The project “A Fleet of Artificial Chemosensing Moths for Distributed Environmental Monitoring” (AMOTH) 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 coordinated 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

• ambient information control systems
• 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:

1. 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.

2. Map the chemical composition of the environmentImplement 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.

4. 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:

1. 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.

2. 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.

3. 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.

4. Insect-Based System for Obstacle Avoidance and Visually Guided Navigation : integrate insect-based control systems for obstacle avoidance, course stabilization, and terrain following.

5. 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.

6. Collective Sampling and Mapping of Chemical Environments : construct a fleet of cUAVs and base station for the efficient and robust mapping of chemical environments.

7. 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.

8. 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.

9. 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 moth.

10. 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.

11. 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 January 2002.) 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. 3.

http://www.neurolab.le.ac.uk/amoth