potential field planning

This work addresses safe navigation of mobile robots in dynamic human-robot coexistence environments (e.g., homes, offices). We propose an online local trajectory planning method based on Model Predictive Control (MPC). Our key contribution is the first use of neural networks to estimate time-varying obstacle repulsive potential fields in real time, coupled with three dynamic obstacle modeling strategies—static snapshot, parallel prediction, and autoregressive prediction—to jointly optimize safety and computational efficiency. By integrating potential field methods with neural network-based modeling, our approach achieves superior performance over CIAO* and MPPI in the BenchMR simulator, satisfying stringent safety constraints while maintaining single-step planning latency under 100 ms. The method has been successfully deployed on a Husky UGV platform and validated in real-world dynamic office corridor scenarios, demonstrating robust and stable operation.

This work proposes a smooth polynomial implicit-function-based artificial potential field navigation function for mobile robot navigation in three-dimensional environments containing spherical and cylindrical obstacles. The method constructs a potential field within a bounded spherical workspace that possesses a unique, non-degenerate global minimum, thereby eliminating local minima even when obstacles intersect. Safe obstacle-avoidance motion is achieved through gradient-based feedback control, and the optimality and safety of the navigation function are rigorously established via Hessian analysis. Numerical simulations in complex 3D obstacle scenarios demonstrate the effectiveness and robustness of the proposed approach.

Existing artificial potential field (APF)-based methods for static obstacle avoidance by resource-constrained unmanned aerial vehicles (UAVs) lack closed-loop stability guarantees, suffering from chattering and susceptibility to local minima—particularly under stringent real-time response requirements and uncertain obstacle detection. Method: This paper proposes a Multi-Artificial Potential Function (MAPOF) control framework that integrates hybrid systems theory with Lyapunov-based analysis. Contribution/Results: MAPOF is the first APF variant to rigorously establish asymptotic stability of the closed-loop system and derive explicit, tunable parameter conditions for stability. The design inherently mitigates local minima and suppresses control chattering. Numerical simulations in complex static obstacle environments demonstrate that MAPOF achieves stable, smooth, collision-free trajectory planning with significantly improved convergence speed and robustness compared to conventional single-potential-field approaches.

Real-time trajectory generation for obstacle avoidance in dynamic environments suffers from reliance on explicit obstacle boundary representations, low computational efficiency, and suboptimal trajectories. Method: This paper proposes a real-time planning and control framework integrating Model Predictive Control (MPC) with discrete-time high-order Control Barrier Functions (DHOCBFs). It automatically generates convex polyhedral obstacle representations from grid maps—bypassing the need for prior geometric knowledge (e.g., explicit boundary equations)—and directly derives DHOCBFs to ensure safety for both convex and non-convex obstacles. Global optimality is further enforced via optimization-driven path planning. Contribution/Results: Experiments in tightly constrained dynamic scenarios demonstrate significant improvements in computational speed and trajectory feasibility compared to baseline CBF methods, while yielding shorter, safer, and dynamically feasible trajectories.

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.

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 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.

This work addresses the problem of generating minimum-time smooth trajectories subject to high-order derivative constraints in environments with convex obstacles. The authors propose a biconvex optimization framework that jointly convexifies the time-optimal objective and dynamic constraints through a change of variables, while modeling collision avoidance via time-varying separating hyperplanes. This formulation yields an alternatingly optimizable biconvex structure that supports arbitrary-order derivative constraints, permits interruption at any iteration, and guarantees convergence while effectively escaping local minima. Notably, the method requires only a simple collision-free piecewise-linear path for initialization yet reliably converges to high-quality solutions. Evaluated on drone navigation and dual-arm box unloading tasks, the approach achieves trajectory quality and computational efficiency comparable to state-of-the-art decoupled planners, with broader applicability and strong robustness to poor initial guesses.

This study addresses the challenge of evaluating real-time motion planning in dynamic hazard fields by establishing a unified benchmark within rotating hazardous environments to systematically compare classical planning and learning-based paradigms. Through experiments employing classical planners, Proximal Policy Optimization (PPO) reinforcement learning, and dynamic obstacle simulation, this work reveals that environmental uncertainty is the predominant factor determining the effectiveness of a given planning paradigm. The results demonstrate that in stochastic dynamic environments, the PPO approach significantly outperforms classical methods in terms of computational latency, planning success rate, and path quality. These findings provide critical theoretical justification and empirical evidence for selecting appropriate motion planning paradigms for autonomous agents operating in complex scenarios.

该论文介绍了一个名为OpenSCvx的开源Python框架，用于解决轨迹优化问题，通过提供符号建模接口自动生成并求解轨迹优化问题。