This paper presents a Center of Mass (CoM) based manipulation and regrasp planner that implements stability constraints to preserve the robot balance. The planner provides a graph of IK-feasible, collision-free and stable motion sequences, constructed using an energy based motion planning algorithm. It assures that the assembly motions are stable and prevent the robot from falling while performing dexterous tasks in different situations. Furthermore, the constraints are also used to perform an RRT-inspired task-related stability estimation in several simulations. The estimation can be used to select between single-arm and dual-arm regrasping configurations to achieve more stability and robustness for a given manipulation task. To validate the planner and the task-related stability estimations, several tests are performed in simulations and real-world experiments involving the HRP5P humanoid robot, the 5th generation of the HRP robot family. The experiment results suggest that the planner and the task-related stability estimation provide robust behavior for the humanoid robot while performing regrasp tasks.
In this paper we consider the motion planning problem of humanoid with articulated-spine for object manipulation in double support phase. Complexity of motion planning increases due to higher degrees-of-freedom (DOF), redundancy arising due to articulated spine, inherent underactuation and stability constraint. Additionally, loop-closure constraints arise during double-support phase, and self-collision constraints exist independent of the task to be performed. These problems make motion planning of humanoids a challenging task. In this work, we address the above issues by proposing a sampling based approach for planning the motion of the humanoid. Our approach to the problem is based on RRT* which can generate asymptotically optimal paths. The proposed approach deals with the stability constraints by rejective sampling approach which divides random configurations generated into valid and invalid configurations. Additionally, the loop-closure constraints are tackled by separating the humanoid into two open-kinematic sub-chains, and then generating random configurations in one sub-chain whereas the remaining sub-chain uses inverse kinematics for closure. Efficacy of the proposed approach is demonstrated for whole-body motion planning of a 25-DOF humanoid in generating asymptotically optimal end-effector paths. What would humans be like if nature had invented the wheel? Golem Krang is a novel humanoid torso designed at Georgia Tech. The robot dynamically transforms from a.5 m static to a 1.5 m dynamic configuration. Our robot development has led to two advances in the design of platforms for mobility and manipulation: (1) A 2-DOF robot base that autonomously stands from horizontal rest; (2) A 4-DOF humanoid torso that adds a waist roll joint to replicate human torso folding and a yaw joint for spine rotation. The mobile torso also achieves autonomous standing in a constrained space while lifting a 40 kg payload. Golem validates our assertions by consistently achieving static-dynamic transformations. This paper describes the design of our mobile torso. It considers a number of factors including its suitability for human environments, mechanical simplicity and the ability to store potential and kinetic energy for handling heavy human and even super-human tasks. This paper studies the balance of a humanoid robot carrying a heavy object. Without knowing the mass and the position of the center of gravity of the object, the humanoid robot carries a heavy object stably by using the force sensor information attached at the wrists and the ankles. We first show how to generate the motion of a humamoid robot by taking the force sensor information into consideration. We also show the method for generating the gait pattern by taking the dynamics of the carried object. The effectiveness of the proposed method is shown by experiments. This paper discusses a strategy for a humanoid robot to pull a fire hose while walking towards a desired position and orientation. A hybrid controller on the robot's wrist holding the fire hose is implemented for pulling it. The proposed controller can automatically determine the pulling force according to the robot's walking velocity. Through simulation analysis it is shown that when the robot walks while pulling the fire hose a drift in the walking direction is generated. To cope with this drift and to direct the robot to a desired position and orientation, a walking task is introduced. Using a motion capture system, the robot's chest position and orientation are monitored and feed to the robot's walking pattern generator to correct the orientation drift and to determine where to walk and when to stop walking. Through experimental results the validity of the proposed strategy was confirmed. It is shown that the proposed hybrid controller contributes to the improvement of the robot's balance when walking.