Researcher profile

Yangang Liang

Yangang Liang contributes to research discovery and scholarly infrastructure.

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Published work

2 published item(s)

preprint2026arXiv

An Analytic Solution to the Optimal Spherical Dubins Path Problem with Geodesic Curvature Constraints

Computing shortest paths for curvature-constrained Dubins vehicles on the unit sphere is fundamental to many engineering applications, including long-range flight planning, persistent surveillance patterns, and global routing problems where great circles are natural routes. Numerical optimization methods on $\SO(3)$ suffer from sensitivity to initialization, may converge to local minima, and often miss feasible solution branches. This paper proposes a unified analytic computational approach for spherical Dubins CGC and CCC paths that overcomes these limitations. By exploiting the axis-fixing property of rotations and developing a closed-form back-substitution method using geometric projection, the three-dimensional boundary value problem is reduced to solving a quadratic polynomial equation. The proposed analytic solver achieves machine precision accuracy with errors on the order of $10^{-16}$, is approximately $717$ times faster than numerical methods under the same computational environment, and systematically enumerates all feasible solution branches without requiring exhaustive multi-start initialization. The method provides closed-form solutions for optimal path computation in the regime where turning radius $\Rturn \in (0, 1/2]$, corresponding to $U_{\max} \geq \sqrt{3}$.

preprint2026arXiv

Born-Qualified: An Autonomous Framework for Deploying Advanced Energy and Electronic Materials

Autonomous science is transforming how we discover materials and chemical systems for advanced energy technologies. However, many initially promising systems never reach deployment. This "valley of death" stems from optimization that prioritizes laboratory metrics over industrial viability. We propose a new strategy: "born-qualified" autonomous development, which embeds manufacturability, cost, and durability constraints from the outset. This approach is enabled by four pillars, including the development of multi-objective metrics, causal models, a modular infrastructure, and embedding manufacturing in the discovery loop. Realizing this vision will require sustained, community-wide commitment, but the potential return on that investment is commensurate with the scale of the challenge.