Obstacle/Gap Detection and Terrain Classification of Walking Robots Based on a 2d Laser Range Finder

Kesper, Patrick; Grinke, Eduard; Hesse, Frank; Wörgötter, Florentin; Manoonpong, Poramate

doi:10.1142/9789814525534_0053

Cited by 10 publications

(13 citation statements)

References 3 publications

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“…Regarding the methods to describe the complexity of the terrain, no similar method to describe the complexity of terrain with respect to the hexapod geometry has been introduced before. In the past, methods for describing obstacles in a terrain were only mentioned [30]. Not only does our approach allow for the description of the complexity of a terrain, it also provides description of the size of the obstacle with respect to LW [2,23].…”

Section: Discussionmentioning

confidence: 99%

“…The proposed methods rely on the movement of most insects since they move slowly, and the problem of vertical size of obstacles has been partially solved in [22]. However, vertical movement of the legs is determined exclusively by the height of the obstacles in direction of walking [30] and does not affect the actions (i.e. the strategy of leg coordination of hexapod and the transition from current-state to future-state) determined by the methods described in this article.…”

Section: Discussionmentioning

confidence: 99%

“…Also, we assume that obstacles in a terrain are already identified, e.g. using methods based on recursive density estimation and evolving Takagi-Sugeno fuzzy systems [29] or 2D laser range finder and obstacle/gap detection based on edge detection [30]. An example of a terrain model with complicated distribution of obstacles is shown in Fig.…”

Section: Methods For Terrain Description and Gait Evaluationmentioning

confidence: 99%

“…To verify the designed RL based control system, it is necessary to describe lengths (horizontal sizes, [30]) of the obstacles in the direction of movement to consider only those obstacles which are on the path of the hexapod and corresponding with the section of the complex terrain, i.e. the covered total walking distance l walked .…”

Section: Methods For Terrain Description and Gait Evaluationmentioning

confidence: 99%

“…The maximum possible step length d kSmax in a terrain without obstacles is defined by the LW geometry [2,17,30]. The lengths of small obstacles are smaller or equal to the length of the maximum possible step h d kSmax , Fig.…”

Section: Methods For Terrain Description and Gait Evaluationmentioning

confidence: 99%

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Decision Making Process of Hexapods in a Model of Complex Terrains

2016

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methods For Terrain Description and Gait Evaluationmentioning

confidence: 99%

Section: Methods For Terrain Description and Gait Evaluationmentioning

confidence: 99%

Section: Methods For Terrain Description and Gait Evaluationmentioning

confidence: 99%

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Decision Making Process of Hexapods in a Model of Complex Terrains

2016

APH

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Haptic Feedback with a Reservoir Computing-Based Recurrent Neural Network for Multiple Terrain Classification of a Walking Robot

Borijindakul

Jinuntuya

Manoonpong

2019

Intelligent Robotics and Applications

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Bio-Inspired Adaptive Locomotion Control System for Online Adaptation of a Walking Robot on Complex Terrains

et al. 2020

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Developing a controller that enables a walking robot to autonomously adapt its locomotion to navigate unknown complex terrains is difficult, and the methods developed to address this problem typically require robot kinematics with arduous parameter tuning or machine learning techniques that require several trials or repetitions. To overcome this limitation, in this paper, we present continuous, online, and self-adaptive locomotion control inspired by biological control systems, including neural control and hormone systems. The control approach integrates our existing modular neural locomotion control (MNLC) and a newly introduced artificial hormone mechanism (AHM). While the MNLC can generate various gaits through its modulatory input, the AHM, which replicates the endocrine system, adapts to rapid changes in online walking frequency and gait in response to different complex terrains. The control approach is evaluated on an insect-like hexapod robot with 18 degrees of freedom. We provide the results in three sections. First, we demonstrate the adaptability of the robot with the proposed artificial hormones. Second, we compare the performance of two robots with and without artificial hormones while walking on different complex terrains using three performance indexes (stability, harmony, and displacement). Third, we evaluate real-time online adaptations in the real world through real robot walking on different unknown terrains. The experimental results demonstrate that the robot with the proposed artificial hormones does not require several learning trials to adapt its locomotion. Instead, it can continuously adapt its locomotion online, thereby providing greater success and higher performance than other techniques when walking on all terrains. INDEX TERMS Adaptive behaviors, artificial hormones, central pattern generators, locomotion control, neural networks, walking machines.

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Obstacle/Gap Detection and Terrain Classification of Walking Robots Based on a 2d Laser Range Finder

Cited by 10 publications

References 3 publications

Decision Making Process of Hexapods in a Model of Complex Terrains

Decision Making Process of Hexapods in a Model of Complex Terrains

Haptic Feedback with a Reservoir Computing-Based Recurrent Neural Network for Multiple Terrain Classification of a Walking Robot

Bio-Inspired Adaptive Locomotion Control System for Online Adaptation of a Walking Robot on Complex Terrains

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