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RGB-D terrain perception and dense mapping for legged robots

100%

Belter D., Łabecki P., Fankhauser P., Siegwart R.

International Journal of Applied Mathematics and Computer Science

2016

tom 26

nr 1

81-97

This paper addresses the issues of unstructured terrain modeling for the purpose of navigation with legged robots. We present an improved elevation grid concept adopted to the specific requirements of a small legged robot with limited perceptual capabilities. We propose an extension of the elevation grid update mechanism by incorporating a formal treatment of the spatial uncertainty. Moreover, this paper presents uncertainty models for a structured light RGB-D sensor and a stereo vision camera used to produce a dense depth map. The model for the uncertainty of the stereo vision camera is based on uncertainty propagation from calibration, through undistortion and rectification algorithms, allowing calculation of the uncertainty of measured 3D point coordinates. The proposed uncertainty models were used for the construction of a terrain elevation map using the Videre Design STOC stereo vision camera and Kinect-like range sensors. We provide experimental verification of the proposed mapping method, and a comparison with another recently published terrain mapping method for walking robots.

A biologically inspired approach to feasible gait learning for a hexapod robot

88%

Belter D., Skrzypczyński P.

International Journal of Applied Mathematics and Computer Science

2010

tom 20

nr 1

69-84

The objective of this paper is to develop feasible gait patterns that could be used to control a real hexapod walking robot. These gaits should enable the fastest movement that is possible with the given robot's mechanics and drives on a flat terrain. Biological inspirations are commonly used in the design of walking robots and their control algorithms. However, legged robots differ significantly from their biological counterparts. Hence we believe that gait patterns should be learned using the robot or its simulation model rather than copied from insect behaviour. However, as we have found tahula rasa learning ineffective in this case due to the large and complicated search space, we adopt a different strategy: in a series of simulations we show how a progressive reduction of the permissible search space for the leg movements leads to the evolution of effective gait patterns. This strategy enables the evolutionary algorithm to discover proper leg co-ordination rules for a hexapod robot, using only simple dependencies between the states of the legs and a simple fitness function. The dependencies used are inspired by typical insect behaviour, although we show that all the introduced rules emerge also naturally in the evolved gait patterns. Finally, the gaits evolved in simulations are shown to be effective in experiments on a real walking robot.

Ograniczanie wyników

2 International Journal of Applied Mathematics and Computer Science

2 Belter D.

1 Fankhauser P.

1 Siegwart R.

1 Skrzypczyński P.

1 Łabecki P.

1 2016

1 2010

Wyniki wyszukiwania

RGB-D terrain perception and dense mapping for legged robots

A biologically inspired approach to feasible gait learning for a hexapod robot