Searcharxiv⌕ Search

arXiv · 2610.05008

Min-Max Uniform Circle Formation by Asynchronous Mobile Robots

Abstract

Given a set of point robots $\mathcal{R}$ in the Euclidean plane and a target circle $\mathbf C$ enclosing all robot positions, the \textsc{Min-Max Uniform Circle Formation (MMUCF)} problem requires the robots to move to distinct positions on $\mathbf C$ such that the final configuration forms a regular $n$-gon while minimizing the maximum distance traveled by any robot. Uniform circle formation is a fundamental coordination task in swarm robotics with applications in perimeter monitoring, surveillance, boundary coverage, and pattern formation. The literature does not address the optimization of the maximum individual displacement during the formation process. In this work, we study the min--max versions of the circle formation and uniform circle formation problems, where the goal is to minimize the maximum distance traveled by any robot. We consider these problems under the $\mathcal{ASYNC}$ model, where robots are autonomous, anonymous, identical, homogeneous, oblivious, and silent, and operate under the \textit{Look--Compute--Move} model with non-rigid motion. We first give necessary conditions for a deterministic solution and then present deterministic, distributed, and collision-free algorithms that form a circle and a uniform circle in finite time while minimizing the maximum movement. The algorithms ensure that robots reach distinct positions on the circle and, in the uniform case, equally spaced positions on $\mathbf C$ under the considered model.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Animesh Maiti, Prakhar Shukla, Subhash Bhagat. 2026-10-04. Min-Max Uniform Circle Formation by Asynchronous Mobile Robots. https://arxiv.org/abs/2610.05008

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

DIKTAMO: Extending CXL for Resilience to CPU Failures

Compute Express Link (CXL) 3.0 and beyond allows the compute nodes of a cluster to share data with hardware cache coherence and at the granularity of a cache line. This enables shared-memory semantics for distributed computing, but introduces a new resilience challenge: a node failure leads to the loss of the dirty data in its caches, corrupting application state. Sadly, the CXL specification does not consider processor failures. Moreover, when a component fails, the specification tries to isolate it and continue application execution; there is no attempt to bring the application to a consistent state -- a step required to recover a shared-memory program. To address these limitations, this paper extends CXL to be resilient to node failures, and to correctly recover the application after node failures. We call the system DIKTAMO. To survive node failures, DIKTAMO augments the coherence transaction of a write with messages that propagate the update to a small set of other nodes (i.e., Replicas). Replicas save the update in a local hardware Logging Unit. Replication ensures resilience to node failures. Then, at regular intervals, the Logging Units dump the compressed updates to memory. After a node failure, recovery involves using the logs to bring the directory and memory to a correct state. Our evaluation with 16 4-core nodes shows that DIKTAMO enables fault-tolerant execution with 30% slowdown with 3 replicas (or 27% with 2 replicas) over a platform without fault-tolerance support. DIKTAMO is 2.82x faster than ensuring fault tolerance by using a write-through protocol.

cs.DC↗

EmuGEMM: Fused Tensor Core Kernels for Precision Emulation in Matrix Multiplication

Modern GPUs devote an increasing silicon budget to low-precision matrix-multiplication units, widening the precision-throughput gap for scientific computing workloads. Ozaki Schemes I and II offer an alternative by reconstructing high-precision general matrix multiplication (GEMM) from low-precision operations, yet existing implementations leave substantial performance untapped. In particular, intermediate results are repeatedly materialized in global memory, making data movement the dominant bottleneck. We present EmuGEMM, fused integer Tensor Core kernels for NVIDIA Hopper and Blackwell GPUs that eliminate redundant memory round-trips in both Ozaki schemes. Using Scheme I, EmuGEMM sustains up to 1,639 Top/s on Hopper (83% of INT8 peak) and 3,654 Top/s on Blackwell (81%). For large matrices, EmuGEMM surpasses cuBLAS TF32 throughput by up to 1.4x on Hopper and 1.7x on Blackwell, at comparable accuracy. Using Scheme II, EmuGEMM extends to complex arithmetic and outperforms cuBLAS ZGEMM by up to 2.3x on Hopper and 5.5x on Blackwell.

cs.DC↗

Lightweight and Resource-Efficient Perception for Robotic Guide Dogs

Robotic guide dogs should understand their surroundings, objects, and potential risks. Prior research has focused on raw sensor data from cameras and 2D or 3D LiDAR, which precisely measure distance points rather than provide a semantic understanding of the scene. While these physical measurements are effective for robot-centric collision avoidance and robot safety, they are not suitable for human-centric guidance. The system should recognize the type and relevance of obstacles and explain them, clearly and actionably, in terms of their spatial relation to the user. We present complete on-device perception modules that fuse a 360 camera and a 2D LiDAR for reliable collision avoidance, with moving-object detection and tracking for human-centric guidance. Finally, in walking-impossible situations, a vision--language model delivers pathway explanations as a safety mechanism to reduce user anxiety. In experiments, verification of fused 360 camera--LiDAR depth shows reliable near-range perception but inherent mid-range bias, while the system as a whole sustained real-time performance under 55 W. On the real-world egocentric GuideDogQA benchmark, our system achieved 83.8\% accuracy, compared with 67.1\% for GPT-4o. These results demonstrate that practical human-centric guidance with real-time on-device inference is feasible even on quadrupeds.

cs.DC↗