Presentation + Paper
13 May 2019 DSOARS: a swarm engineering and verification environment
Author Affiliations +
Abstract
Advances in unmanned systems enable smaller, less expensive platforms that can be deployed in high numbers or swarms providing superior intelligence and overwhelming effects over a widely dispersed battlefield greatly multiplying their effectiveness. But swarming system remain a laboratory curiosity with only a few live demonstrations of limited scope. Swarms are complex and their behavior is difficult to predict requiring skilled engineers and hand-tuning for each mission. Based on 20 years of experience designing swarms for military missions, the Design of Self-Organizing Adaptive Robotic Swarms (DSOARS) is an engineering environment which addresses the three core challenges of swarm design: (1) decomposing mission tasks into the behaviors of the swarm entities, (2) configuring the size of the swarm to a specific mission, and (3) verifying that the resulting swarm behavior consistently achieves the mission goals with a high level of confidence. DSOARS addresses these challenges through two primary innovations: (1) a means to create verified swarm design patterns that decompose high level mission tasks into individual behaviors and (2) a constructive test environment that simultaneously optimizes and characterizes the swarm performance against a range of possible mission conditions. Users with no swarm expertise can specify the requirements and constraints of their mission and DSOARS will configure a swarm that can meet those objectives with performance guarantees. This paper describes the approach and reports experimental results building and configuring a suite of swarm tactics for an urban mission.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
John A. Sauter and Kellen Bixler "DSOARS: a swarm engineering and verification environment", Proc. SPIE 11021, Unmanned Systems Technology XXI, 110210N (13 May 2019); https://doi.org/10.1117/12.2518127
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KEYWORDS
Unmanned aerial vehicles

Data modeling

Sensors

Detection and tracking algorithms

Systems modeling

Optimization (mathematics)

Performance modeling

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