John Harwell

John Harwell, London Lowmanstone, Maria Gini

Modern intelligent systems researchers employ the scientific method: they form hypotheses about system behavior, and then run experiments using one or more independent variables to test their hypotheses. We present SIERRA, a novel framework structured around that idea for accelerating research developments and improving reproducibility of results. SIERRA makes it easy to quickly specify the independent variable(s) for an experiment, generate experimental inputs, automatically run the experiment, and process the results to generate deliverables such as graphs and videos. SIERRA provides reproducible automation independent of the execution environment (HPC hardware, real robots, etc.) and targeted platform (arbitrary simulator or real robots), enabling exact experiment replication (up to the limit of the execution environment and platform). It employs a deeply modular approach that allows easy customization and extension of automation for the needs of individual researchers, thereby eliminating manual experiment configuration and result processing via throw-away scripts.

John Harwell, Maria Gini

Modern intelligent systems researchers form hypotheses about system behavior and then run experiments using one or more independent variables to test their hypotheses. We present SIERRA, a novel framework structured around that idea for accelerating research development and improving reproducibility of results. SIERRA accelerates research by automating the process of generating executable experiments from queries over independent variables(s), executing experiments, and processing the results to generate deliverables such as graphs and videos. It shifts the paradigm for testing hypotheses from procedural ("Do these steps to answer the query") to declarative ("Here is the query to test--GO!"), reducing the burden on researchers. It employs a modular architecture enabling easy customization and extension for the needs of individual researchers, thereby eliminating manual configuration and processing via throw-away scripts. SIERRA improves reproducibility of research by providing automation independent of the execution environment (HPC hardware, real robots, etc.) and targeted platform (arbitrary simulator or real robots). This enables exact experiment replication, up to the limit of the execution environment and platform, as well as making it easy for researchers to test hypotheses in different computational environments.

John Harwell, Angel Sylvester, Maria Gini

We study the limits of linear modeling of swarm behavior by characterizing the inflection point beyond which linear models of swarm collective behavior break down. The problem we consider is a central place object gathering task. We design a linear model which strives to capture the underlying dynamics of object gathering in robot swarms from first principles, rather than extensively relying on post-hoc model fitting. We evaluate our model with swarms of up to 8,000 robots in simulation, demonstrating that it accurately captures underlying swarm behavioral dynamics when the swarm can be approximated using the mean-field model, and when it cannot, and finite-size effects are present. We further apply our model to swarms exhibiting non-linear behaviors, and show that it still provides accurate predictions in some scenarios, thereby establishing better practical limits on linear modeling of swarm behaviors.

John Harwell, Maria Gini

We present a set of metrics intended to supplement designer intuitions when designing swarm-robotic systems, increase accuracy in extrapolating swarm behavior from algorithmic descriptions and small test experiments, and lead to faster and less costly design cycles. We build on previous works studying self-organizing behaviors in autonomous systems to derive a metric for swarm emergent self-organization. We utilize techniques from high performance computing, time series analysis, and queueing theory to derive metrics for swarm scalability, flexibility to changing external environments, and robustness to internal system stimuli such as sensor and actuator noise and robot failures. We demonstrate the utility of our metrics by analyzing four different control algorithms in two scenarios: an indoor warehouse object transport scenario with static objects and a spatially unconstrained outdoor search and rescue scenario with moving objects. In the spatially constrained warehouse scenario, efficient use of space is key to success so algorithms that use mechanisms for traffic regulation and congestion reduction are the most appropriate. In the search and rescue scenario, the same will happen with algorithms which can cope well with object motion through dynamic task allocation and randomized search trajectories. We show that our intuitions about comparative algorithm performance are well supported by the quantitative results obtained using our metrics, and that our metrics can be synergistically used together to predict collective behaviors based on previous results in some cases.

John Harwell, Maria Gini

When designing swarm-robotic systems, systematic comparison of algorithms from different domains is necessary to determine which is capable of scaling up to handle the target problem size and target operating conditions. We propose a set of quantitative metrics for scalability, flexibility, and emergence which are capable of addressing these needs during the system design process. We demonstrate the applicability of our proposed metrics as a design tool by solving a large object gathering problem in temporally varying operating conditions using iterative hypothesis evaluation. We provide experimental results obtained in simulation for swarms of over 10,000 robots.

Anthony Chen, John Harwell, Maria Gini

Miniaturization and cost, two of the main attractive factors of swarm robotics, have motivated its use as a solution in object collecting tasks, search & rescue missions, and other applications. However, in the current literature only a few papers consider energy allocation efficiency within a swarm. Generally, robots recharge to their maximum level every time unconditionally, and do not incorporate estimates of the energy needed for their next task. In this paper we present an energy efficiency maximization method that minimizes the overall energy cost within a swarm while simultaneously maximizing swarm performance on an object gathering task. The method utilizes dynamic thresholds for upper and lower battery limits. This method has also shown to improve the efficiency of existing energy management methods.

Nathan White, John Harwell, Maria Gini

Localized communication in swarms has been shown to increase swarm effectiveness in some situations by allowing for additional opportunities for cooperation. However, communication and utilization of potentially outdated information is also a concern. We present an explicit non-directional goal-based communication model and message accept/reject scheme, and test our model in a set of object gathering experiments with a swarm of robots. The results of the experiments indicate that even low levels of communication regarding the swarm's goal outperform high levels of random information communication.