Decision Transformer-Based Drone Trajectory Planning with Dynamic Safety-Efficiency Trade-Offs

TL;DR

This paper introduces a Decision Transformer-based drone trajectory planner that uses a single parameter, RTG, to dynamically balance safety and efficiency, outperforming traditional methods in simulations and real-world tests.

Contribution

The paper presents a novel trajectory planning approach that employs RTG as a temperature parameter for dynamic safety-efficiency trade-off adjustment, eliminating the need for expert tuning.

Findings

The planner effectively adjusts safety and efficiency by tuning RTG.

It outperforms baseline methods in simulation environments.

Real-world experiments confirm its practicality.

Abstract

A drone trajectory planner should be able to dynamically adjust the safety-efficiency trade-off according to varying mission requirements in unknown environments. Although traditional polynomial-based planners offer computational efficiency and smooth trajectory generation, they require expert knowledge to tune multiple parameters to adjust this trade-off. Moreover, even with careful tuning, the resulting adjustment may fail to achieve the desired trade-off. Similarly, although reinforcement learning-based planners are adaptable in unknown environments, they do not explicitly address the safety-efficiency trade-off. To overcome this limitation, we introduce a Decision Transformer-based trajectory planner that leverages a single parameter, Return-to-Go (RTG), as a \emph{temperature parameter} to dynamically adjust the safety-efficiency trade-off. In our framework, since RTG intuitively measures the safety and efficiency of a trajectory, RTG tuning does not require expert knowledge. We validate our approach using Gazebo simulations in both structured grid and unstructured random environments. The experimental results demonstrate that our planner can dynamically adjust the safety-efficiency trade-off by simply tuning the RTG parameter. Furthermore, our planner outperforms existing baseline methods across various RTG settings, generating safer trajectories when tuned for safety and more efficient trajectories when tuned for efficiency. Real-world experiments further confirm the reliability and practicality of our proposed planner.