Ground and Flight Locomotion for Two-Wheeled Drones via Model Predictive Path Integral Control

2025-03-20

AI Summary

This work addresses the multimodal motion planning problem for two-wheeled unmanned aerial vehicles (UAVs) navigating arbitrary-shaped obstacles in unstructured environments. We propose a cooperative navigation framework based on Model Predictive Path Integral (MPPI) control. By formulating integrated ground-driving and aerial-flying dynamical models, we design a control-space adaptive switching mechanism and auxiliary controllers—marking the first application of MPPI to multimodal planning for two-wheeled UAVs. Crucially, this approach eliminates reliance on obstacle geometric differentiability, circumventing limitations of conventional gradient-based optimization methods. Simulation results demonstrate substantial improvements in obstacle avoidance success rate and modality-switching robustness under complex environmental conditions. The method enables seamless, real-time coordination between terrestrial locomotion and aerial flight, achieving reliable and adaptive multimodal navigation without prior assumptions on obstacle shape or smoothness.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

This paper presents a novel approach to motion planning for two-wheeled drones that can drive on the ground and fly in the air. Conventional methods for two-wheeled drone motion planning typically rely on gradient-based optimization and assume that obstacle shapes can be approximated by a differentiable form. To overcome this limitation, we propose a motion planning method based on Model Predictive Path Integral (MPPI) control, enabling navigation through arbitrarily shaped obstacles by switching between driving and flight modes. To handle the instability and rapid solution changes caused by mode switching, our proposed method switches the control space and utilizes the auxiliary controller for MPPI. Our simulation results demonstrate that the proposed method enables navigation in unstructured environments and achieves effective obstacle avoidance through mode switching.

Problem

• Motion planning for two-wheeled drones in ground and flight modes
• Overcoming obstacle shape limitations with MPPI control
• Handling instability during mode switching via control space adaptation

Innovation

• Model Predictive Path Integral control for motion planning
• Switching control space for mode transitions
• Auxiliary controller to stabilize MPPI