A bio-inspired sighted robot chases like a hoverfly

Reconfigurable Microlens Array Enables Tunable Imaging Based on Shape Memory Polymers

Microlens arrays (MLAs) with a tunable imaging ability are core components of advanced micro-optical systems. Nevertheless, tunable MLAs generally suffer from high power consumption, an undeformable rigid body, large and complex systems, or limited focal length tunability. The combination of reconfigurable smart materials with MLAs may lead to distinct advantages including programmable deformation, remote manipulation, and multimodal tunability. However, unlike photopolymers that permit flexible structuring, the fabrication of tunable MLAs and compound eyes (CEs) based on transparent smart materials is still rare. In this work, we report reconfigurable MLAs that enable tunable imaging based on shape memory polymers (SMPs). The smart MLAs with closely packed 200 × 200 microlenses (40.0 μm in size) are fabricated via a combined technology that involves wet etching-assisted femtosecond laser direct writing of MLA templates on quartz, soft lithography for MLA duplication using SMPs, and the mechanical heat setting for programmable reconfiguration. By stretching or squeezing the shape memory MLAs at the transition temperature (80 °C), the size, profiles, and spatial distributions of the microlenses can be programmed. When the MLA is stretched from 0 to 120% (area ratio), the focal length is increased from 116 to 283 μm. As a proof of concept, reconfigurable MLAs and a 3D CE with a tunable field of view (FOV, 160-0°) have been demonstrated in which the thermally triggered shape memory deformation has been employed for tunable imaging. The reconfigurable MLAs and CEs with a tunable focal length and adjustable FOV may hold great promise for developing smart micro-optical systems.

Insect-inspired AI for autonomous robots

Autonomous robots are expected to perform a wide range of sophisticated tasks in complex, unknown environments. However, available onboard computing capabilities and algorithms represent a considerable obstacle to reaching higher levels of autonomy, especially as robots get smaller and the end of Moore's law approaches. Here, we argue that inspiration from insect intelligence is a promising alternative to classic methods in robotics for the artificial intelligence (AI) needed for the autonomy of small, mobile robots. The advantage of insect intelligence stems from its resource efficiency (or parsimony) especially in terms of power and mass. First, we discuss the main aspects of insect intelligence underlying this parsimony: embodiment, sensory-motor coordination, and swarming. Then, we take stock of where insect-inspired AI stands as an alternative to other approaches to important robotic tasks such as navigation and identify open challenges on the road to its more widespread adoption. Last, we reflect on the types of processors that are suitable for implementing insect-inspired AI, from more traditional ones such as microcontrollers and field-programmable gate arrays to unconventional neuromorphic processors. We argue that even for neuromorphic processors, one should not simply apply existing AI algorithms but exploit insights from natural insect intelligence to get maximally efficient AI for robot autonomy.

Behavioural and neural responses of crabs show evidence for selective attention in predator avoidance

Selective attention, the ability to focus on a specific stimulus and suppress distractions, plays a fundamental role for animals in many contexts, such as mating, feeding, and predation. Within natural environments, animals are often confronted with multiple stimuli of potential importance. Such a situation significantly complicates the decision-making process and imposes conflicting information on neural systems. In the context of predation, selectively attending to one of multiple threats is one possible solution. However, how animals make such escape decisions is rarely studied. A previous field study on the fiddler crab, Gelasimus dampieri, provided evidence of selective attention in the context of escape decisions. To identify the underlying mechanisms that guide their escape decisions, we measured the crabs' behavioural and neural responses to either a single, or two simultaneously approaching looming stimuli. The two stimuli were either identical or differed in contrast to represent different levels of threat certainty. Although our behavioural data provides some evidence that crabs perceive signals from both stimuli, we show that both the crabs and their looming-sensitive neurons almost exclusively respond to only one of two simultaneous threats. The crabs' body orientation played an important role in their decision about which stimulus to run away from. When faced with two stimuli of differing contrasts, both neurons and crabs were much more likely to respond to the stimulus with the higher contrast. Our data provides evidence that the crabs' looming-sensitive neurons play an important part in the mechanism that drives their selective attention in the context of predation. Our results support previous suggestions that the crabs' escape direction is calculated downstream of their looming-sensitive neurons by means of a population vector of the looming sensitive neuronal ensemble.

A biohybrid fly-robot interface system that performs active collision avoidance

We have designed a bio-hybrid fly-robot interface (FRI) to study sensorimotor control in insects. The FRI consists of a miniaturized recording platform mounted on a two-wheeled robot and is controlled by the neuronal spiking activity of an identified visual interneuron, the blowfly H1-cell. For a given turning radius of the robot, we found a proportional relationship between the spike rate of the H1-cell and the relative distance of the FRI from the patterned wall of an experimental arena. Under closed-loop conditions during oscillatory forward movements biased towards the wall, collision avoidance manoeuvres were triggered whenever the H1-cell spike rate exceeded a certain threshold value. We also investigated the FRI behaviour in corners of the arena. The ultimate goal is to enable autonomous and energy-efficient manoeuvrings of the FRI within arbitrary visual environments.