# QP Chaser: Polynomial Trajectory Generation for Autonomous Aerial Tracking

## TL;DR

This paper introduces QP Chaser, a quadratic programming-based trajectory planning method for autonomous aerial tracking that robustly maintains target visibility in various dynamic environments by considering target prediction errors and entire body visibility.

## Contribution

It presents a novel trajectory planning pipeline that handles multiple tracking scenarios and accounts for prediction errors, unlike prior methods focusing on single setups or target center visibility.

## Key findings

- Successfully maintains target visibility in diverse scenarios
- Validated through high-fidelity simulations and real-world tests
- Robustly handles static and dynamic environments

## Abstract

Maintaining the visibility of the target is one of the major objectives of aerial tracking missions. This paper proposes a target-visible trajectory planning pipeline using quadratic programming. Our approach can handle various tracking settings, including single and dual target following and both static and dynamic environments, unlike other works that focus on a single specific setup. In contrast to other studies that fully trust the predicted trajectory of the target and consider only the visibility of the center of the target, our pipeline considers error in target path prediction and the entire body of the target to maintain the target visibility robustly. First, a prediction module uses a sample-check strategy to quickly calculate the reachable areas of moving objects, which represent the areas their bodies can reach, considering obstacles. Subsequently, the planning module formulates a single QP problem, considering path homotopy, to generate a tracking trajectory that maximizes the visibility of the target's reachable area among obstacles. The performance of the planner is validated in multiple scenarios, through high-fidelity simulations and real-world experiments.