and escape characteristics that will allow the link between behavior and eco-evolutionary dynamics of a given species are rarely included in agent-based models of collective behavior. Here, to tackle this challenge, we introduce a framework that uses individual-based state machines to model spatio-temporal dynamics of collective escape. A synthetic agent in our framework can switch its behavior between 'flocking' with different coordination specifics (e.g., quicker interactions when vigilant) and 'escape' with various maneuvers through a dynamic Markov-chain, depending on its local information (e.g., its relative position to the predator). A user can compose a new agent-based model adjusted to empirical data by choosing a set of states (which includes rules of motion, interaction, and escape), their temporal order, and a detailed parameterization. The flexibility and structure of our software allows substantial changes in a model with very minimal code alterations, showing great potential for future use to identify the underlying mechanisms of collective escape across species and ecological contexts.
In collective biological systems, social contagion processes play an important role in evaluating and processing information on the level of the collective. These group-level abilities typically arise from individual-level mechanisms and through local interactions. We are interested in the role of these mechanisms and their effect on the system's response to environmental inputs. In this paper, we present a spatially embedded network model that is inspired by large fish shoals performing collective action in response to predation. We compare the observations of spatio-temporal dynamics in the model simulations with empirical observations, studying specifically the effect of spatial heterogeneities on system activity. The model demonstrates how already simple mechanisms suffice to represent key characteristics of the study system, and highlights the importance of taking into account the spatial embedding for understanding group-level processes in animal collectives.
With the rise of robot swarms, it has become a relevant problem how humans can control them. Extended swarming is a potential approach in which robot swarms are treated as self-organising extensions of human bodies. Swarm control takes the form of controlling the observable swarm body while robot chains connect the human operator to relevant aspects of the environment. Inspired by how natural bodies are controlled by a nervous system, we here investigate how the swarm body's self-organisation can be influenced by robot chains acting as embodied neural traces while remaining under human high-level control. Three design principles are proposed for such embodied neural computation. First, the swarm body's self-organisation is controlled both by top-down human control and bottom-up sensor inputs alike to the hierarchical control architecture of the nervous system. Second, robots participating in robot chains are treated as rate-coded neurons rendering the chains as embodied neural traces which offers intuitive control possibilities for the human. Third, neural and swarm self-organisation are integrated by utilizing the swarm's communication network as a scaffolding for neural function influencing swarm dynamics. This process is interpreted as embodied Hebbian learning. Human control over the swarm is demonstrated in a grid-based search-and-rescue simulation with the objective of selecting the most valuable subregion defined by accumulated victims in need. We evaluate how using embodied trace relevance in terms of neural activation improves completion time to finding the highest-value trace as well as how attracting units to relevant traces increases their robustness.
