Frequency response method for terrain classification in autonomous ground vehicles

Abstract

Many autonomous ground vehicle (AGV) missions, such as those related to agricultural applications, search and rescue, or reconnaissance and surveillance, require the vehicle to operate in difficult outdoor terrains such as sand, mud, or snow. To ensure the safety and performance of AGVs on these terrains, a terrain-dependent driving and control system can be implemented. A key first step in implementing this system is autonomous terrain classification. It has recently been shown that the magnitude of the spatial frequency response of the terrain is an effective terrain signature. Furthermore, since the spatial frequency response is mapped by an AGV's vibration transfer function to the frequency response of the vibration measurements, the magnitude of the latter frequency responses also serve as a terrain signature. Hence, this paper focuses on terrain classification using vibration measurements. Classification is performed using a probabilistic neural network, which can be implemented online at relatively high computational speeds. The algorithm is applied experimentally to both an ATRV-Jr and an eXperimental Unmanned Vehicle (XUV) at multiple speeds. The experimental results show the efficacy of the proposed approach.

Investigation into use of piezoelectric sensors in a wheeled robot tire for surface characterization

“…Several published studies demonstrate the potential of using a small wheeled robot to estimate soil parameters [14]. Nagatani et al [44] used a 12 kg robot equipped with four rigid wheels with grousers.…”

Terrain Roughness Identification for High-Speed UGVs

“…Proprioceptive based techniques such as those proposed by Sadhukhan [2], Brooks et al [3], Weiss et al [4], and DuPont et al [5] have used frequency domain vibration information measured from UGV mounted accelerometers along with machine learning techniques to classify terrain into discrete terrain types (e.g. grass, gravel, dirt).…”

Real-time Recognition of the Terrain Configuration to Increase Driving Stability for Unmanned Robots

“…로봇의 시작위치 와 목표위치가 주어진다면 Dynamic A* 알고리즘을 사용 하여 목적지까지의 경로를 계획할 수 있다 [5] . 효율적인 주행 방법이 제시되었다 [6][7][8][9][10][11] . [7][8][9][10] .…”

“…This method is verified by outdoor driving experiments of a real mobile robot. 효율적인 주행 방법이 제시되었다 [6][7][8][9][10][11] . 따라서 영상 센서 와 레이저 거리센서로부터 얻어지는 데이터는 로봇이 직…”