Joshua P. Hecker

Formica ex machina : ant swarm foraging from physical to virtual and back again

Ants use individual memory and pheromone communication to forage efficiently. We implement these strategies as distributed search algorithms in robotic swarms. Swarms of simple robots are robust, scalable and capable of exploring for resources in unmapped environments. We test the ability of individual robots and teams of three robots to collect tags distributed in random and clustered distributions in simulated and real environments. Teams of three real robots that forage based on individual memory without communication collect RFID tags approximately twice as fast as a single robot using the same strategy. Our simulation system mimics the foraging behaviors of the robots and replicates our results. Simulated swarms of 30 and 100 robots collect tags 8 and 22 times faster than teams of three robots. This work demonstrates the feasibility of programming large robot teams for collective tasks such as retrieval of dispersed resources, mapping, and environmental monitoring. It also lays a foundation for evolving collective search algorithms in silico and then implementing those algorithms in machina in robust and scalable robotic swarms.

Beyond pheromones: evolving error-tolerant, flexible, and scalable ant-inspired robot swarms

For robot swarms to operate outside of the laboratory in complex real-world environments, they require the kind of error tolerance, flexibility, and scalability seen in living systems. While robot swarms are often designed to mimic some aspect of the behavior of social insects or other organisms, no systems have yet addressed all of these capabilities in a single framework. We describe a swarm robotics system that emulates ant behaviors, which govern memory, communication, and movement, as well as an evolutionary process that tailors those behaviors into foraging strategies that maximize performance under varied and complex conditions. The system evolves appropriate solutions to different environmental challenges. Solutions include the following: (1) increased communication when sensed information is reliable and resources to be collected are highly clustered, (2) less communication and more individual memory when cluster sizes are variable, and (3) greater dispersal with increasing swarm size. Analysis of the evolved behaviors reveals the importance of interactions among behaviors, and of the interdependencies between behaviors and environments. The effectiveness of interacting behaviors depends on the uncertainty of sensed information, the resource distribution, and the swarm size. Such interactions could not be manually specified, but are effectively evolved in simulation and transferred to physical robots. This work is the first to demonstrate high-level robot swarm behaviors that can be automatically tuned to produce efficient collective foraging strategies in varied and complex environments.

Evolving Error Tolerance in Biologically-Inspired iAnt Robots

Evolutionary algorithms can adapt the behavior of individuals to maximize the fitness of cooperative multi-agent teams. We use a genetic algorithm (GA) to optimize behavior in a team of simulated robots that mimic foraging ants, then transfer the evolved behaviors into physical iAnt robots. We introduce positional and resource detection error models into our simulation to characterize the empirically-measured sensor error in our physical robots. Physical and simulated robots that live in a world with error and use parameters adapted specifically for an error-prone world perform better than robots in the same error-prone world using parameters adapted for an error-free world. Additionally, teams of robots in error-adapted simulations collect resources at the same rate as the physical robots. Our approach extends state-of-theart biologically-inspired robotics, evolving high-level behaviors that are robust to sensor error and meaningful for phenotypic analysis. This work demonstrates the utility of employing evolutionary methods to optimize the performance of distributed robot teams in unknown environments.

Exploiting clusters for complete resource collection in biologically-inspired robot swarms

The complete collection of resources from a predefined search area is a challenging task for autonomous robot swarms. Because naturally-occurring resources are likely to be distributed in clusters, foraging robot swarms can identify and exploit these resource clusters to improve collection efficiency. We describe an ant-inspired robot swarm foraging system that searches for and collects resources from a variety of distributions, and a cluster prediction and exploitation algorithm that augments swarm foraging by directing robots to residual resources. By characterizing the cumulative resource collection time for a robot swarm foraging in a variety of clustered resource distributions, we can identify the relationship between the “clusteredness” of the distribution and the change in the resource collection rate over time. Experiments show that collection efficiency is most significantly increased when robots switch from ant-inspired foraging to focused exploitation of clusters after approximately 90% of resources have been collected. Not surprisingly, clustering algorithms are most effective when resources are highly clustered in the environment. This work demonstrates the feasibility of efficient, complete resource collection using simple, range-limited robot swarms programmed with ant-inspired foraging behaviors.

An evolutionary approach for robust adaptation of robot behavior to sensor error

Evolutionary algorithms can adapt the behavior of individual agents to maximize the fitness of populations of agents. We use a genetic algorithm (GA) to optimize behavior in a team of simulated robots that mimic foraging ants. We introduce positional and resource detection error models into this simulation, emulating the sensor error characterized by our physical iAnt robot platform. Increased positional error and detection error both decrease resource collection rates. However, they have different effects on GA behavior. Positional error causes the GA to reduce time spent searching for local resources and to reduce the likelihood of returning to locations where resources were previously found. Detection error causes the GA to select for more thorough local searching and a higher likelihood of communicating the location of found resources to other agents via pheromones. Agents that live in a world with error and use parameters evolved specifically for those worlds perform significantly better than agents in the same error-prone world using parameters evolved for an error-free world. This work demonstrates the utility of employing evolutionary methods to adapt robot behaviors that are robust to sensor errors.

From Microbiology to Microcontrollers: Robot Search Patterns Inspired by T Cell Movement

In order to trigger an adaptive immune response, T cells move through lymph nodes searching for dendritic cells that carry antigens indicative of infection. We observe T cell movement in lymph nodes and implement those movement patterns as a search strategy in a team of simulated robots. We find that the distribution of step-sizes taken by T cells are best described

A Multi-agent System Approach to Load-Balancing and Resource Allocation for Distributed Computing

In this research we use a decentralized computing approach to allocate and schedule tasks on a massively distributed grid. Using emergent properties of multi-agent systems, the algorithm dynamically creates and dissociates clusters to serve the changing resource demands of a global task queue. The algorithm is compared to a standard first-in first-out (FIFO) scheduling algorithm. Experiments done on a simulator show that the distributed resource allocation protocol (dRAP) algorithm outperforms the FIFO scheduling algorithm on time to empty queue, average waiting time, and CPU utilization. Such a decentralized computing approach holds promise for massively distributed processing scenarios like SETI@home and Google MapReduce.

Distinguishing Adaptive Search from Random Search in Robots and T cells

In order to trigger an adaptive immune response, T cells move through lymph nodes (LNs) searching for dendritic cells (DCs) that carry antigens indicative of infection. We hypothesize that T cells adapt to cues in the (LN) environment to increase search efficiency. We test this hypothesis by identifying locations that are visited by T cells more frequently than a random model of search would suggest. We then test whether T cells that visit such locations have different movement patterns than other T cells. Our analysis suggests that T cells do adapt their movement in response to cues that may indicate the locations of DC targets. We test the ability of our method to identify frequently visited sites in T cells and in a swarm of simulated iAnt robots evolved to search using a suite of biologically-inspired behaviours. We compare the movement of T cells and robots that repeatedly sample the same locations in space with the movement of agents that do not resample space in order to understand whether repeated sampling alters movement. Our analysis suggests that specific environmental cues can be inferred from the movement of T cells. While the precise identity of these cues remains unknown, comparing adaptive search strategies of robots to the movement patterns of T cells lends insights into search efficiency in both systems.

The MPFA: A multiple-place foraging algorithm for biologically-inspired robot swarms

Finding and retrieving resources in unmapped environments is an important and difficult challenge for robot swarms. Central-place foraging algorithms can be tuned to produce efficient collective strategies for different resource distributions. However, efficiency decreases as swarm size scales up: larger swarms produce more inter-robot collisions and increase competition for resources. We propose a novel extension to central-place foraging in which multiple nests are distributed in the environment. In this multiple-place foraging algorithm, robots depart from a home nest but always return to the nest closest to them. We simulate robot swarms that mimic foraging ants using the multiple-place strategy, employing a genetic algorithm to optimize their behavior in the robot simulator ARGoS. Experiments show that multiple nests produce higher foraging rates and lower average travel time compared to central-place foraging for three different resource distributions. Time spent avoiding robot-robot collisions is not always reduced as was expected, primarily because the use of pheromone-like waypoints leads to more collisions when robots forage for clustered resources. These results demonstrate the importance of careful design in order to create efficient multiple collection points to mitigate the central-place bottleneck for foraging robot swarms.

Immune-inspired search strategies for robot swarms

Detection of targets distributed randomly in space is a task common to both robotic and biological systems. Lévy search has previously been used to characterize T cell search in the immune system. We use a robot swarm to evaluate the effectiveness of a Lévy search strategy and map the relationship between search parameters and target configurations. We show that the fractal dimension of the Lévy search which optimizes search efficiency depends strongly on the distribution of targets but only weakly on the number of agents involved in search. Lévy search can therefore be tuned to the target configuration while also being scalable. Implementing search behaviors observed in T cells in a robot swarm provides an effective, adaptable, and scalable swarm robotic search strategy. Additionally, the adaptability and scalability of Lévy search may explain why Lévy-like movement has been observed in T cells in multiple immunological