arXiv subjects

Brendan Long

Publications and source records attributed to Brendan Long.

4 recordsLinked to original sources

arXiv2026

Real-Time Optical Communication Using Event-Based Vision with Moving Transmitters

In multi-robot systems, traditional radio frequency (RF) communication struggles with contention and jamming. Optical communication offers a strong alternative. However, conventional frame-based cameras suffer from limited frame rates, motion blur, and reduced robustness under high dynamic range lighting. Event cameras support microsecond temporal resolution and high dynamic range, making them extremely sensitive to scene changes under fast relative motion with an optical transmitter. Leveraging these strengths, we develop a complete optical communication system capable of tracking moving transmitters and decoding messages in real time. Our system achieves over $95\%$ decoding accuracy for text transmission during motion by implementing a Geometry-Aware Unscented Kalman Filter (GA-UKF), achieving 7x faster processing speed compared to the previous state-of-the-art method, while maintaining equivalent tracking accuracy at transmitting frequencies $\geq$ 1 kHz.

cs.RO↗

arXiv2026

Distributed Control Barrier Functions for Safe Multi-Vehicle Navigation in Heterogeneous USV Fleets

Collision avoidance in heterogeneous fleets of uncrewed vessels is challenging because the decision-making processes and controllers often differ between platforms, and it is further complicated by the limitations on sharing trajectories and control values in real-time. This paper presents a pragmatic approach that addresses these issues by adding a control filter on each autonomous vehicle that assumes worst-case behavior from other contacts, including crewed vessels. This distributed safety control filter is developed using control barrier function (CBF) theory and the application is clearly described to ensure explainability of these safety-critical methods. This work compares the worst-case CBF approach with a Collision Regulations (COLREGS) behavior-based approach in simulated encounters. Real-world experiments with three different uncrewed vessels and a human operated vessel were performed to confirm the approach is effective across a range of platforms and is robust to uncooperative behavior from human operators. Results show that combining both CBF methods and COLREGS behaviors achieves the best safety and efficiency.

cs.RO↗

arXiv2025

Domain-Aware Machine Learning for Accelerating MILP-Based Motion Planning with Temporal Logic and Chance Constraints

Motion-planning problems with temporal-logic or chance constraints are often encoded as mixed-integer linear programs (MILPs). Although these encodings provide rigorous specifications, their combinatorial structure can make planning prohibitively slow. Machine learning for combinatorial optimization (ML4CO) has accelerated general-purpose MILP solving, but its standard graph representations discard semantic information available in control problems, such as variable roles, time indices, sample identities, and formula structure. We introduce a domain-aware ML4CO framework for MILP-based motion planning with temporal logic and chance constraints. The framework augments a conventional variable--constraint bipartite graph with features derived from the planning formulation and uses the resulting representation for two solver-guidance tasks: selecting branching backdoors and configuring solver parameters. We study three domains---Signal Temporal Logic (STL) planning, chance-constrained planning through Conformal Predictive Programming (CPP), and multi-agent Capability Temporal Logic (CaTL) planning---and compare against solver defaults, domain-agnostic learned methods, non-learned branching rules, MCTS transfer, and SMAC3 transfer. Across the tested distributions, domain-aware backdoor selection has the lowest reported mean solve time in all three domains, improving on default Gurobi by 14.4--20.4%. Domain-aware configuration also has the lowest mean primal gap and primal integral in all three domains under a fixed SCIP time limit. These results show that exposing control-specific structure can improve learned MILP guidance beyond generic optimization features.

eess.SY↗

arXiv2025

PRISM: Complete Online Decentralized Multi-Agent Pathfinding with Rapid Information Sharing using Motion Constraints

We introduce PRISM (Pathfinding with Rapid Information Sharing using Motion Constraints), a decentralized algorithm designed to address the multi-task multi-agent pathfinding (MT-MAPF) problem. PRISM enables large teams of agents to concurrently plan safe and efficient paths for multiple tasks while avoiding collisions. It employs a rapid communication strategy that uses information packets to exchange motion constraint information, enhancing cooperative pathfinding and situational awareness, even in scenarios without direct communication. We prove that PRISM resolves and avoids all deadlock scenarios when possible, a critical challenge in decentralized pathfinding. Empirically, we evaluate PRISM across five environments and 25 random scenarios, benchmarking it against the centralized Conflict-Based Search (CBS) and the decentralized Token Passing with Task Swaps (TPTS) algorithms. PRISM demonstrates scalability and solution quality, supporting 3.4 times more agents than CBS and handling up to 2.5 times more tasks in narrow passage environments than TPTS. Additionally, PRISM matches CBS in solution quality while achieving faster computation times, even under low-connectivity conditions. Its decentralized design reduces the computational burden on individual agents, making it scalable for large environments. These results confirm PRISM's robustness, scalability, and effectiveness in complex and dynamic pathfinding scenarios.

cs.RO↗