Brain inspired path planning algorithms for drones

Yixun Chao; Philipp Augenstein; Arne Roennau; Ruediger Dillmann; Zhi Xiong

doi:10.3389/fnbot.2023.1111861

Brain inspired path planning algorithms for drones

Front Neurorobot. 2023 Mar 3:17:1111861. doi: 10.3389/fnbot.2023.1111861. eCollection 2023.

Authors

Yixun Chao^{1

2}, Philipp Augenstein², Arne Roennau², Ruediger Dillmann², Zhi Xiong¹

Affiliations

¹ Navigation Research Center, School of Automation Engineering in Nanjing University of Aeronautics and Astronautics, Nanjing, China.
² FZI Research Center for Information Technology, Karlsruhe, Germany.

Abstract

Introduction: With the development of artificial intelligence and brain science, brain-inspired navigation and path planning has attracted widespread attention.

Methods: In this paper, we present a place cell based path planning algorithm that utilizes spiking neural network (SNN) to create efficient routes for drones. First, place cells are characterized by the leaky integrate-and-fire (LIF) neuron model. Then, the connection weights between neurons are trained by spike-timing-dependent plasticity (STDP) learning rules. Afterwards, a synaptic vector field is created to avoid obstacles and to find the shortest path.

Results: Finally, simulation experiments both in a Python simulation environment and in an Unreal Engine environment are conducted to evaluate the validity of the algorithms.

Discussion: Experiment results demonstrate the validity, its robustness and the computational speed of the proposed model.

Keywords: Airsim; navigation; path planning; place cells; spiking neural network.

Grants and funding

This work was supported in part by the National Defense Basic Research Program under Grant JCKY2020605C009, in part by the Aeronautic Science Foundation of China under Grant ASFC-2020Z071052001, and supported by the China Scholarship Council under student Number 202106830082. Part of the work has been supported during a 1 year doctoral internship at FZI.