arXiv subjects

Shuhao Qi

Publications and source records attributed to Shuhao Qi.

8 recordsLinked to original sources

arXiv2026

Opinion-Guided Layered Strategies for Decentralized Coordination

Autonomous agents increasingly interact with other independent agents, and such interactions typically admit multiple joint behaviors. When two agents prefer different ones, their independent strategies may be mutually incompatible and fail to reach a coordinated outcome; when they are identical, neither can differentiate its role when needed. Ideally, an agent should coordinate with any agent it encounters, regardless of which admissible joint behavior that agent aims to realize. We therefore propose a new form of strategy, the opinion-guided strategy, which keeps all the admissible joint behaviors available and postpones the selection to execution time, when the other agent's behavior reveals which one to realize. To realize this, nonlinear opinion dynamics are leveraged in a layered realization to guide the agent to a common admissible joint behavior in response to the other agent's evolving behavior, even without communication. We formally establish the conditions under which the strategy remains robust to every preference the other agent may hold. This robustness has an important implication: two agents running identical strategies can break symmetry when needed, a capability that conventional strategies lack. Three case studies across different applications show that the opinion-guided strategy coordinates with every randomly encountered agent, as long as it is willing to realize one of the admissible joint behaviors. One of them corresponds to a general-sum game: unlike conventional approaches devoted to finding a unique Nash equilibrium in advance, the opinion-guided strategy keeps every equilibrium open and guarantees the agents reach one, decided by their runtime interaction.

cs.MA↗

arXiv2026

Autonomous Driving with Priority-Ordered STL Specifications Under Multimodal Uncertainty

Autonomous vehicles must plan trajectories that satisfy multiple requirements, such as safety, traffic-rule compliance, and passenger comfort. However, in safety-critical scenarios, it is not always possible to satisfy all requirements simultaneously, necessitating their prioritization based on importance. At the same time, the uncertainty in the predicted trajectories of surrounding road users, such as other vehicles and pedestrians, must be explicitly accounted for. In this work, we propose an uncertainty-aware trajectory planning framework that incorporates a predefined priority ordering over Signal Temporal Logic (STL) specifications and preserves the induced lexicographic ordering under multimodal uncertainty. We implement this formulation with Model Predictive Path Integral (MPPI) control and demonstrate the effectiveness of our method on simulation scenarios, showing that our framework efficiently handles conflicting objectives under realistic multimodal uncertainty.

cs.RO↗

arXiv2025

Situation-Aware Interactive MPC Switching for Autonomous Driving

Autonomous driving in interactive traffic scenarios remains challenging because of the mutual influence among vehicles and the inherent uncertainty of surrounding agents. Several model predictive control (MPC) formulations have been proposed to address this challenge, each adopting a different model of inter-agent interaction. While higher-fidelity interaction models enable more intelligent behavior, they incur substantially greater computational cost. Since strong interactions arise only occasionally in real traffic, a practical strategy for balancing performance and computational overhead is to invoke an appropriate controller based on situational demands. To this end, we first conduct a comparative study to assess and hierarchize the interactive capabilities of different MPC formulations. Building on this hierarchy, we then develop a neural network-based classifier for situation-aware switching among these controllers. We demonstrate that, by invoking the most advanced interactive MPC only in rare but critical situations and relying on a basic MPC in the majority of situations, situation-aware switching substantially improves overall performance while significantly reducing computational load.

cs.RO↗

arXiv2025

Integrating Opinion Dynamics into Safety Control for Decentralized Airplane Encounter Resolution

As the airspace becomes increasingly congested, decentralized conflict resolution methods for airplane encounters have become essential. While decentralized safety controllers can prevent dangerous midair collisions, they do not always ensure prompt conflict resolution. As a result, airplane progress may be blocked for extended periods in certain situations. To address this blocking phenomenon, this paper proposes integrating bio-inspired nonlinear opinion dynamics into the airplane safety control framework, thereby guaranteeing both safety and blocking-free resolution. In particular, opinion dynamics enable the safety controller to achieve collaborative decision-making for blocking resolution and facilitate rapid, safe coordination without relying on communication or preset rules. Extensive simulation results validate the improved flight efficiency and safety guarantees. This study provides practical insights into the design of autonomous controllers for airplanes.

eess.SY↗

arXiv2024

Avoiding Deadlocks Is Not Enough: Analysis and Resolution of Blocked Airplanes

This paper is devoted to the analysis and resolution of a pathological phenomenon in airplane encounters called blocking mode. As autonomy in airplane systems increases, a pathological phenomenon can be observed in two-aircraft encounter scenarios, where airplanes stick together and fly in parallel for an extended period. This parallel flight results in a temporary blocking that significantly delays progress. In contrast to widely studied deadlocks in multi-robot systems, such transient blocking is often overlooked in existing literature. Since such prolonged parallel flying places high-speed airplanes at elevated risks of near-miss collisions, encounter conflicts must be resolved as quickly as possible in the context of aviation. We develop a mathematical model for a two-airplane encounter system that replicates this blocking phenomenon. Using this model, we analyze the conditions under which blocking occurs, quantify the duration of the blocking period, and demonstrate that the blocking condition is significantly less restrictive than that of deadlock. Based on these analytical insights, we propose an intention-aware strategy with an adaptive priority mechanism that enables efficient resolution of ongoing blocking phenomena while also incidentally eliminating deadlocks. Notably, the developed strategy does not rely on central coordination and communications that can be unreliable in harsh situations. The analytical findings and the proposed resolution strategy are validated through extensive simulations.

eess.SY↗

arXiv2024

Risk-Aware Autonomous Driving with Linear Temporal Logic Specifications

Human drivers naturally balance the risks of different concerns while driving, including traffic rule violations, minor accidents, and fatalities. However, achieving the same behavior in autonomous driving systems remains an open problem. This paper extends a risk metric that has been verified in human-like driving studies to encompass more complex driving scenarios specified by linear temporal logic (LTL) that go beyond just collision risks. This extension incorporates the timing and severity of events into LTL specifications, thereby reflecting a human-like risk awareness. Without sacrificing expressivity for traffic rules, we adopt LTL specifications composed of safety and co-safety formulas, allowing the control synthesis problem to be reformulated as a reachability problem. By leveraging occupation measures, we further formulate a linear programming (LP) problem for this LTL-based risk metric. Consequently, the synthesized policy balances different types of driving risks, including both collision risks and traffic rule violations. The effectiveness of the proposed approach is validated by three typical traffic scenarios in Carla simulator.

eess.SY↗

arXiv2024

VernaCopter: Disambiguated Natural-Language-Driven Robot via Formal Specifications

It has been an ambition of many to control a robot for a complex task using natural language (NL). The rise of large language models (LLMs) makes it closer to coming true. However, an LLM-powered system still suffers from the ambiguity inherent in an NL and the uncertainty brought up by LLMs. This paper proposes a novel LLM-based robot motion planner, named \textit{VernaCopter}, with signal temporal logic (STL) specifications serving as a bridge between NL commands and specific task objectives. The rigorous and abstract nature of formal specifications allows the planner to generate high-quality and highly consistent paths to guide the motion control of a robot. Compared to a conventional NL-prompting-based planner, the proposed VernaCopter planner is more stable and reliable due to less ambiguous uncertainty. Its efficacy and advantage have been validated by two small but challenging experimental scenarios, implying its potential in designing NL-driven robots.

cs.RO↗

arXiv2023

Automated Formation Control Synthesis from Temporal Logic Specifications

In many practical scenarios, multi-robot systems are envisioned to support humans in executing complicated tasks within structured environments, such as search-and-rescue tasks. We propose a framework for a multi-robot swarm to fulfill complex tasks represented by temporal logic specifications. Given temporal logic specifications on the swarm formation and navigation, we develop a controller with runtime safety and convergence guarantees that drive the swarm to formally satisfy the specification. In addition, the synthesized controller will autonomously switch formations as necessary and react to uncontrollable events from the environment. The efficacy of the proposed framework is validated with a simulation study on the navigation of multiple quadrotor robots.

eess.SY↗