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: 🤖.

FROST∗: Fast robot optimization and simulation toolkit

Abstract: This paper presents FROST, an open-source MATLAB toolkit for modeling, trajectory optimization and simulation of hybrid dynamical systems with a particular focus in dynamic locomotion. The design objective of FROST is to provide a unified software environment for developing model-based control and motion planning algorithms for robotic systems whose dynamics is hybrid in nature. In particular, FROST uses directed graphs to describe the underlying discrete structure of hybrid system models, which renders it capable of representing a wide variety of robotic systems. Equipped with a custom symbolic math toolbox in MATLAB using Wolfram Mathematica, one can rapidly prototype the mathematical model of robot kinematics and dynamics and generate optimized code of symbolic expressions to boost the speed of optimization and simulation in FROST. In favor of agile and dynamic behaviors, we utilize virtual constraint based motion planning and feedback controllers for robotic systems to exploit the full-order dynamics of the model. Moreover, FROST provides a fast and tractable framework for planning optimal trajectories of hybrid dynamical systems using advanced direct collocation algorithms. FROST has been successfully used to synthesize dynamic walking in multiple bipedal robots. Case studies of such applications are considered in this paper, wherein different types of walking gaits are generated for two specific humanoid robots and validated in simulation.

Cooperative Collision Avoidance for Nonholonomic Robots

Abstract: In this paper, we present a method, namely $\epsilon$ CCA, for collision avoidance in dynamic environments among interacting agents, such as other robots or humans. Given a preferred motion by a global planner or driver, the method computes a collision-free local motion for a short time horizon, which respects the actuator constraints and allows for smooth and safe control. The method builds on the concept of reciprocal velocity obstacles and extends it to respect the kinodynamic constraints of the robot and account for a grid-based map representation of the environment. The method is best suited for large multirobot settings, including heterogeneous teams of robots, in which computational complexity is of paramount importance and the robots interact with one another. In particular, we consider a set of motion primitives for the robot and solve an optimization in the space of control velocities with additional constraints. Additionally, we propose a cooperative approach to compute safe velocity partitions in the distributed case. We describe several instances of the method for distributed and centralized operation and formulated both as convex and nonconvex optimizations. We compare the different variants and describe the benefits and tradeoffs both theoretically and in extensive experiments with various robotic platforms: robotic wheelchairs, robotic boats, humanoid robots, small unicycle robots, and simulated cars.

Motion capture based human motion recognition and imitation by direct marker control

Abstract: This paper deals with the imitation of human motions by a humanoid robot based on marker point measurements from a 3D motion capture system. For imitating the humanpsilas motion, we propose a Cartesian control approach in which a set of control points on the humanoid is selected and the robot is virtually connected to the measured marker points via translational springs. The forces according to these springs drive a simplified simulation of the robot dynamics, such that the real robot motion can finally be generated based on joint position controllers effectively managing joint friction and other uncertain dynamics. This procedure allows to make the robot follow the marker points without the need of explicitly computing inverse kinematics. For the implementation of the marker control on a humanoid robot, we combine it with a center of gravity based balancing controller for the lower body joints. We integrate the marker control based motion imitation with the mimesis model, which is a mathematical model for motion learning, recognition, and generation based on hidden Markov models (HMMs). Learning, recognition, and generation of motion primitives are all performed in marker coordinates paving the way for extending these concepts to task space problems and object manipulation. Finally, an experimental evaluation of the presented concepts using a 38 degrees of freedom humanoid robot is discussed.

Robot placement based on reachability inversion

Abstract: Having a representation of the capabilities of a robot is helpful when online queries, such as solving the inverse kinematics (IK) problem for grasping tasks, must be processed efficiently in the real world. When workspace representations, e.g. the reachability of an arm, are considered, additional quality information such as manipulability or self-distance can be employed to enrich the spatial data. In this work we present an approach of inverting such precomputed reachability representations in order to generate suitable robot base positions for grasping. Compared to existing works, our approach is able to generate a distribution in SE(2), the cross-space consisting of 2D position and 1D orientation, that describes potential robot base poses together with a quality index. We show how this distribution can be queried quickly in order to find oriented base poses from which a target grasping pose is reachable without collisions. The approach is evaluated in simulation using the humanoid robot ARMAR-III [1] and an extension is presented that allows to find suitable base poses for trajectory execution.

Robust speech interface based on audio and video information fusion for humanoid HRP-2

Abstract: For cooperative work of robots and humans in the real world, a communicative function based on speech is indispensable for robots. To realize such a function in a noisy real environment, it is essential that robots be able to extract target speech spoken by humans from a mixture of sounds by their own resources. We have developed a method of detecting and extracting speech events based on the fusion of audio and video information. In this method, audio information (sound localization using a microphone array) and video information (human tracking using a camera) are fused by a Bayesian network to enable the detection of speech events. The information of detected speech events is then utilized in sound separation using adaptive beam forming. In this paper, some basic investigations for applying the above system to the humanoid robot HRP-2 are reported. Input devices, namely a microphone array and a camera, were mounted on the head of HRP-2, and acoustic characteristics for sound localization/separation performance were investigated. Also, the human tracking system was improved so that it can be used in a dynamic situation. Finally, overall performance of the system was tested via off-line experiments.