We will develop a novel technology for efficiently identifying, localising,
and mapping complex chemical information within uncertain and realistic chemosensory
environments. Unmanned Aerial Vehicle (UAV) technology can be applied to this
problem providing adequate sensor and control technology. Our approach will
exploit the collective behaviour of autonomous moth-based Chemosensory UAVs
(cUAVs), the sensory and control information processing subsystems of which
will be based exclusively on models of information processing in insect olfaction.
Each individual cUAV will be capable of autonomous behaviour including exploration,
obstacle avoidance, and foraging. The cUAV will be equipped with chemical and
visual sensors and will autonomously navigate, react to environmental stimuli,
and assess the chemical composition of its environment. This development continues
our research in artificial and biological olfaction, sensory processing and
analysis, neuronal models of learning, real-time behavioural control, and robotics.
These cUAV artefacts will be co-ordinated for robust exploration, search, and
identification behaviour based upon chemical cues. The effective mapping of
the chemosensory environment will be achieved through the collective behaviour
of a fleet of these agents co-ordinated from a centralised ground station. Fleets
of cUAVs will be deployed to sense and map the airborne chemical composition
of large-scale environments. Although we will demonstrate this approach for
environmental monitoring, as a combined system for highly robust and efficient
chemical/biochemical exploration and localisation this technology also has enormous
potential for application in
- Military Intelligence (MI)/Battlefield Information Control Systems (BICS),
- pollution treatment
- unexploded ordnance and mine localisation,
- search and rescue,
- safety monitoring,
- food/energy source localisation,
- medical diagnosis/treatment when combined with nanotechnology,
- unmanned space exploration.
High level objectives for this project are:
- Develop a Chemosensory UAV that uses onboard chemical and visual
sensors to autonomously navigate outdoors. The cUAV's mission is to identify
volatile compounds and locate their sources.
- Map the chemical composition of the environment using a new class
of chemical sensors and information processing technologies designed for:
a. Measurement of chemical concentration,
b. Classification of chemical composition,
c. Automatic sensor recalibration.
- Implement mechanisms and models of adaptive sensory classification, sensory-motor
integration, and action selection. These technologies are based on our
investigation of insect strategies of sensory processing and control and their
application to robots.
- Deploy a fleet of cUAVs to collectively solve the task of mapping a
chemosensory environment. The main components of our cUAV will be chemical
sensor arrays complete with a wide range of broadly tuned chemosensors (supplied
by Alpha MOS SA, France) adapted from a separate EU RTD project, antennal
lobe model for encoding the chemosensory stimulus (University of Leicester,
UK), distributed adaptive control (DAC) subsystem, motor subsystem, visual
system, and mechatronics to drive the device (ETH, Switzerland).
Specific objectives relating to the work programme are:
- Build a Chemo-sensing cUAV: develop an cUAV that uses on-board chemical
and visual sensors to autonomously navigate outdoors. The cUAV's mission is
to identify volatile compounds and locate their sources in complex indoor
and outdoor environments.
- Odour Based Navigation: demonstrate an ability to conduct chemotaxis
behaviour in steady-state odour concentration gradients and complex turbulent
odour plumes in indoor and outdoor environments.
- Learning within a Realistic Chemo-sensory Environment: demonstrate
an ability to discriminate between complex odour blends during navigation
and learn odour cues as a result of behavioural conditioning.
- Insect Based System for Obstacle Avoidance and Visually Guided Navigation:
integrate insect based control systems for obstacle avoidance, course stabilization,
and terrain following.
- Learning of Behavioral Sequences Applied to Active Sampling of Chemosensors:
apply for the first time a neuronal model of sequence learning to the cUAV's
task in order to learn optimal behavioural patterns for exploration and sampling.
- Collective Sampling and Mapping of Chemical Environments: construct
a fleet of cUAVs and base station for the efficient and robust mapping of
- Sensory Encoding Optimisation with Learning: to achieve in our cUAV
an ability to adapt to salient odour stimuli through optimisation of sensory
encoding at the level of the antennal lobe.
- Fusion of Sensory Data: to achieve in our cUAV navigation by integrating
sensory data from multiple modalities. This objective provides the main interface
to other projects within the EU-FET Neuroinformatics proactive intiative.
- Localised Adaptation to Compensate for Changing Sensor Characteristics:
to achieve an ability for compensation within our cUAV to changes in chemical
sensor characteristics over time (temporal drift), by adopting a convergent
front-end architecture as used in the biological olfactory pathway of the
- Odour Intensity and Odour Quality Discrimination: to achieve an
ability in our cUAV to separate odour concentration (intensity) and odour
quality in real-time. Biological olfactory systems are adept at distinguishing
between odour quality and intensity. Such a property would be of great benefit
to machine olfaction applications.
- Hyperacuity and Sensitivity Enhancement: by understanding principles
of hyperacuity in the olfactory pathway, implement a chemosensory system that
can demonstrate higher overall system sensitivity to stimuli than provided
by the sensitivity of individual sensing elements.
This project has been funded by the EU-IST-FET
Programme under the Fifth Framework (AMOTH IST-2001-33066 -- start date
Tim C. Pearce, University
of Leicester, UK (Antennal lobe modelling and project co-ordinator)
Paul F.M.J. Verschure,
ETH, Switzerland (UAV development and sequence-based learning)
Hansson, Sveriges Lantbruksuniversitet, Sweden (Electrophysiology and behavioural
Alpha MOS, Toulouse, France (Chemical
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Results will be posted here as they become available ...
Author: Tim Pearce , last updated 18th
Any opinions and views expressed are the author's and not
those of the University