Abstract
For household humanoid robots, reaching as much workspace as possible with their hands is an important issue because the locations of target objects may range from the floor to above the robot’s head. At the same time, to adapt to the constantly-changing household environment, inverse kinematics for the whole body must be solved in real time. In this paper, to achieve real-time motion generation for a humanoid robot, we propose a method of separating the inverse kinematics calculation into simpler problems. Using regression to estimate the torso orientation, we independently solve inverse kinematics for the lower body and both arms. First, using the target pose of both hands as input, we calculate the orientation of the torso and determine the target position of the center of mass considering the reachability of both arms. At each control step, we calculate the joint angles of the lower body from the position of the center of mass, feet poses, and torso orientation. Then, we calculate the joint angles of both arms. In experiments, we apply the proposed method to a human-size humanoid robot for reaching low-height positions while hunkering down. The proposed inverse kinematics solver is ten times faster than the numerical solution using the Jacobian matrix. We also verify the applicability of the proposed method using a sequence of random target positions for the hands as input.
Highlights
There are many research efforts aimed at enabling robots to perform household tasks such as cooking and serving food in daily-life environments [1–5]
Comparing the calculation times using a dynamics simulation We show the effectiveness of the whole-body controller using the humanoid robot HRP-4 in the dynamics simulator OpenHRP [26]
The Jacobian matrix has 28 rows corresponding to the degree(s) of freedom (DoF) and 24 columns corresponding to the position of both hands, the center of mass (CoM), and the orientation of both hands, the torso, and the swing leg
Summary
There are many research efforts aimed at enabling robots to perform household tasks such as cooking and serving food in daily-life environments [1–5]. In real-robot experiments, we verify that the proposed method generates whole-body motions to reach low-height positions while hunkering down and keeping balanced. Using our method, both the IK calculations and the robot’s movements take three s on average. Nishiwaki et al proposed whole-body motion generation for reaching an object using one arm [15] They tested their method with a real robot grasping an object on the floor. Estimation of torso posture For our proposed whole-body motion generation approach, we use regression to estimate the orientation of the torso The inputs for this regression are the positions of both arms, i.e., (x1, y1, z1) and (x2, y2, z2). It turns out that the most time-consuming process is the SVR calculation
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