construct belief roadmap

This work addresses the challenge of providing reachability guarantees in belief-space planning under motion uncertainty and state-control constraints. The authors propose PRISM, an algorithm that establishes, for the first time, a theory of constrained state covariance controllability, thereby ensuring full coverage in belief space while guaranteeing completeness within finite time and memory. PRISM decomposes planning into deterministic mean trajectory generation and covariance contraction, integrating multi-query belief roadmap construction with online local optimization. This approach achieves substantially improved performance: it attains 100% coverage in low- to moderate-difficulty scenarios and maintains 97–100% coverage even in the most challenging cases—significantly outperforming existing methods, all of which fall below 45%. Moreover, PRISM yields trajectories with lower cost and reduced variance.

本文通过信息空间框架定义场景图转换系统及动作语义，提出了一种任务导向的场景图充分性形式化方法，解决了复杂环境中机器人任务规划效率问题。

This work addresses the challenge of robot planning under partial observability, where observation-dependent branching decisions render conventional sequential trajectories inadequate for handling uncertainty. The paper introduces tree-structured trajectories into partially observable model predictive control (MPC) and task and motion planning (TAMP), explicitly modeling multiple belief-state evolution paths induced by observations. Key contributions include a distributed augmented Lagrangian algorithm (D-AuLa) enabling parallel optimization, an extension of logical geometric programming (LGP) to support hierarchical decision-making in belief space, and a macro-action policy to enhance scalability. Experimental results demonstrate that the proposed approach significantly reduces control cost and meets real-time requirements in autonomous driving scenarios, validates effectiveness on small-scale problems, and extends to larger-scale applications through exploratory strategies.

Belief systems often exhibit global inconsistency yet support reliable local reasoning. This paper addresses the challenge of enabling sound classical logical inference over globally inconsistent symbolic knowledge graphs. Method: We introduce the notion of “reasoning regions”—high-confidence, structurally balanced subgraphs extracted from directed signed weighted graphs. Our approach decouples source credibility from structural confidence, employs a contractive confidence propagation algorithm augmented with parity-based structural balance detection, and incorporates shock-robust local updates. Greedy repair and Jaccard-based deduplication further yield compact, interpretable region atlases. Contribution/Results: The framework achieves near-linear time complexity and demonstrates strong robustness against perturbations on synthetic benchmarks. It establishes, for the first time, a computationally tractable and dynamically evolvable foundation for inconsistency-tolerant reasoning—providing both theoretical grounding and practical algorithms for identifying locally coherent fragments within globally contradictory belief systems.

Existing belief representation formalisms—such as propositional sets or probability distributions—fail to capture the internal structure of beliefs, conflate external credibility with internal coherence, and cannot adequately model fragmented or contradictory cognitive states. Method: We propose a directed weighted graph-based belief system model: nodes represent individual beliefs, and directed edges encode cognitive relations (e.g., support, contradiction); we introduce a dual-dimensional quantification scheme—“credibility” (reflecting reliability of external sources) and “confidence” (measuring strength of internal structural support)—thereby decoupling these orthogonal dimensions. Contribution/Results: This framework transcends limitations of classical logic, probabilistic, and argumentation-based models by enabling rigorous representation of inconsistent and fragmented beliefs. Leveraging graph-theoretic tools—including weighted directed graphs, node-weight functions, and connectivity analysis—it supports fine-grained relational expression and provides formally grounded, static analyses of cognitive coherence, structural conflict, and representational boundaries.

This work addresses motion planning for continuous-time stochastic systems under both process and observation uncertainties by proposing a sampling-based planning framework that enables continuous-time probabilistic safety verification over entire trajectories. The approach constructs an offline hybrid belief propagation model that integrates continuous-time ordinary differential equation (ODE) dynamics with discrete Kalman updates, and introduces a belief barrier function as a safety checker capable of detecting potential constraint violations between sampling instants—marking the first method to achieve such intra-interval safety guarantees. Integrated with RRT/SST planners, the framework demonstrates superior performance over conventional discrete-time methods across multiple benchmark scenarios, including narrow passages, achieving higher success rates, enhanced robustness, and stronger formal safety assurances.

This study addresses the challenge that existing generative models struggle to produce goal-directed floor plans under strict structural and geometric constraints. We propose a dataset-free constrained Markov decision process formulation that integrates parametric graph grammars with safe reinforcement learning. The core innovation lies in designing a state-dependent safe-set action projection mechanism, which satisfies hard construction constraints in real time while optimizing task objectives. Experimental results demonstrate that our method comprehensively outperforms both traditional and deep generative baselines on a newly introduced benchmark, consistently producing high-quality, feasible floor plan embeddings.

This study addresses the challenge of balancing roadmap density and solution quality in multi-agent path planning by proposing an automated shared roadmap generation framework based on heterogeneous graph neural networks. The method exploits task permutation invariance to enable roadmap reuse and is trained under supervision from occupancy density maps aggregated over expert trajectories, yielding compact yet coordination-aware roadmaps. Experimental results demonstrate that the proposed framework reduces both runtime and graph size by at least 40%, while significantly enhancing global connectivity and interaction reasoning capabilities. Consequently, it effectively improves planning efficiency and solution quality in densely populated scenarios.

This work addresses the challenge of jointly planning spatiotemporal constraints—specifying when and where tasks are executed—and topological constraints—governing agent interaction structures—in multi-agent systems. To this end, the authors propose a unified modeling framework grounded in STL-GO logic, which for the first time incorporates dynamic multi-graph interactions. They develop two complete solution approaches based on Mixed-Integer Programming (MIP) and Satisfiability Modulo Theories (SMT), enabling seamless switching and comparative analysis between the two paradigms. The effectiveness of the proposed method is validated on a multi-UAV search-and-rescue benchmark, demonstrating strong expressiveness and favorable scalability across varying team sizes and levels of time-varying graph complexity.

This work addresses the challenge of event localization in communication-denied environments, where path-integral sensors provide only binary path observations, thereby hindering precise event detection and limiting information fusion and path planning. The paper proposes a Bayesian network–based belief map updating method that, for the first time, enables principled Bayesian inference over path observations by explicitly modeling false alarms and missed detections. By integrating Shannon information theory, the approach plans trajectories that maximize information gain. In contrast to existing methods relying on posterior mean approximations, the proposed technique significantly accelerates belief map convergence and substantially improves both accuracy and efficiency in static hazard detection, demonstrating consistent advantages in both single-robot and multi-robot scenarios.