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arXiv 最近完成扫描:2026-11-15 13:55 · 全站最近入库:2026-11-14 15:09各信源进度 →

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版本更新 · v2arXiv:2605.24311

Terrain-Adaptive Grouser Wheel for Optimal Planetary Exploration: Design and Experimental Investigation

Vincent Griffo, Yashwanth Kumar Nakka

原文摘要

Planetary rovers operating in extraterrestrial environments often encounter significant mobility challenges due to varying terrain features such as gradients and granularity. While recent works in multimodal wheel design have explored adjustments in stiffness, compliance, and diameter as a means to improve terrain adaptability, full wheel grouser-adjustable designs remain largely unexplored. Grousers are a compelling feature to actuate, as granular terrains tend to require increased grouser height for improved wheel performance. As a result, we introduce GROVER, a multimodal wheel capable of continuously adjusting its grouser height for terrain adaptation. Fixed-height performance was characterized on vinyl, coarse rock, pea gravel, and sand under two packing states. Across 950 trials, the best configuration varied with terrain, reducing slip by 34.6\%--58.0\% in granular terrains and improving travel time and energy consumption by up to 77.4\%. No single grouser height minimized slip across all terrains, motivating mid-traversal reconfiguration toward lower-slip operation. Two experiments demonstrated this. First, an instructed terrain transition changed grouser height toward a known low-slip configuration during motion, with slip decreasing by $44.0\pm4.7\%$ from the transient peak across 20 trials. Second, an online adaptation routine used closed-loop slip feedback alone to search the grouser-height range for a lower-slip configuration on four terrains with previously uncharacterized granularities: three field terrains and one laboratory regolith terrain. The controller selected different heights, with settled slip 25.4\%--37.2\% lower than the pre-settled mean. Together, these results demonstrate GROVER's ability to improve mobility by varying wheel morphology during traversal toward lower-slip operating states.

cs.RO

新论文 · v1arXiv:2610.40427

socx:基于 JAX 的 GPU 加速机会约束随机最优控制求解器

socx: GPU-Accelerated Chance-Constrained Stochastic Optimal Control using JAX

Tanmay Dokania, Yashwanth Kumar Nakka

AI 速读 基于摘要

作者提出并实现 JAX 版 GPU 加速求解器 socx,用于线性机会约束随机最优控制的提升凸重构,并在四旋翼悬停线性化模型上报告了相对 MOSEK 与 SCS 的显著加速。

eess.SY · cs.RO

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In this article, we present socx, a GPU-accelerated solver for lifted convex reformulations of linear stochastic optimal control under chance constraints, implemented in JAX. We demonstrate that by exploiting the sparse and independent structure of the problem, distributed optimization can reduce it to simpler problems: independent linear-quadratic tracking (LQT) problems and Euclidean projections onto sets. These simpler problems can be solved in parallel and then coupled using the constraints. To validate our solver, we run it on a hover-linearized 3D quadrotor and show a speed-up of up to $98\times$ over MOSEK and up to $2224\times$ over SCS. socx also solves horizons up to $N=1120$, whereas MOSEK runs out of memory at $N=320$. We present ablations to isolate the key components that enable the speed-up.