James A. Hilder

CoCoRo -- The Self-Aware Underwater Swarm

The EU-funded CoCoRo project studies heterogeneous swarms of AUVs used for the purposes of under water monitoring and search. The CoCoRo underwater swarm system will combine bio-inspired motion principles with biologically-derived collective cognition mechanisms to provide a novel robotic system that is scalable, reliable and flexible with respect its behavioural potential. We will investigate and develop swarm-level emergent self-awareness, taking biological inspiration from fish, honeybees, the immune system and neurons. Low-level, local information processing will give rise to collective-level memory and cognition. CoCoRo will develop a novel bio-inspired operating system whose default behaviour will be to provide AUV shoaling functionality and the maintenance of swarm coherence. Collective discrimination of environmental properties will be processed on an individual-or on a collective-level given the cognitive capabilities of the AUVs. We will investigate collective self-recognition through experiments inspired by ethology and psychology, allowing for the quantification of collective cognition.

Use of a multi-objective fitness function to improve cartesian genetic programming circuits

This paper describes an approach of using a multi-objective fitness function to improve the performance of digital circuits evolved using CGP. Circuits are initially evolved for correct functionality using conventional CGP before the NSGA-II algorithm is used to extract circuits which are more efficient in terms of design complexity and delay. This approach is used to evolve typical digital-system building block circuits with results compared to standard-CGP, other evolutionary methods and conventional designs.

Evolving Variability-Tolerant CMOS Designs

As the size of CMOS devices is approaching the atomic level, the increasing intrinsic device variability is leading to higher failure rates in conventional CMOS designs. In this paper, two approaches are proposed for evolving unconventional variability-tolerent CMOS designs: one uses a simple Genetic Algorithm, whilst the other uses Cartesian Genetic Programming. Both approaches successfully evolve unconventional designs for logic gates, whilst an inverter design also shows signs of variability-tolerance.

Towards evolving industry-feasible intrinsic variability tolerant CMOS designs

As the size of CMOS devices is approaching the atomic level, the increasing intrinsic device variability is leading to higher failure rates in conventional CMOS designs. This paper introduces a design tool capable of evolving CMOS topologies using a modified form of Cartesian Genetic Programming and a multi-objective strategy. The effect of intrinsic variability within the design is then analysed using statistically enhanced SPICE models based on 3D-atomistic simulations. The goal is to produce industry-feasible topology designs which are more tolerant to the random fluctuations that will be prevalent in future technology nodes. The results show evolved XOR and XNOR CMOS topologies and compare the impact of threshold voltage variation on the evolved designs with those from a standard cell library.

Chemical Detection Using the Receptor Density Algorithm

This paper describes the application of the receptor density algorithm, an artificial immune system, as used to detect chemicals from data provided by various spectrometers. The system creates chemical signatures which are matched to a library of known chemicals, allowing the positive identification of hazardous substances. The performance of the system is tested against a publicly available mass-spectrometry dataset, against which it has previously been demonstrated as an effective anomaly detection algorithm. An autonomous chemical-detection device is then discussed, in which the algorithm is running on hardware embedded in a Pioneer robot carrying a portable chemical agent monitor.

Optimising variability tolerant standard cell libraries

This paper describes an approach to optimise transistor dimensions within a standard cell library. The goal is to extract high-speed and low-power circuits which are more tolerant to the random fluctuations that will be prevalent in future technology nodes. Using statistically enhanced SPICE models based on 3D-atomistic simulations, a Genetic Algorithm optimises the device widths within a circuit using a multi-objective fitness function. The results show the impact of threshold voltage variation can be reduced by optimising transistor widths, and suggest a similar method could be extended to the optimisation of larger circuits.

Profiling Underwater Swarm Robotic Shoaling Performance Using Simulation

Underwater exploration is important for mapping out the oceans, environmental monitoring, and search and rescue, yet water represents one of the most challenging of operational environments. The CoCoRo project proposes to address these challenges using cognitive swarm intelligent systems. We present here CoCoRoSim, an underwater swarm robotics simulation used in designing underwater swarm robotic systems. Collective coordination of robots represents principle challenge here, and use simulation in evaluating shoaling algorithm performance given the communication, localization and orientation challenges of underwater environments. We find communication to be essential for well-coordinated shoals, and provided communication is possible, inexact localization does not significantly impact performance. As a proof of concept simulation is employed in evaluating shoaling performance in turbulent waters.

Optimizing electronic standard cell libraries for variability tolerance through the nano-CMOS grid

The project Meeting the Design Challenges of nano-CMOS Electronics (http://www.nanocmos.ac.uk) was funded by the Engineering and Physical Sciences Research Council to tackle the challenges facing the electronics industry caused by the decreasing scale of transistor devices, and the inherent variability that this exposes in devices and in the circuits and systems in which they are used. The project has developed a grid-based solution that supports the electronics design process, incorporating usage of large-scale high-performance computing (HPC) resources, data and metadata management and support for fine-grained security to protect commercially sensitive datasets. In this paper, we illustrate how the nano-CMOS (complementary metal oxide semiconductor) grid has been applied to optimize transistor dimensions within a standard cell library. The goal is to extract high-speed and low-power circuits which are more tolerant of the random fluctuations that will be prevalent in future technology nodes. Using statistically enhanced circuit simulation models based on three-dimensional atomistic device simulations, a genetic algorithm is presented that optimizes the device widths within a circuit using a multi-objective fitness function exploiting the nano-CMOS grid. The results show that the impact of threshold voltage variation can be reduced by optimizing transistor widths, and indicate that a similar method could be extended to the optimization of larger circuits.

Parameter optimisation in the receptor density algorithm

In this paper a system which optimises parameter values for the Receptor Density Algorithm (RDA), an algorithm inspired by T-cell signalling, is described. The parameter values are optimised using a genetic algorithm. This system is used to optimise the RDA parameters to obtain the best results when finding anomalies within a large prerecorded dataset, in terms of maximising detection of anomalies and minimising false-positive detections. A trade-off front between the objectives is extracted using NSGA-II as a base for the algorithm. To improve the run-time of the optimisation algorithm with the goal of achieving real-time performance, the system exploits the inherent parallelism of GPGPU programming techniques, making use of the CUDA language and tools developed by NVidia to allow multiple evaluations of a given data set in parallel.

The Pi Swarm: A Low-Cost Platform for Swarm Robotics Research and Education

The paper introduces the Pi Swarm robot, a platform developed to allow research and education in swarm robotics. Motivated by the goals of reducing costs and simplifying the tool-chain and programming knowledge needed to investigate swarming algorithms, we have developed a trackable, sensor-rich and expandable platform which needs only a computer with internet browser and no additional software to program. This paper details the design and use of the robot in a variety of settings, and we feel the platform makes for a viable, low-cost alternative for development of swarm robotic solutions.