constraint-driven plan synthesis

This work addresses the common issue in hybrid discrete–continuous planning where first-order trajectories generated by conventional methods often violate second-order dynamical constraints of robotic systems, rendering them infeasible for execution. To bridge the gap between high-level task planning and low-level physical execution, the authors propose a reinforcement learning–based trajectory refinement framework that explicitly embeds analytical second-order dynamics into a Markov decision process. This approach continuously optimizes first-order trajectories produced by a high-level hybrid planner while respecting constraints on time windows, velocity, and acceleration. By integrating reinforcement learning with explicit second-order dynamical modeling—a combination not previously explored—the method significantly enhances the physical feasibility and real-world executability of planned trajectories.

Robots struggle to jointly integrate high-level semantic instructions with formal temporal constraints for safe path planning in complex environments. Method: We propose a hierarchical planning framework jointly driven by Linear Temporal Logic (LTL) and natural language. Our approach pioneers direct compilation of natural language instructions into LTL formulas; constructs a hierarchical map abstraction coupled with a transition system model; and enables end-to-end closed-loop planning—from text input to deterministic finite automaton (DFA), high-level BFS-based task planning, and low-level sampling-based navigation (e.g., RRT/PRM). It supports dynamic instruction updates and semantic-physical co-optimization. Results: Experiments across multiple scenarios demonstrate successful execution of complex tasks involving temporal ordering, obstacle avoidance, and multi-goal constraints (e.g., “first patrol Area A, then retrieve an object, and finally return while avoiding obstacles”). The framework significantly improves task fidelity, safety, and interpretability—establishing a novel paradigm for natural language–guided autonomous robot planning.

Traditional sampling-based motion planning algorithms fail for tasks involving discrete configuration-space symmetries (e.g., manipulation of symmetric objects) due to topological distortions induced by symmetry. Method: This paper establishes the first geometric modeling and sample-complexity theoretical framework for sampling-based planning in symmetric configuration spaces. It introduces group-action-based modeling, quotient-space construction, symmetry-aware distance metrics, and modified RRT/PRM sampling strategies. Contribution/Results: The core innovation is a sampling primitive tailored to finite symmetry groups, with theoretical sample complexity reduced to (O(1/varepsilon^d)), where (d) is the dimension of the quotient space. Experiments demonstrate an average 27% reduction in path length and a 35% decrease in planning time, significantly improving both efficiency and solution quality in symmetric environments.

Addressing the challenge of motion planning under strong spatiotemporal–dynamical coupling in complex scenarios, this paper proposes a three-layer cooperative planning framework: (1) generation of candidate action sequences satisfying spatial constraints; (2) construction of a geometry-guided initial trajectory; and (3) progressive optimal dynamical planning driven by a unified optimization objective—robustness with respect to Signal Temporal Logic (STL). This approach achieves, for the first time, jointly spatiotemporally and dynamically feasible planning for intricate maneuvers such as intersection crossing and evasive circumnavigation—overcoming performance bottlenecks inherent in conventional hierarchical architectures due to constraint decoupling. Evaluated on an Ackermann vehicle model, the method significantly improves planning efficiency and successfully generates high-difficulty cooperative trajectories that are infeasible for existing approaches.

This work addresses multi-target motion planning under kinodynamic differential constraints in unstructured obstacle environments. We propose a synergistic framework integrating machine learning, Traveling Salesman Problem (TSP) optimization, and sampling-based search. Specifically, a regression model predicts the time- and distance-weighted cost for single-target planning; this enables construction of a kinodynamically aware TSP cost matrix. During RRT*-style motion tree expansion, low-cost target sequences are prioritized to generate dynamically feasible, collision-free trajectories traversing multiple regions. The method incorporates rigorous kinodynamic feasibility verification and high-fidelity collision checking. Experiments on a car-like vehicle model demonstrate a 3.2× speedup in planning time over baseline approaches, significantly improving computational efficiency and scalability to larger problem instances. Our approach establishes a novel paradigm for multi-target navigation under high-dimensional, nonlinear constraints.

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.

This work addresses the challenges of trajectory planning for multi-robot systems under signal temporal logic (STL) specifications and dynamic constraints, which often suffer from poor scalability, limited adaptability, and low sampling efficiency. The authors propose a two-stage planning framework: at the single-robot level, a constrained Bayesian optimization tree search (cBOT) learns local cost maps and feasibility constraints; at the multi-robot level, an STL-monitored enhanced kinodynamic conflict-based search (STL-KCBS) embeds STL semantics into conflict detection and resolution mechanisms. By integrating Bayesian optimization with formal STL reasoning, the approach ensures probabilistic completeness while significantly improving trajectory efficiency, safety, and compliance with STL specifications. Extensive simulations and real-world experiments with autonomous surface vessels demonstrate the method’s robustness and practicality in uncertain environments.

This work addresses the challenge of real-time motion planning for complex robotic systems under geometric constraints, which is often hindered by high computational costs. For the first time, SIMD parallelization is introduced into manifold-constrained motion planning by reformulating projection operations into a parallelizable structure that leverages CPU SIMD instruction sets to efficiently accelerate constraint satisfaction. The proposed method dramatically improves computational efficiency, enabling real-time whole-body quasi-static motion planning on a physical humanoid robot. Experimental results demonstrate speedups of 100 to 1,000 times compared to existing approaches, while maintaining accuracy and feasibility under stringent geometric constraints.

This work addresses the challenge of collision-free motion planning for multiple robots operating in spatiotemporally continuous environments with transient, geometrically constrained regions. The authors propose the Space-Time Geometric Convex Set (ST-GCS) framework, which integrates Exact Convex Decomposition (ECD) to jointly model dynamic obstacles and inter-robot interactions, augmented by an occupancy reservation mechanism. The approach combines heuristic best-first graph search with continuous trajectory optimization and employs a windowed coordination strategy to enable efficient large-scale computation. Experimental results demonstrate that the method significantly outperforms existing planners in narrow, transient scenarios, achieving high-quality solutions for problems involving up to one hundred robots within several minutes.

This study addresses the problem of collision-free cooperative path planning for multiple robots operating on graphs derived from the discretization of simple polygons, with the objective of minimizing the total travel distance across all robots. The work proposes a fixed-parameter tractable (FPT) algorithm parameterized by the number of robots \(k\), thereby establishing—for the first time—that this problem is FPT on such polygon-induced graphs. By leveraging structural properties of the underlying graph and geometric constraints inherent to simple polygons, the approach extends existing FPT results from grid environments to more general planar, obstacle-constrained settings. This advancement provides significant progress toward resolving open questions concerning subgrid and planar graph variants of multi-robot path planning.