Case Studies

Case Study (June 10)

From Compact to Combat: Smart Hinges for Robust Structural Deployment

Led by Evgueni Filipo (UM) & Lorenzo Valdevit (UCI)

Ground vehicle lightweighting is clearly recognized as a critical priority for the Army, and essential for its plans to become more lethal, expeditionary, and agile, with greater capability to conduct decentralized, distributed and integrated operations. The development of advanced functional structures is key to implementing this vision. Among emerging structural components, origami-inspired designs are particularly intriguing: when optimally designed and fabricated, these systems can simultaneously provide structural strength and increased standoff for impact, adaptability to change functionality/performance, deployment for space claim and maneuverability for full vehicle transport, modular design allowing for simple integration and replacement, and acoustic cloaking combined with thermal management. In current origami designs, the tiles of the origami structures are connected by simple hinges, that require manual operation for assembly, including locking at the end of the deployment process. Emerging metamaterial platforms based on negative stiffness concepts can provide inspiration for the design of superior hinges; the rich dynamics of these systems is based on the assembly of highly non-linear springs with multi-stable elastic response. Here, we demonstrate the integration of metamaterial-inspired hinges with deployable and impact-resistant structures. We employ a classic 2D truss structure as a demonstration platform, and use magnetic forces to develop easily manufacturable multi-stable joints with tunable highly non-linear torsional stiffness. This concept can be readily extended to more complex origami designs and opens the door to easily manufacturable deployable and impact-resistant structures with exciting functionalities.

Variation and Validity: Scalable Parameter-Driven Simulation Testing with Autonomous Ground Vehicles

Led by Daniel Carruth (PI, MSU) & Christopher Hudson (Co-PI, MSU)

Current evaluation methods for autonomous ground vehicles (AGVs) often rely on highly specific test setups that improve repeatability across platforms, but limit insight into how systems respond to underlying driving challenges. To address this, we use the Mississippi State University Autonomous Vehicle Simulator (MAVS) to evaluate a Polaris MRZR, Subaru Forester, and Army truck in five test cases: Straight Line Jersey Barrier, Random Obstacle, Slalom, Vegetation Override, and Emergency Braking. To represent real operating conditions, we vary the visual appearance of critical scene elements and scale obstacle spacing and path dimensions relative to vehicle size. These modifications preserve the perceptual, geometric, and traversal challenges, enabling fair comparison across vehicles. Rather than relying on binary pass/fail outcomes, we execute large simulation ensembles with increasing environmental difficulty to identify system limits in perception, traversal, and control stability. Results are integrated into a GitLab CI/CD pipeline for automated analysis and reproducibility. This methodology enables scalable AGV evaluation using statistically grounded performance envelopes, while incorporating realistic scenario variation that maintains equivalence in underlying challenges, ensuring fair comparison and capturing behavior under representative operating conditions.