Real-Time Sampling-based Online Planning for Drone Interception

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.

Authors

• Gilhyun Ryou
• Lukas Lao Beyer
• Sertac Karaman

Keywords

• Uncertainty
• System dynamics
• Neural networks
• Prediction algorithms
• Real-time systems
• Inference algorithms
• Planning
• Robotics and automation
• Trajectory optimization
• Drones
• Online Planning
• Neural Network
• Dynamic Environment
• Arrival Time
• Simulation Environment
• Reachable
• Nonlinear Programming
• Multiple Trajectories
• Parallel Position
• Neural Network Inference
• Prediction Model
• Time Step
• Scaling Factor
• Target Location
• Maximum Speed
• Gaussian Process
• Motion Capture
• Tracking Error
• Policy Planning
• Time Allocation
• Gaussian Mixture Model
• Trajectory Generation
• Goal Position
• Policy Outputs
• Quadratic Programming
• Real-world Experiments
• Departure Time
• Positional Candidate
• Reference Position