A Model Predictive Path Integral Method for Fast, Proactive, and Uncertainty-Aware UAV Planning in Cluttered Environments

TL;DR

This paper introduces a Model Predictive Path Integral (MPPI) based planning method for UAVs that proactively adapts to cluttered environments, reducing collisions and improving trajectory safety through risk-aware, fast, and flexible planning.

Contribution

It presents a novel MPPI-based motion planning framework that incorporates risk assessment and reduces sample requirements, enabling real-time, proactive UAV navigation in complex environments.

Findings

Reduces UAV collisions in cluttered environments.

Adapts speed based on obstacle proximity for safer navigation.

Validated through simulations and hardware experiments.

Abstract

Current motion planning approaches for autonomous mobile robots often assume that the low level controller of the system is able to track the planned motion with very high accuracy. In practice, however, tracking error can be affected by many factors, and could lead to potential collisions when the robot must traverse a cluttered environment. To address this problem, this paper proposes a novel receding-horizon motion planning approach based on Model Predictive Path Integral (MPPI) control theory -- a flexible sampling-based control technique that requires minimal assumptions on vehicle dynamics and cost functions. This flexibility is leveraged to propose a motion planning framework that also considers a data-informed risk function. Using the MPPI algorithm as a motion planner also reduces the number of samples required by the algorithm, relaxing the hardware requirements for implementation. The proposed approach is validated through trajectory generation for a quadrotor unmanned aerial vehicle (UAV), where fast motion increases trajectory tracking error and can lead to collisions with nearby obstacles. Simulations and hardware experiments demonstrate that the MPPI motion planner proactively adapts to the obstacles that the UAV must negotiate, slowing down when near obstacles and moving quickly when away from obstacles, resulting in a complete reduction of collisions while still producing lively motion.