Autonomous terrain characterisation and modelling for dynamic control of unmanned vehicles

Talukder, Ashit; Manduchi, Roberto; Castaño, Rebecca; Owens, K.; Matthies, Larry; Castano, A.; Hogg, Robert

doi:10.1109/irds.2002.1041474

Cited by 45 publications

(38 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typically "learning" applies to perception as in learning a map of the environment [12] or learning a classification of the terrain given image or range data [13]- [15]. In a few cases learning is applied to the control itself as in a vehicle that learns to follow roads based on onboard video cameras while a human drives [16], [17].…”

Section: Related Workmentioning

confidence: 99%

Learning to Drive Among Obstacles

Hamner

Scherer

Singh

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-This paper reports on an outdoor mobile robot that learns to avoid collisions by observing a human driver operate a vehicle equipped with sensors that continuously produce a map of the local environment. We have implemented steering control that models human behavior in trying to avoid obstacles while trying to follow a desired path. Here we present the formulation for this control system and its independent parameters, and then show how these parameters can be automatically estimated by observation of a human driver. We present results from experiments with a vehicle (both real and simulated) that avoids obstacles while following a prescribed path at speeds up to 4 m/sec. We compare the proposed method with another method based on Principal Component Analysis, a commonly used learning technique. We find that the proposed method generalizes well and is capable of learning from a small number of examples.

show abstract

Section: Related Workmentioning

confidence: 99%

Learning to Drive Among Obstacles

Hamner

Scherer

Singh

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

show abstract

“…A search algorithm generated an optimal path from these measures. Similarly in (Seraji and Howard, 2002;Talukder et al, 2002;Huber et al, 1998), shape, texture, and color analysis aided in generating a path traversability measure. These works predict future vehicle-terrain interactions for the purposes of finding a global path and for determining an open-loop locomotion strategy.…”

Section: Related Workmentioning

confidence: 99%

“…Iagnemma et al estimated terrain cohesion and internal friction to calculate drawbar pull (force that pulls a wheel forward) (Iagnemma et al, 2002), and in (Iagnemma et al, 2003), maintained stability using ground contact angle estimates. Talukder et al (Talukder et al, 2002) used a spring-mass model to estimate terrain compliance both to maintain a safe velocity and to predict vehicle dynamics. In each case, the robot required several sensors to estimate terrain conditions, additionally these techniques are applicable to wheeled vehicles only.…”

Section: Related Workmentioning

confidence: 99%

Terrain Classification Using Weakly-Structured Vehicle/Terrain Interaction

2005

View full text Add to dashboard Cite

Abstract. We present a new terrain classification technique both for effective, autonomous locomotion over rough, unknown terrains and for the qualitative analysis of terrains for exploration and mapping. Our approach requires a single camera with little processing of visual information. Specifically, we derived a gait bounce measure from visual servoing errors that results from vehicle-terrain interactions during normal locomotion. Characteristics of the terrain, such as roughness and compliance, manifest themselves in the spatial patterns of this signal and can be extracted using pattern classification techniques. This vision-based approach is particularly beneficial for resource-constrained robots with limited sensor capability. In this paper, we present the gait bounce derivation. We demonstrate the viability of terrain classification for legged vehicles using gait bounce with a rigorous study of more than 700 trials, obtaining an 83% accuracy on a set of laboratory terrains. We describe how terrain classification may be used for gait adaptation, particularly in relation to an efficiency metric. We also demonstrate that our technique may be generally applicable to other locomotion mechanisms such as wheels and treads.

show abstract

“…Many researchers have approached the rough terrain navigation problem by creating terrain representations from sensor information and then using a vehicle model to make predictions of the future vehicle trajectory to determine safe control actions [1,2,3,4]. These techniques have been successful on rolling terrain with discrete obstacles and have shown promise in more cluttered environments, but handling vegetation remains a challenge.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…A grassy area on a steep slope may be dangerous to drive on whereas the same grass on a flat area could be easily traversable. Researchers have modeled the statistics of laser data in grass to find hard objects [5], assigned spring models to different terrain classes to determine traversability using a simple dynamic analysis [4], and kept track of the ratio of laser hits to laser passthroughs to determine the ground surface in vegetation [3].…”

Section: Introduction and Related Workmentioning

confidence: 99%