A Framework for Multi-Vehicle Navigation Using Feedback-Based Motion Primitives

TL;DR

This paper introduces a hybrid control framework combining feedback-based motion primitives and automaton-based planning for multi-vehicle navigation, demonstrating robustness and efficacy in quadrocopter experiments.

Contribution

It presents a novel multi-vehicle navigation framework integrating low-level feedback primitives with high-level automaton-based planning.

Findings

Effective in 2D and 3D quadrocopter navigation

Robust against disturbances and uncertainties

Automated planning with formalized constraints

Abstract

We present a hybrid control framework for solving a motion planning problem among a collection of heterogenous agents. The proposed approach utilizes a finite set of low-level motion primitives, each based on a piecewise affine feedback control, to generate complex motions in a gridded workspace. The constraints on allowable sequences of successive motion primitives are formalized through a maneuver automaton. At the higher level, a control policy generated by a shortest path non-deterministic algorithm determines which motion primitive is executed in each box of the gridded workspace. The overall framework yields a highly robust control design on both the low and high levels. We experimentally demonstrate the efficacy and robustness of this framework for multiple quadrocopters maneuvering in a 2D or 3D workspace.