Disturbance-Aware Dynamical Trajectory Planning for Air-Land Bimodal Vehicles

TL;DR

This paper introduces a real-time disturbance-aware motion planning framework for air-land bimodal vehicles, improving trajectory accuracy and energy efficiency in complex, disturbance-prone environments.

Contribution

It presents a novel adaptive planning approach that dynamically adjusts safety boundaries and trajectories based on disturbance estimation for bimodal vehicles.

Findings

33.9% reduction in trajectory tracking error

Enhanced energy efficiency and time performance

Effective disturbance mitigation in complex scenarios

Abstract

Air-land bimodal vehicles provide a promising solution for navigating complex environments by combining the flexibility of aerial locomotion with the energy efficiency of ground mobility. However, planning dynamically feasible, smooth, collision-free, and energy-efficient trajectories remains challenging due to two key factors: 1) unknown dynamic disturbances in both aerial and terrestrial domains, and 2) the inherent complexity of managing bimodal dynamics with distinct constraint characteristics. This paper proposes a disturbance-aware motion-planning framework that addresses this challenge through real-time disturbance estimation and adaptive trajectory generation. The framework comprises two key components: 1) a disturbance-adaptive safety boundary adjustment mechanism that dynamically determines the feasible region of dynamic constraints for both air and land modes based on estimated disturbances via a disturbance observer, and 2) a constraint-adaptive bimodal motion planner that integrates disturbance-aware path searching to guide trajectories toward regions with reduced disturbances and B-spline-based trajectory optimization to refine trajectories within the established feasible constraint boundaries. Experimental validation on a self-developed air-land bimodal vehicle demonstrates substantial performance improvements across three representative disturbance scenarios, achieving an average 33.9% reduction in trajectory tracking error while still maintaining superior time-energy trade-offs compared to existing methods.