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For a legged robot that walks on rough terrain it is very important to avoid falling down while executing its mission. For this purpose, maintaining the six legged robot statically stable is considered an important issue and challenging task. This paper will shed light on the applied approach for detecting the direction of ground inclination, obstacle avoidance as well as the effect of amputation on statically stable up- and downhill walking based on evaluating the local current consumption and angular position of each leg’s joint. The body posture will be adjusted in relation to the direction of inclination. Our approach is based on an organic computing architecture and was tested on a low-cost version of the OSCAR robot.

Improving robot walking over compliant surfaces is considered an important issue. However, walking on an uneven surface requires controlling the robot‘s leg position to span a varying distance between the robot’s body and the ground. This paper will shed light on the applied decentralized controller approach that enables the robot‘s leg to adapt its position to be commensurate with the type of walking surface in order to improve walking on compliant surfaces. When the substrate compliance is high (sandy ground), the leg has to correct its position close to the point of contact with the ground. When the substrate compliance is low (gravel ground), the correction of the leg position is low. The novelty of our approach is the evaluation of the local current consumption and the angular position of each leg’s joint as somatosensory feedback. Our approach is based on an organic computing architecture and was tested on a low-cost version of the OSCAR walking robot.

Walking on uphill sandy surface is a challenging task and is considered one of the most difficult terrains that a walking robot can face. This paper will shed light on our applied decentralized approach to enable a hexapod robot to walk on uphill sandy surface effectively. In this paper we will show that the combination between our previously introduced approaches and our new strategy enables the hexapod robot to cope with this type of terrain. The suggested strategy is based on the synchronization between the moved legs that are touching the ground. This synchronization provides the robot's legs with the sufficient driving force during uphill walking. The novelty of our approach is the only evaluation of the local current consumption and angular position of each leg's joint as somatosensory feedback. It is based on an organic computing architecture and was tested on a low-cost version of the OSCAR walking robot.