Position-Posture Control of Multilegged Walking Robot Based on Kinematic Correction

Posture-position control is the fundamental technology among multilegged robots as it is hard to get an effective control on rough terrain. These robots need to constantly adjust the position-posture of its body to move stalely and flexibly. However, the actual footholds of the robot constantly changing cause serious errors during the position-posture control process because their foot-ends are basically in nonpoint contact with the ground. Therefore, a position-posture control algorithm for multilegged robots based on kinematic correction is proposed in this paper. Position-posture adjustment is divided into two independent motion processes: robot body position adjustment and posture adjustment. First, for the two separate adjustment processes, the positions of the footholds relative to the body are obtained and their positions relative to the body get through motion synthesis. Then, according to the modified inverse kinematics solution, the joint angles of the robot are worked out. Unlike the traditional complex closed-loop position-posture control of the robot, the algorithm proposed in this paper can achieve the purpose of reducing errors in the position-posture adjustment process of the leg-foot robot through a simple and general kinematic modification. Finally, this method is applied in the motion control of a bionic hexapod robot platform with a hemispherical foot-end. A comparison experiment of linear position-posture change on the flat ground shows that this method can reduce the attitude errors, especially the heading error reduced by 55.46%.

SURF-BRISK–Based Image Infilling Method for Terrain Classification of a Legged Robot

In this study, we propose adaptive locomotion for an autonomous multilegged walking robot, an image infilling method for terrain classification based on a combination of speeded up robust features, and binary robust invariant scalable keypoints (SURF-BRISK). The terrain classifier is based on the bag-of-words (BoW) model and SURF-BRISK, both of which are fast and accurate. The image infilling method is used for identifying terrain with obstacles and mixed terrain; their features are magnified to help with recognition of different complex terrains. Local image infilling is used to improve low accuracy caused by obstacles and super-pixel image infilling is employed for mixed terrain. A series of experiments including classification of terrain with obstacles and mixed terrain were conducted and the obtained results show that the proposed method can accurately identify all terrain types and achieve adaptive locomotion.

No Need for a Body Model: Positive Velocity Feedback for the Control of an 18-DOF Robot Walker

In a multilegged walking robot several legs usually have ground contact and thereby form a closed kinematic chain. The control of such a system is generally assumed to require the explicit calculation of the body kinematics. Such a computation requires knowledge concerning all relevant joint angles as well as the segment lengths. Here, we propose a biologically inspired solution that does not need such a body model. This is done by using implicit communication through the body mechanics (embodiment) and a local positive velocity feedback strategy (LPVF) on the single joint level. In this control scheme the locally measured joint velocity of an elastic joint is fed into the same joint during the next time step to maintain the movement. At the same time, an additional part of this joint controller observes the mechanical joint power to confine the positive feedback. This solution does not depend on changes of the geometry, e.g. length of individual segments, and allows for a simple solution of negotiation of curves. The principle is tested in a dynamics simulation on a six-legged walker and, for the first time, also on a real robot.