temporal trajectory planning

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.

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 safe navigation in dynamic environments with uncertain, time-varying obstacles by anticipating local observations. It introduces the first integration of precise contingency planning with Safe Interval Path Planning (SIPP) to generate formally verified safe macro-actions. The approach performs bounded AND/OR search over a cached action–observation graph to select optimal action sequences for each reachable observation. To guide search efficiently, it employs optimistic and robust SIPP relaxations that yield admissible heuristic bounds. Decisions are made dynamically based on local observations, enabling real-time adaptation. Experiments demonstrate superior performance over fixed-path baselines in controlled road networks and successful planning in gated scenarios where conservative methods fail. The study also reveals a scalability bottleneck as observation uncertainty increases.

Multi-agent systems under limited communication range lack formal infinite-horizon safety guarantees. Method: This paper proposes the first decentralized asynchronous motion planning framework that provides rigorous collision-avoidance safety for nonlinear Dubins-type agents operating over dynamic topologies. It integrates Guardian-based safety control with R-bounded geometric constraints to establish, for the first time, a theoretical linkage between communication radius and distributed safety planning capability. Forward invariance analysis and locally informed asynchronous trajectory optimization ensure infinite-horizon safety using only neighbor-to-neighbor communication. Results: In high-density simulations with 128 agents, the framework achieves 100% collision-free operation, with safety performance invariant to system scale—demonstrating both formally provable safety and computationally scalable decentralized planning.

This work addresses the robust satisfaction of Signal Temporal Logic (STL) specifications under tracking errors and model mismatch by proposing a unified planning-and-control framework. The approach uniquely translates STL specifications into time-varying convex sets in configuration space and embeds them within a Graph of Convex Sets (GCS) framework for trajectory planning. Continuous-time constraint satisfaction is achieved through B-spline parameterization, while a feedback controller is designed to prioritize specification compliance during execution. By employing a shared convex-set representation across both planning and control layers, the method enhances system consistency and robustness. Simulations and real-world experiments on a space robot demonstrate that the proposed framework generates smooth, collision-free trajectories that robustly satisfy STL specifications even in the presence of disturbances.

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 collision risks posed by occluded traffic participants in urban autonomous driving. The authors propose a formal occlusion-aware trajectory planning framework that, for the first time, unifies reachability reasoning under both future observability and complete non-observability within a single model. Integrated with a tree-based motion planner, the approach reduces the excessive conservatism of traditional methods while preserving formal safety guarantees. By explicitly modeling occlusion states and the evolution of observability, the framework enables proactive and efficient trajectory planning. Experimental results demonstrate that the method effectively avoids collisions and significantly improves traffic throughput in challenging simulated occlusion scenarios.