motion planning

This work proposes an online trajectory generation method based on piecewise quintic/quartic splines to address the challenge of converting arbitrary geometric paths into kinematically feasible and collision-free trajectories in dynamic environments. The approach explicitly enforces jerk constraints and supports real-time replanning under high-frequency goal updates. By integrating dynamic environment perception and a responsive adaptation mechanism, it guarantees collision avoidance within finite time while permitting bounded deviations from the original path. Both simulation and real-world experiments demonstrate that the method outperforms existing approaches in trajectory smoothness, computational efficiency, and real-time performance, achieving stable operation in human-in-the-loop dynamic scenarios with target update rates up to 1 kHz.

This paper addresses collaborative motion planning for homogeneous linear multi-agent systems operating in unknown obstacle-rich environments without explicit system models. Method: We propose a fully data-driven framework that is dynamically feasible and provably safe. It learns feedback gains and local invariant ellipsoids—serving as safety certificates—by solving a semidefinite program on experimental data. Distributed, optimization-free trajectory generation is achieved by integrating grid-based RRT sampling with a spatiotemporal resource reservation mechanism. Contribution/Results: To the best of our knowledge, this is the first work to unify data-driven invariant set learning with spatiotemporal reservation. Relying solely on limited experimental data and convex optimization tools, it simultaneously guarantees collision avoidance with static/dynamic obstacles and inter-agent collisions. The framework significantly reduces computational overhead while providing formal safety guarantees. Extensive simulations validate its effectiveness under tight dynamical constraints and complex obstacle configurations.

This work addresses the challenge of inefficient motion planning and excessively large search spaces for nonholonomic mobile robots operating in complex structured environments. To this end, the authors propose a rectangular corridor graph representation based on deterministic free-space decomposition. By constructing a compact yet overlapping set of rectangular corridors, the method significantly reduces the search space while preserving path resolution completeness. Integrating efficient graph-based search with analytical trajectory generation, the framework enables near-time-optimal navigation that respects kinematic constraints. Extensive experiments on both large-scale simulations and physical robot platforms demonstrate the approach’s efficiency and practicality, and the implementation has been made publicly available.

To address the stringent real-time, safety, and dynamic feasibility requirements of high-speed autonomous navigation in large-scale, complex environments, this paper proposes a non-optimization-based graph-search and trajectory-stitching framework. The method constructs a state graph from a predefined motion primitive library and integrates heuristic graph search, trajectory stitching, smoothing, and multi-constraint feasibility verification—including state, actuator, and collision constraints—thereby avoiding the computational overhead of numerical optimization. It achieves millisecond-level long-horizon trajectory generation in complex scenes spanning tens of meters, with guaranteed dynamic feasibility, collision-free execution, and full-state constraint satisfaction. Compared to two state-of-the-art optimization-based planners, our approach demonstrates significant improvements in real-time performance, robustness, and computational efficiency. This work establishes a new paradigm for highly reliable, real-time motion planning for agile mobile robots.

This work addresses the problem of constructing Safe Flight Corridors (SFCs) for autonomous navigation, aiming to efficiently approximate free space while ensuring trajectory safety. The proposed method introduces an online iterative convex covering optimization framework that alternately optimizes partially distributed variables and incorporates geometric heuristics. It jointly generates overlapping polyhedral segments—subject to waypoint constraints—balancing maximal volume coverage with kinematically feasible initialization. Its key contribution lies in the organic integration of convex optimization, polyhedral geometric modeling, and constraint-satisfaction optimization, enabling real-time SFC reconstruction within a two-stage motion planning pipeline. Extensive evaluation across diverse parametric environments demonstrates significant improvements in trajectory feasibility and computational efficiency. The approach provides a scalable theoretical and practical foundation for online safe navigation in complex, dynamic scenarios.

Addressing the challenge of ensuring real-time performance, dynamic feasibility, and safety simultaneously for high-speed autonomous navigation in known environments, this paper proposes STITCHER—a real-time trajectory planning framework that avoids numerical optimization. STITCHER integrates graph-search-driven motion primitive matching with short-trajectory-segment stitching, enabling millisecond-scale handling of non-convex state and actuator constraints (e.g., tilt angle, motor thrust limits). By leveraging a precomputed trajectory library, efficient online querying, and lightweight dynamic feasibility verification coupled with collision checking, STITCHER generates collision-free trajectories over long horizons (<5 ms) in complex 50 m × 50 m environments. Hardware experiments on a quadrotor demonstrate robust real-time trajectory tracking. Quantitative evaluation shows STITCHER significantly outperforms two state-of-the-art optimization-based planners in both computational efficiency and tracking accuracy under dynamic constraints.

In high-dimensional and uncertain environments, traditional robotic motion planning struggles to simultaneously achieve obstacle avoidance, computational efficiency, and robust execution. This work proposes an Environment-Constrained Exploitation (ECE) approach that transcends the conventional “avoidance-as-non-contact” paradigm by treating environmental contacts as intentional constraints. These active constraints reduce the effective planning dimensionality and computational complexity while directing exploration toward task-relevant regions. By integrating contact-aware modeling and uncertainty handling within a rapidly-exploring random tree (RRT) framework, ECE substantially enhances both planning efficiency and execution robustness. Real-world experiments demonstrate that the method effectively simplifies motion planning in complex settings and improves system adaptability and overall performance.

This work addresses the challenge of deadlock and local infeasibility in robotic task and motion planning under signal temporal logic (STL) specifications within non-convex, complex environments. To overcome these issues, the authors propose a hybrid planning framework that integrates discrete decision variables with continuous dynamics. By constructing control barrier functions in a geometrically transformed, disk-shaped workspace and incorporating local feasibility analysis under input saturation, the approach holistically resolves conflicts among multiple spatiotemporal tasks. The key innovation lies in the co-design of hybrid systems, STL specifications, and geometry-driven barrier functions, which collectively enhance planning feasibility and system robustness. Simulations demonstrate the method’s efficiency and reliability in handling overlapping spatiotemporal tasks.

In semi-static environments, motion planning must satisfy strict fixed-time response constraints while providing formal safety guarantees—a longstanding challenge. To address this, we propose the Coverage-Verified Roadmap (CVRM) framework: it incrementally constructs a roadmap, partitions the obstacle configuration space into disjoint subregions, and systematically verifies path feasibility within each subregion—thereby enabling theoretically guaranteed fixed-time query resolution. CVRM is the first approach to introduce coverage-based verification into continuous configuration spaces, eliminating reliance on configuration-space discretization inherent in conventional methods. Evaluated on 7-DOF Panda robot tabletop manipulation simulations, CVRM achieves a 23% higher query success rate and expands feasible configuration coverage by 31% compared to baseline methods, significantly improving both practical utility and formal assurance.

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.