Towards Search-based Motion Planning for Micro Aerial Vehicles

TL;DR

This paper develops a search-based motion planning framework for Micro Aerial Vehicles that generates safe, feasible, and optimal trajectories considering dynamics, uncertainties, and environmental constraints in real-time.

Contribution

It extends search-based planning to handle MAV-specific challenges like motion uncertainty, field-of-view constraints, and dynamic environments with motion primitives.

Findings

Effective planning in complex scenarios demonstrated

Real-time computation achieved for MAV trajectories

Addresses multiple navigation challenges for MAVs

Abstract

Search-based motion planning has been used for mobile robots in many applications. However, it has not been fully developed and applied for planning full state trajectories of Micro Aerial Vehicles (MAVs) due to their complicated dynamics and the requirement of real-time computation. In this paper, we explore a search-based motion planning framework that plans dynamically feasible, collision-free, and resolution optimal and complete trajectories. This paper extends the search-based planning approach to address three important scenarios for MAVs navigation: (i) planning safe trajectories in the presence of motion uncertainty; (ii) planning with constraints on field-of-view and (iii) planning in dynamic environments. We show that these problems can be solved effectively and efficiently using the proposed search-based planning with motion primitives.