Strategies for Maintaining Large Robot Communities

The confluence of progress in micro-actuators, power sources, and mixed-signal microelectronics have recently moved swarm robotics and robot communities from simulation to reality. Swarms of 20 to 100 robots are in use already, implementations with several hundred robots are practicable, and communities exceeding a thousand robots are certainly conceivable. Such large robotic communities provide platforms for numerous exciting research directions including collaborative swarms and self-reconfiguring structures. Maintaining hundreds of robots, however, poses significant practical challenges. The literature on strategies for maintaining software and hardware in large robot communities is sparse, even if applicable concepts from wireless sensor-networks are included. Crucial for the viability of any such strategy is its impact on cost per robot. To provide a realistic setting we introduce a robot platform designed to be fabricated in full on standard printed circuit board (PCB) assembly lines. In this context we introduce a framework for on-line testing and calibration based on code pieces, termed plasmids, that migrate among the micro-controllers of the robots. The proposed approach allows the robots access to a larger library of code then what could be stored locally. The robot consists of a single PCB that doubles as chassis and contains no custom mechanical components. Inexpensive motors (mass produced to vibrate mobile phones) are directly soldered to the circuit board and used in direct drive. Our prototypes use a 200 mAh rechargeable lithium polymer battery giving the robot over an hour of autonomy while moving at a speedy 1 m/s. An MSP430F2131 microcontroller controls the robot and communicates with neighbouring robots via infrared light. The simplicity of the design allows the entire robot to be assembled with low-cost PCB manufacturing techniques and is well suited for small-scale mass production of several hundred robots. While this design significantly reduces the current cost barrier to obtaining a robot swarm, it also shifts the attention to the practical problem of maintaining hundreds of robots. Recharging batteries, sieving out robots with worn tyres or accidental damage is one aspect. A second aspect is testing and calibration. It can not be performed in the PCB assembly process and cost considerations prevent proprioceptive sensor. Collaboration among robots to verify performance and provide feedback (e.g., drift direction during a run and return) provide a scalable alternative. A third aspect is the maintenance of software in the robot community. Our plasmid framework addresses all three aspects with a design that is lightweight enough to run on the microcontrollers. Pieces of code and associated attributes (version, target number for redistribution, lifetime, conditions for transmission) are propagated among robots that meet. For example, the code may perform a test on the robot and require to be forwarded to four other robots that have not encountered this test, before it is deleted. Such test plasmids traverse the robot community which, in its collective memory,contains and executes more code than would fit within the program memory of a single device

PRESENCE: A human-inspired architecture for speech-based human-machine interaction

Recent years have seen steady improvements in the quality and performance of speech-based human-machine interaction driven by a significant convergence in the methods and techniques employed. However, the quantity of training data required to improve state-of-the-art systems seems to be growing exponentially and performance appears to be asymptotic to a level that may be inadequate for many real-world applications. This suggests that there may be a fundamental flaw in the underlying architecture of contemporary systems, as well as a failure to capitalize on the combinatorial properties of human spoken language. This paper addresses these issues and presents a novel architecture for speech-based human-machine interaction inspired by recent findings in the neurobiology of living systems. Called PRESENCE-"PREdictive SENsorimotor Control and Emulation" - this new architecture blurs the distinction between the core components of a traditional spoken language dialogue system and instead focuses on a recursive hierarchical feedback control structure. Cooperative and communicative behavior emerges as a by-product of an architecture that is founded on a model of interaction in which the system has in mind the needs and intentions of a user and a user has in mind the needs and intentions of the system

Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment

We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population

Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads.

As metagenomic studies move to increasing numbers of samples, communities like the human gut may benefit more from the assembly of abundant microbes in many samples, rather than the exhaustive assembly of fewer samples. We term this approach leaderboard metagenome sequencing. To explore protocol optimization for leaderboard metagenomics in real samples, we introduce a benchmark of library prep and sequencing using internal references generated by synthetic long-read technology, allowing us to evaluate high-throughput library preparation methods against gold-standard reference genomes derived from the samples themselves. We introduce a low-cost protocol for high-throughput library preparation and sequencing