Insect-inspired visual navigation on-board an autonomous robot: real-world routes encoded in a single layer network

Insect-Inspired models of visual navigation, that operate by scanning for familiar views of the world, have been shown to be capable of robust route navigation in simulation. These familiarity-based navigation algorithms operate by training an artificial neural network (ANN) with views from a training route, so that it can then output a familiarity score for any new view. In this paper we show that such an algorithm – with all computation performed on a small low-power robot – is capable of delivering reliable direction information along real-world outdoor routes, even when scenes contain few local landmarks and have high-levels of noise (from variable lighting and terrain). Indeed, routes can be precisely recapitulated and we show that the required computation and storage does not increase with the number of training views. Thus the ANN provides a compact representation of the knowledge needed to traverse a route. In fact, rather than losing information, there are instances where the use of an ANN ameliorates the problems of sub optimal paths caused by tortuous training routes. Our results suggest the feasibility of familiarity-based navigation for long-range autonomous visual homing

Robust view based navigation through view classification

Current implementations of view-based navigation on robots have shown success, but are limited to routes of <10m [1] [2]. This is in part because current strategies do not take into account whether a view has been correctly recognised, moving in the most familiar direction given by the rotational familiarity function (RFF) regardless of prediction confidence. We demonstrate that it is possible to use the shape of the RFF to classify if the current view is from a known position, and thus likely to provide valid navigational information, or from a position which is unknown, aliased or occluded and therefore likely to result in erroneous movement. Our model could classify these four view types with accuracies of 1.00, 0.91, 0.97 and 0.87 respectively. We hope to use these results to extend online view-based navigation and prevent robot loss in complex environments

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

Stanmer Park outdoor navigational data

This dataset contains omnidirectional 1440✕1440 resolution images taken using a Kodak Pixpro SP360 camera paired with RTK GPS information obtained using a simple RTK2B - 4G NTRIP kit and fused yaw, pitch and roll data recorded from a BNO055 IMU. The data was collected using a 4 wheel ground robot that was manually controlled by a human operator. The robot was driven 15 times along a route at Stanmer Park (shown in map.png). The route consists mostly of open fields and a narrow path through a forest and is approximately 700m long. The recordings took place at various days and times starting in March 2021, with the date and time indicated by the filename. For example ‘20210420_135721.zip’ corresponds to a route driven on 20/03/2021 starting at 13:57:21 GMT. During the recordings the weather varied from clear skies and sunny days to overcast and low light conditions. Each recording consists of an mp4 video of the camera footage for the route, and a database_entries.csv file with the following columns:Timestamp of video frame (in ms)X, Y and Z coordinate (in mm) and zone representing location in UTM coordinates from GPSHeading, pitch and roll (in degrees) from IMU. In some early routes, the IMU failed and when this occurs these values are recorded as “NaN”.Speed and Steering angle commands being sent to robot at that timeGPS quality (1=GPS, 2=DGNSS, 4=RTK Fixed and 5=RTK Float)X, Y and Z coordinates (in mm) fitted to a degree one polynomial to smooth out GPS noiseHeading (in degrees) derived from smoothed GPS coordinatesIMU heading (in degrees) with discontinuities resulting from IMU issues fixedFor completeness, each folder also contains a database_entries_original.csv containing the data before pre-processing. The pre-processing is documented in more detail in pre_processing_notes.pdf.</p