Klinotaxis as a basic form of navigation

On motion camouflage as proportional navigation

Motion camouflage is a stealth behaviour by which an insect can appear stationary at a fixed point while approaching or escaping another moving insect. Although several approaches have been proposed to generate motion camouflage in simulated and real agents, the exact mechanisms insects use to perform this complex behaviour are not well understood, especially considering their limited perceptual and computational resources. This paper sheds light on the possible underlying control mechanisms insect might use to generate motion camouflage, by training and analysing a series of motion camouflage controllers using reinforcement learning. We first investigate through simulations the most relevant information available to the insect that can be used to perform motion camouflage and analyse the learnt controllers. The results of this analysis drove us to hypothesise two simpler control mechanisms which, we show, can also generate motion camouflage. The proposed controllers are an extension of proportional navigation, another interception technique found in nature, and therefore, both animal behaviours seem to be connected. Motion camouflage can lead, among others, to novel approaches to closely observe animals in the wild, record sports events or gather information in military operations without being noticed.

Olfactory navigation in the real world: Simple local search strategies for turbulent environments

Olfaction informs animal navigation for foraging, social interaction, and threat evasion. However, turbulent flow on the spatial scales of most animal navigation leads to intermittent odor information and presents a challenge to simple gradient-ascent navigation. Here we present two strategies for iterative gradient estimation and navigation via olfactory cues in 2D space: tropotaxis, spatial concentration comparison (i.e., instantaneous comparison between lateral olfactory sensors on a navigating animal) and klinotaxis, spatiotemporal concentration comparison (i.e., comparison between two subsequent concentration samples as the animal moves through space). We then construct a hybrid model that uses klinotaxis but utilizes tropotactic information to guide its spatial sampling strategy. We find that for certain body geometries in which bilateral sensors are closely-spaced (e.g., mammalian nares), klinotaxis outperforms tropotaxis; for widely-spaced sensors (e.g., arthropod antennae), tropotaxis outperforms klinotaxis. We find that both navigation strategies perform well on smooth odor gradients and are robust against noisy gradients represented by stochastic odor models and real turbulent flow data. In some parameter regimes, the hybrid model outperforms klinotaxis alone, but not tropotaxis.

Getting closer to the goal by being less capable

Understanding how systems with many semi-autonomous parts reach a desired target is a key question in biology (e.g., Drosophila larvae seeking food), engineering (e.g., driverless navigation), medicine (e.g., reliable movement for brain-damaged individuals), and socioeconomics (e.g., bottom-up goal-driven human organizations). Centralized systems perform better with better components. Here, we show, by contrast, that a decentralized entity is more efficient at reaching a target when its components are less capable. Our findings reproduce experimental results for a living organism, predict that autonomous vehicles may perform better with simpler components, offer a fresh explanation for why biological evolution jumped from decentralized to centralized design, suggest how efficient movement might be achieved despite damaged centralized function, and provide a formula predicting the optimum capability of a system's components so that it comes as close as possible to its target or goal.