online planning

Real-time multi-step planning and obstacle avoidance for autonomous robots in dynamic environments remain challenging, particularly under resource constraints and without prior map knowledge. Method: We propose a lightweight, closed-loop reactive planning framework that requires no pre-mapping or offline computation. Our approach integrates biologically inspired attention mechanisms with local LiDAR perception to construct transient control-chain plans. It introduces forward depth-first model checking—novel in real-time multi-step planning—combined with environment-aware 2D LiDAR discretization and closed-loop feedback control. Contribution/Results: The framework provides theoretical guarantees on safety and interpretability. Empirically, it generates safe, multi-step local trajectories within 100 ms on low-power embedded hardware. In complex scenarios—including dead ends and playgrounds—it significantly outperforms single-step reactive systems in obstacle avoidance success rate and response robustness.

Existing approaches to automated planning with continuous control parameters typically treat them as constraints, hindering efficient search in infinite-dimensional decision spaces. This paper proposes a novel heuristic best-first search algorithm that explicitly models continuous control parameters as first-class decision variables in the search space—not as auxiliary constraints. To ensure scalability and completeness, we introduce a lazy partial expansion mechanism that incrementally and boundedly unfolds both state and parameter spaces. Theoretical analysis establishes systematic search guarantees, including completeness under mild assumptions. Empirical evaluation on diverse benchmark domains featuring continuous control parameters demonstrates consistent superiority over state-of-the-art planners. Our method provides a scalable, robust, and practical paradigm for parameterized planning over infinite domains, advancing the frontier of continuous-space automated planning.

Real-time robot replanning in dynamic environments incurs high computational overhead and relies heavily on explicit change detection and graph updates. Method: This paper proposes a novel incremental planning paradigm that eliminates the need for explicit reuse or update of historical paths. It decouples dynamic replanning into a sequence of independent, asymptotically optimal sampling-based planning problems—thereby avoiding dependence on obstacle change perception and dense graph reconstruction. The approach leverages almost-surely asymptotically optimal algorithms, including Effort-Informed Trees* (EIT*) and Asymptotically Optimal RRT-Connect (AORRTC), to balance rapid initial solution generation with continuous path refinement. Contribution/Results: Simulation results show that EIT*-generated paths achieve significantly shorter median lengths than those produced by mainstream reactive planners. Physical experiments on a robotic manipulator demonstrate AORRTC’s effectiveness and robustness in complex, dynamic task scenarios.

This study addresses the challenge of designing an optimal recommendation mechanism in a finite-horizon discrete-time dynamic system where a system designer cannot directly control the actions of two strategic agents. The designer aims to maximize their own objective by recommending actions based on shared historical information, while ensuring that the agents find it sequentially rational to follow these recommendations, thereby forming a sequential rationality equilibrium. To this end, the paper proposes a novel recommendation mechanism that explicitly satisfies sequential rationality constraints and develops a computationally tractable solution framework combining backward induction with linear programming. This approach achieves, for the first time, the optimization of the designer’s objective under strict sequential rationality conditions, demonstrating both the effectiveness and computational feasibility of the proposed mechanism.

This work addresses the limitations of traditional sampling-based motion planning algorithms in real-time performance and integration with modern AI research workflows by presenting a systematic upgrade to the open-source OMPL library. For the first time, hardware acceleration support—encompassing GPUs and FPGAs—is introduced into OMPL, alongside enhanced compatibility with mainstream AI toolchains. The extension supports diverse planning paradigms, including asymptotically optimal planning, lazy sampling, constraint handling, and task specifications expressed in linear temporal logic. These advancements substantially improve computational efficiency and scalability, reinforcing OMPL’s foundational role in motion planning while significantly broadening its applicability to complex intelligent systems and autonomous robotic platforms.

This work addresses the challenge of intractable exact solution for partially observable Markov decision processes (POMDPs) due to their high computational complexity by proposing an adaptive open-loop simplification framework. The approach constructs a belief tree based on topological structure and alternates between open-loop and closed-loop planning. It introduces, for the first time, a safety-aware replanning-skipping mechanism for multi-step open-loop action sequences with formal performance guarantees. By deriving efficiently computable performance bounds, the method ensures that the simplified planning process still identifies the optimal immediate action of the original problem. Experimental results demonstrate that the proposed framework significantly reduces planning overhead while preserving provable performance guarantees, thereby substantially improving the scalability and efficiency of online POMDP solvers.

In high-density industrial environments, heterogeneous multi-robot systems are prone to path conflicts, increased waiting times, and congestion propagation due to communication delays and execution uncertainties. This work proposes the SCALE framework, which innovatively integrates robot motion characteristics into conflict resolution and constructs a generalized conjugate action-priority hypergraph (CAPH) to dynamically adjust robot priorities, enabling online generation of feasible paths and adaptive coordination. Leveraging a reactive architecture combined with an adaptive scheduling algorithm, the approach significantly reduces congestion propagation and waiting times in both simulations and a three-day real-world warehouse deployment, thereby enhancing coordination efficiency and execution robustness of heterogeneous robot fleets in complex operational scenarios.

This work addresses the exponential growth in computational complexity with planning horizon that plagues online planning in continuous Markov decision processes due to tree-based structures. To overcome this limitation, the authors propose the Graph-based Sparse Sampling (GSS) algorithm, which introduces a branching-free graph structure into online planning for continuous MDPs for the first time. GSS enables efficient allocation of computational resources by sharing sampled future trajectories across multiple candidate actions and integrating smooth backtracking with heuristic policies, while also supporting GPU-accelerated batch processing. Under conditions of trajectory overlap, regularity, and action coverage, theoretical analysis shows that GSS incurs only polynomial growth in performance error with respect to the planning horizon, thereby circumventing the exponential bottleneck inherent in traditional tree search. Empirical results demonstrate that GSS significantly outperforms existing tree-based planners in continuous control tasks, achieving near-optimal performance especially in long-horizon scenarios.

This work proposes a unified planning and control framework that integrates formal specifications with efficient synthesis to ensure reliable robot operation in complex, dynamic environments. By precisely encoding spatiotemporal and logical constraints using Linear Temporal Logic (LTL) and Signal Temporal Logic (STL), the approach synergistically combines multiple paradigms—including graph search, reactive synthesis via game-theoretic methods, sampling-based motion planning, trajectory optimization, and control barrier functions—to simultaneously guarantee correctness of behavior and computational tractability. The framework establishes a coherent theoretical foundation and practical synthesis pipeline for high-assurance autonomous systems, explicitly elucidating the fundamental trade-offs among modeling fidelity, scalability, and verification strength.

This work addresses the challenge of inefficient online planning in long-horizon partially observable Markov decision processes (POMDPs) by introducing the ROP-RAS3 method. ROP-RAS3 uniquely integrates ultra-fast sampling-based motion planning with a reference policy to guide belief-space exploration through online generation of diverse macro-actions, thereby circumventing exhaustive search over the action space. As a result, its convergence rate depends only on the number of sampled actions rather than the size of the full action space. The approach accommodates continuous, discrete, or hybrid state, action, and observation spaces. Evaluated on tasks involving up to 3,000-step horizons and 35-dimensional state spaces, ROP-RAS3 achieves several-fold higher success rates than current state-of-the-art methods and demonstrates practical efficacy on physical robotic platforms.