FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments

Abstract: Planning high-speed trajectories for UAVs in unknown environments requires algorithmic techniques that enable fast reaction times to guarantee safety as more information about the environment becomes available. The standard approaches that ensure safety by enforcing a "stop" condition in the free-known space can severely limit the speed of the vehicle, especially in situations where much of the world is unknown. Moreover, the ad-hoc time and interval allocation scheme usually imposed on the trajectory also leads to conservative and slower trajectories. This work proposes FASTER (Fast and Safe Trajectory Planner) to ensure safety without sacrificing speed. FASTER obtains high-speed trajectories by enabling the local planner to optimize in both the free-known and unknown spaces. Safety is ensured by always having a safe back-up trajectory in the free-known space. The MIQP formulation proposed also allows the solver to choose the trajectory interval allocation. FASTER is tested extensively in simulation and in real hardware, showing flights in unknown cluttered environments with velocities up to 7.8m/s, and experiments at the maximum speed of a skid-steer ground robot (2m/s).

• The paper introduces FASTER, which employs dual-trajectory optimization to enable high-speed UAV navigation in partially mapped environments.
• It combines whole-trajectory planning across known and unknown spaces with a concurrently computed safe trajectory using MIQP for rapid decision-making.
• Experiments demonstrate UAVs achieving speeds up to 7.8 m/s while safely maneuvering in cluttered and dynamic settings.

FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments

The paper "FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments" presents a novel approach to trajectory planning for UAVs operating at high speeds in environments that are not fully mapped. This paper emphasizes the need for quick response times and agility, without compromising safety, by introducing the FASTER algorithm.

Summary of Contributions

FASTER addresses the constraints commonly encountered in trajectory planning by avoiding the restrictive “stop” condition typically enforced in the free-known space. Instead, it facilitates optimized high-speed trajectories through both free-known and unknown spaces. The cornerstone of this approach is maintaining a safe back-up trajectory in the free-known space, ensuring the UAV can retreat safely if needed.

The planning method involves a two-step optimization process:

1. Whole Trajectory Optimization: This trajectory is calculated within both the unknown and known spaces, maximizing speed and ensuring alignment with the global path.
2. Safe Trajectory Optimization: Runs concurrently to guarantee collision-free alternatives should obstacles be encountered. This trajectory is strictly within the known free space.

FASTER implements these features using a Mixed-Integer Quadratic Program (MIQP) that facilitates autonomous decision-making over trajectory intervals, while significantly reducing conservatism typically imposed by fixed-time allocations.

Methodology and Implementation

Mapping and Trajectory Planning

The planning logic operates using a map that dynamically adjusts to the UAV's immediate surroundings. The UAV’s trajectory planning is segregated into a global planner and a local planner, where the former outlines a preliminary path to the target, and the latter refines the path to accommodate dynamic constraints and immediate obstacles. Specifically, FASTER employs Jump Point Search (JPS) for efficient global pathfinding, combined with convex decomposition to localize and optimize trajectories through predictable sections of the route.

Dynamic Trajectory Replanning

Central to FASTER is its capacity for continual replanning. The algorithm perpetually recalculates two trajectories:

• A primary “Whole Trajectory” intended for optimal speed towards targets,
• A secondary “Safe Trajectory” ensuring safe havens in response to unexpected obstacles.

This dual trajectory framework ensures the UAV’s actions are dynamically adjusted based on real-time sensor data.

Algorithm Performance

Real-world validation is established through detailed simulations and hardware experiments. UAVs were tested for high-speed maneuvering in complex, cluttered environments, reaching peer-leading velocities of 7.8 m/s. In practical experiments, FASTER outperformed equivalent state-of-the-art methods, demonstrating an impressive balance between flight speed and safety assurance.

Implications and Future Work

FASTER extends the theoretical capabilities of UAV trajectory planning, integrating environmental adaptability with high-speed navigation. In practical terms, the system enables UAVs to function efficiently in partially known terrains, a feature beneficial for UAVs in search-and-rescue missions, parcel delivery, and automated surveying.

Future work will involve improving computational efficiency to accommodate larger known spaces, and adapting the approach for UAVs operating in dynamic and uncertain environments. The current reliance on sliding maps could be supplemented or replaced by more advanced methods of environmental perception. Another potential extension is tuning the selection mechanism for the point “R” in the trajectory, to better balance between speed and safety adaptation dynamically.

Overall, FASTER advances UAV trajectory planning by ensuring rapid, adaptable navigation while maintaining safety — a key requirement for UAV deployment in practice.