Real-Time Sampling-based Online Planning for Drone Interception

Abstract: This paper studies high-speed online planning in dynamic environments. The problem requires finding time-optimal trajectories that conform to system dynamics, meeting computational constraints for real-time adaptation, and accounting for uncertainty from environmental changes. To address these challenges, we propose a sampling-based online planning algorithm that leverages neural network inference to replace time-consuming nonlinear trajectory optimization, enabling rapid exploration of multiple trajectory options under uncertainty. The proposed method is applied to the drone interception problem, where a defense drone must intercept a target while avoiding collisions and handling imperfect target predictions. The algorithm efficiently generates trajectories toward multiple potential target drone positions in parallel. It then assesses trajectory reachability by comparing traversal times with the target drone's predicted arrival time, ultimately selecting the minimum-time reachable trajectory. Through extensive validation in both simulated and real-world environments, we demonstrate our method's capability for high-rate online planning and its adaptability to unpredictable movements in unstructured settings.

Real-Time Sampling-based Online Planning for Drone Interception: An Overview

The paper "Real-Time Sampling-based Online Planning for Drone Interception" explores an innovative approach to the challenge of high-speed trajectory planning in dynamic environments. This endeavor addresses the requirements for time-optimal trajectory computation, accounting for real-time constraints and uncertainties presented by environmental changes. The focal problem tackled is the interception of a maneuvering drone, demanding the quick adaptation of a defense drone's trajectory.

The core contribution of this work is a sampling-based online planning algorithm that leverages neural network inference to expedite trajectory computations traditionally hindered by nonlinear optimization processes. Specifically, the paper demonstrates the method's efficacy in the context of drone interception, where a defense drone must engage a target by efficiently plotting trajectories that consider potential target positions, while incorporating avoidance of obstacles and coping with imperfect predictions of target movements.

Algorithmic Framework

To achieve real-time responsiveness, the proposed method encompasses the following major components:

1. Sampling Strategy: The algorithm samples multiple initial trajectories towards predicted target positions derived from a probabilistic prediction model. These candidate trajectories allow the defense drone to evaluate distinct courses of action immediately.
2. Neural Network-Based Optimization: By deploying a neural network policy for rapid trajectory optimization, the method bypasses the computational intensity of traditional nonlinear trajectory corrections. The policy evaluates each candidate trajectory for its feasibility, optimizing trajectories iteratively to meet dynamic constraints.
3. Iterative Time Adaptation: Utilizing the invariance in trajectory shape with respect to scaled time allocations, the method iteratively adjusts the trajectory's traversal time for synchronization with predicted target interception times. This is crucial in maintaining feasible trajectories while facilitating real-time responsiveness to the changing positional predictions of the target.

The algorithm effectively combines the prediction of target positions, initial rapid pathfinding using A* search, and neural optimization, concluding with an optimal trajectory selection based on minimum traversal times.

Experimental Validation

The efficacy of the presented approach is substantiated through simulations and real-world trials involving dynamic interception tasks in cluttered environments. The simulations performed across varied environments and target speeds demonstrate the algorithm's capability for high-rate updates and its robustness to prediction noise. The real-world experimental results further validate the approach’s practical applicability in autonomous drone interception tasks.

Numerical Results

The performance evaluation highlights the paper's outcomes with a notable success rate in target interceptions across different prediction models, ranging from exact trajectory predictions to noisy and model-derived predictions using Gaussian mixtures. The experiments reveal that the algorithm maintains trajectory adaptability even under uncertain prediction constraints, achieving successful interceptions with significant frequency.

Theoretical and Practical Implications

The research has twofold implications: theoretically, it advances the discourse on real-time planning in dynamic systems, integrating neural inference within sampling-based planning frameworks for agility in response. Practically, it showcases an application-ready methodology for scenarios demanding high-speed, real-time decision-making, such as security and defense applications in drones.

The future directions might encompass enhancements in prediction models to further tighten uncertainties and exploring applications beyond drone interception to other dynamic layers of robotic autonomy involving multi-agent coordination and cooperative behaviors.

This paper distinctly contributes to the literature on agile robotics by presenting a structured methodology that navigates complex planning tasks efficiently, embodying an intersection of sampling-based methods and learning-based optimizations effectively suited for real-world deployments.