Recent advances in evolutionary and bio-inspired adaptive robotics: Exploiting embodied dynamics

This paper explores current developments in evolutionary and bio-inspired approaches to autonomous robotics, concentrating on research from our group at the University of Sussex. These developments are discussed in the context of advances in the wider fields of adaptive and evolutionary approaches to AI and robotics, focusing on the exploitation of embodied dynamics to create behaviour. Four case studies highlight various aspects of such exploitation. The first exploits the dynamical properties of a physical electronic substrate, demonstrating for the first time how component-level analog electronic circuits can be evolved directly in hardware to act as robot controllers. The second develops novel, effective and highly parsimonious navigation methods inspired by the way insects exploit the embodied dynamics of innate behaviours. Combining biological experiments with robotic modeling, it is shown how rapid route learning can be achieved with the aid of navigation-specific visual information that is provided and exploited by the innate behaviours. The third study focuses on the exploitation of neuromechanical chaos in the generation of robust motor behaviours. It is demonstrated how chaotic dynamics can be exploited to power a goal-driven search for desired motor behaviours in embodied systems using a particular control architecture based around neural oscillators. The dynamics are shown to be chaotic at all levels in the system, from the neural to the embodied mechanical. The final study explores the exploitation of the dynamics of brain-body-environment interactions for efficient, agile flapping winged flight. It is shown how a multi-objective evolutionary algorithm can be used to evolved dynamical neural controllers for a simulated flapping wing robot with feathered wings. Results demonstrate robust, stable, agile flight is achieved in the face of random wind gusts by exploiting complex asymmetric dynamics partly enabled by continually changing wing and tail morphologies.

A stunt flying hawk-inspired drone

A winged drone demonstrates aggressive and agile flight by morphing its wings and tail.

Wing shape evolution in bombycoid moths reveals two distinct strategies for maneuverable flight

A wide diversity of wing shapes has evolved, but how is aerodynamic strategy coupled to morphological variation? Here we examine how wing shape has evolved across a phylogenetic split between hawkmoths (Sphingidae) and wild silkmoths (Saturniidae), which have divergent life histories, but agile flight behaviors. Combined with kinematics of exemplar species, we find that these two diverse sister families have evolved two distinct strategies for agile flight. Each group has evolved distinct wing shapes in phylogenetic PCA-space. The notoriously agile hawkmoths have not evolved wing shapes typical of maneuverability, but rather ones that reduce power. Instead their kinematics favor maneuverability, primarily through higher wingbeat frequency. In contrast, silkmoths evolved maneuverable wing shapes and use kinematics that reduce power. Therefore, multiple strategies have evolved to achieve similar aerodynamic performance. We suggest flapping wings provide flexible aerodynamics through kinematics and might release morphological constraints, enabling the diversity of wing shapes across extant flyers.

Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data

Sparse sensor placement is a central challenge in the efficient characterization of complex systems when the cost of acquiring and processing data is high. Leading sparse sensing methods typically exploit either spatial or temporal correlations, but rarely both. This work introduces a sparse sensor optimization that is designed to leverage the rich spatiotemporal coherence exhibited by many systems. Our approach is inspired by the remarkable performance of flying insects, which use a few embedded strain-sensitive neurons to achieve rapid and robust flight control despite large gust disturbances. Specifically, we identify neural-inspired sensors at a few key locations on a flapping wing that are able to detect body rotation. This task is particularly challenging as the rotational twisting mode is three orders of magnitude smaller than the flapping modes. We show that nonlinear filtering in time, built to mimic strain-sensitive neurons, is essential to detect rotation, whereas instantaneous measurements fail. Optimized sparse sensor placement results in efficient classification with approximately 10 sensors, achieving the same accuracy and noise robustness as full measurements consisting of hundreds of sensors. Sparse sensing with neural-inspired encoding establishes an alternative paradigm in hyperefficient, embodied sensing of spatiotemporal data and sheds light on principles of biological sensing for agile flight control.

A Computational Study of Insect Wing Cross-Sectional Geometry on Flight Performance

The influence of cross-sectional geometry on flight performance is investigated for an insect wing using bee-like kinematics. Bee flight is of particular interest due to its mechanical simplicity, utilizing only three degrees of freedom, a high flap frequency, and mechanically linked front and hind wings. These unique flapping flight kinematics result in extremely agile flight characteristics, capable of carrying extraordinary loads relative to the bee’s weight, at a biologically capable efficiency. The performance of a corrugated insect wing and a more intuitively aerodynamic profile are compared computationally. At velocities from 1–3 m/s, the approximated cross-section is foudn to overpredict the lift generated by the corrugated profile by up to 18%. At higher velocities, 4 and 5 m/s, the approximated profile underpredicts the lift generated by the corrugated cross-section by 15%. Based upon this information the cross-sectional geometry of an insect’s wing is significant to the investigation and quantification of insect flight characteristics, for both computational analysis and future robotic applications.

Analysis and Control of a Variable-Pitch Quadrotor for Agile Flight

Fixed-pitch quadrotors are popular research and hobby platforms largely due to their mechanical simplicity relative to other hovering aircraft. This simplicity, however, places fundamental limits on the achievable actuator bandwidth and the possible flight maneuvers. This paper shows that many of these limitations can be overcome by utilizing variable-pitch propellers on a quadrotor. A detailed analysis of the potential benefits of variable-pitch propellers over fixed-pitch propellers for a quadrotor is presented. This analysis is supported with experimental testing to show that variable-pitch propellers, in addition to allowing for generation of reverse thrust, substantially increase the maximum rate of thrust change. A nonlinear, quaternion-based control algorithm for controlling the quadrotor is also presented with an accompanying trajectory generation method that finds polynomial minimum-time paths based on actuator saturation levels. The control law and trajectory generation algorithms are implemented on a custom variable-pitch quadrotor. Several flight tests are shown, which highlight the benefits of a variable-pitch quadrotor over a standard fixed-pitch quadrotor for performing aggressive and aerobatic maneuvers.

Design and Fabrication of a Bio-Inspired Flapping Flight Micro-Air Vehicle

The main objective of the BATMAV project is the development of a biologically-inspired Micro Aerial Vehicle (MAV) with flexible and foldable wings for flapping flight. While flapping flight in MAV has been previously studied and a number of models were realized they usually had unfoldable wings actuated with DC motors and mechanical transmission to provide the flapping motion, a system that brings the disadvantage of a heavy flight platform. This phase of the BATMAV project presents a flight platform that features bat-inspired wings with a number of flexible joints to allow mimicking the kinematics of the real mammalian flyer. The bat was chosen after an extensive analysis of the flight parameters of small birds, bats and large insects characterized by a superior maneuverability and wind gust rejection. Morphological and aerodynamic parameters were collected from existing literature and compared concluding that bat wing present a suitable platform that can be actuated efficiently using artificial muscles. Due to their wing camber variation, the bat species can operate effectively at a large rage of speeds and allow remarkably maneuverable and agile flight. Bat skeleton measurements were taken and modeled in SolidWorks to accurately reproduce bones and body via rapid prototyping machines. Much attention was paid specifically to achieving the comparable strength, elasticity, and range of motion of a naturally occurring bat. Therefore, a desktop model was designed, fabricated and assembled in order to study and optimize the effect of various flapping patterns on thrust and lift forces. As a whole, the BATMAV project consists of four major stages of development: the current phase — design and fabrication of the skeletal structure of the flight platform, selection and testing different materials for the design of a compliant bat-like membrane, analysis of the kinematics and kinetics of bat flight in order to design a biomechanical muscle system for actuation, and design of the electrical control architecture to coordinate the platform flight.