Humanoid robot

About: Humanoid robot is a research topic. Over the lifetime, 14387 publications have been published within this topic receiving 243674 citations. The topic is also known as: 🤖.

ZMP-Based Biped Running Control

Abstract: In this article, a running humanoid robot, HRP-2LR, was presented along with its running pattern generation, its controller, and the experimental result. Applying the proposed controller, HRP-2LR could successfully run with average speed of 0.16 m/s, repeating flight phases of 0.06 s and support phases of 0.3 s. Future work consists of the realization of faster running and online running pattern generation.

Perception-less terrain adaptation through whole body control and hierarchical optimization

Abstract: This paper presents a control approach based on a whole body control framework combined with hierarchical optimization. Locomotion is formulated as multiple tasks (e.g. maintaining balance or tracking a desired motion of one of the limbs) which are solved in a prioritized way using QP solvers. It is shown how complex locomotion behaviors can purely emerge from robot-specific inequality tasks (i.e. torque or reaching limits) together with the optimization of balance and system manipulability. Without any specific motion planning, this prioritized task optimization leads to a natural adaption of the robot to the terrain while walking and hence enables blind locomotion over rough grounds. The presented framework is implemented and successfully tested on ANYmal, a torque controllable quadrupedal robot. It enables the machine to walk while accounting for slippage and torque limitation constraints, and even step down from an unperceived 14 cm obstacle. Thereby, ANYmal exploits the maximum reach of the limbs and automatically adapts the body posture and height.

Non-gaited humanoid locomotion planning

Abstract: This paper presents a non-gaited motion planner for humanoid robots navigating very uneven and sloped terrain The planner allows contact with any pre-designated part of the robot's body, since the use of hands or knees (in addition to feet) may be required to balance It uses a probabilistic, sample-based approach to compute each step One challenge of this approach is that most randomly sampled configurations do not satisfy all motion constraints (closed-chain, equilibrium, collision) To address this problem, a method of iterative constraint enforcement is presented that samples feasible configurations much more quickly Example motions planned for the humanoid robot HRP-2 are shown in simulation

Manipulation planning with caging grasps

Abstract: We present a novel motion planning algorithm for performing constrained tasks such as opening doors and drawers by robots such as humanoid robots or mobile manipulators. Previous work on constrained manipulation transfers rigid constraints imposed by the target object motion directly into the robot configuration space. This often unnecessarily restricts the allowable robot motion, which can prevent the robot from performing even simple tasks, particularly if the robot has limited reachability or low number of joints. Our method computes ldquocaging graspsrdquo specific to the object and uses efficient search algorithms to produce motion plans that satisfy the task constraints. The major advantages of our technique significantly increase the range of possible motions of the robot by not having to enforce rigid constraints between the end-effector and the target object. We illustrate our approach with experimental results and examples running on two robot platforms.

Online footstep planning for humanoid robots

Abstract: We present an online algorithm for planning sequences of footstep locations that encode goal-directed navigation strategies for humanoid robots. Planning footsteps is more general than most existing navigation methods designed for wheeled robots, since the options of stepping over or upon obstacles in a cluttered terrain are available. Given a discrete set of plausible footstep locations, a forward dynamic programming approach is used to compute a footstep sequence to a specified goal location in the environment. Heuristics designed to minimize the number and complexity of the step motions are used to encode cost functions used for searching a footstep transition graph. If successful, the planner returns an optimal sequence of footstep locations according to the cost functions and plausible sets of footstep locations defined. We show results from an experimental implementation of the algorithm running on the H7 humanoid robot. Using a stereo vision system to sense obstacles in the immediate environment and identify a target goal location, the robot updates the current optimal footstep sequence to the goal from its present location.