Spatio-Temporal Reconnection for Multi-Robot Networks using Adaptive Prescribed-Time CBFs

• URL: http://arxiv.org/abs/2606.01526v1
• Date: Mon, 01 Jun 2026 01:18:10 GMT
• Title: Spatio-Temporal Reconnection for Multi-Robot Networks using Adaptive Prescribed-Time CBFs
• Abstract summary: In multi-robot systems, maintaining persistent communication graph connectivity is often overly restrictive.<n>We propose an adaptive prescribed-time control barrier function (adaptive PT-CBF) framework that enables robots to temporarily disconnect and re-enter the communication range.
• Score: 12.425757814316244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In multi-robot systems, maintaining persistent communication graph connectivity is often overly restrictive, especially when robots have limited communication ranges but operate in large environments. Instead, allowing robots to temporarily disconnect and later reconnect is often more desirable for efficient task execution while still ensuring timely information sharing across the team. In this paper, we propose an adaptive prescribed-time control barrier function (adaptive PT-CBF) framework that enables robots to temporarily disconnect and re-enter the communication range within an adjustable and feasible prescribed time. Moreover, we introduce a reconnection triggering mechanism that jointly considers task execution and reconnection urgency, thereby providing a principled way to decide when reconnection should occur. Theoretical analysis justifies convergence to the satisfying reconnection within a prescribed finite time. Experimental results validate the performance of our proposed adaptive PT-CBF with improved task efficiency and satisfying reconnections.

Related papers

• MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks [71.01350912967922]
  In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks.<n>This paper investigates age of information (AoI) minimization for UAV-assisted emergency communications with heterogeneous emergency services.<n>We develop MA-HEAD-Net, an adaptive rule-guided multi-agent deep reinforcement learning framework.
  arXiv  Detail & Related papers  (2026-08-02T10:05:50Z)
• From Real-Time Planning to Reliable Execution:Scalable Coordination for Heterogeneous Multi-Robot Fleets in Industrial Environments [1.4860612474543247]
  This paper presents a reactive online coordination framework that enables real-time planning while maintaining robust execution.<n>We introduce a motion-induced conflict reduction mechanism to support the online generation of feasible paths for online conflict resolution.<n>To mitigate the effects of disturbances, we further design a generalized Conjugate Action-Precedence Hypergraph.
  arXiv  Detail & Related papers  (2026-07-01T08:16:19Z)
• Jamming-Resilient PRB Reservation for Latency-Critical O-RAN Network Slicing [0.8594140167290097]
  In industrial 5G downlink systems, adversarial jamming can abruptly reduce the effective physical resource block capacity.<n>This paper proposes a reserve-based resilience framework for PRB allocation in sliced O-RAN deployments.<n>We formulate reserve activation as a constrained sequential decision problem and design a masked Deep Q-Network to learn effective control policies under non-stationary jamming.
  arXiv  Detail & Related papers  (2026-05-28T22:18:25Z)
• Bringing Network Coding into Multi-Robot Systems: Interplay Study for Autonomous Systems over Wireless Communications [48.38270830044451]
  Communication is a core enabler for multi-robot systems.<n>We show how transport-layer reliability mechanisms that mitigate communication losses and delays shape the autonomy-communication loop.<n>We present two case studies: cooperative localization under delayed and lossy inter-robot communication, and a safety-critical overtaking maneuver.
  arXiv  Detail & Related papers  (2026-03-18T08:26:17Z)
• CollaPipe: Adaptive Segment-Optimized Pipeline Parallelism for Collaborative LLM Training in Heterogeneous Edge Networks [57.95170323315603]
  We introduce CollaPipe, a distributed learning framework that integrates collaborative pipeline parallelism with federated aggregation to support self-evolving networks.<n>In CollaPipe, the encoder part is adaptively partitioned into variable-sized segments and deployed across mobile devices for pipeline-parallel training, while the decoder is deployed on edge servers to handle generative tasks.<n>To enhance training efficiency, we formulate a joint optimization problem that adaptively allocates model segments, micro-batches, bandwidth, and transmission power.
  arXiv  Detail & Related papers  (2025-09-24T07:54:01Z)
• Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learning [31.628735588144096]
  Time-triggered federated learning organizes users into tiers based on fixed time intervals. We apply model pruning to wireless Time-triggered systems and jointly study the problem of optimizing the pruning ratio and bandwidth allocation. Our proposed TT-Prune demonstrates a 40% reduction in communication cost, compared with the asynchronous multi-tier FL without model pruning.
  arXiv  Detail & Related papers  (2024-08-03T12:19:23Z)
• Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control [63.310780486820796]
  We show how a parameterization of recurrent connectivity influences robustness in closed-loop settings. We find that closed-form continuous-time neural networks (CfCs) with fewer parameters can outperform their full-rank, fully-connected counterparts.
  arXiv  Detail & Related papers  (2023-10-05T21:44:18Z)
• Dynamic Scheduling for Federated Edge Learning with Streaming Data [56.91063444859008]
  We consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints. Due to limited communication resources and latency requirements, only a subset of devices is scheduled for participating in the local training process in every iteration.
  arXiv  Detail & Related papers  (2023-05-02T07:41:16Z)
• To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices [13.318419040823088]
  Mobile devices collaborate to train a model based on their own data under the coordination of a central server. Without the central availability of data, computing nodes need to communicate the model updates often to attain convergence. We propose a delay-efficient FL mechanism that reduces the overall time (consisting of both the computation and communication latencies) and communication rounds required for the model to converge.
  arXiv  Detail & Related papers  (2021-11-01T00:35:32Z)
• Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling [57.271494741212166]
  This paper formulates and solves the communication time minimization problem. It is found that the optimized policy gradually turns its priority from suppressing the remaining communication rounds to reducing per-round latency as the training process evolves. The effectiveness of the proposed scheme is demonstrated via a use case on collaborative 3D objective detection in autonomous driving.
  arXiv  Detail & Related papers  (2021-07-24T11:39:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.