Real-time 3D environment perception: An application for small humanoid robots

This chapter is aimed at describing a contemporary bipedal humanoid robot prototyping technology, accompanied with a mathematically rigorous method to generate real-time walking, jumping, and running trajectories that can be applied to this type of robots. The main strategy in this method is to maintain the overall dynamic equilibrium and to prevent undesired rotational actions for the purpose of smooth maneuvering capabilities while the robot is in motion. In order to reach this goal, Zero Moment Point criterion is utilized in spherical coordinates, so that it is possible to fully exploit its properties by the help of Euler’s equations of motions. Such a strategy allows for characterization of the rotational inertia and therefore the associated angular momentum rate change terms, so that undesired torso angle fluctuations during walking and running are well suppressed. It enables prevention of backwards-hopping actions during jumping as well. To validate the proposed approach, the authors performed simulations using a precise 3D simulator and conducted experiments on an actual bipedal robot. Results indicated that the method is superior to classical methods in terms of suppressing undesired rotational actions, such as torso angle fluctuations and backwards-hopping.

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Real-Time Object Detection and Recognition on Low-Compute Humanoid Robots using Deep Learning

2020 6th International Conference on Control, Automation and Robotics (ICCAR) ◽

10.1109/iccar49639.2020.9108054 ◽

2020 ◽

Author(s):

Sayantan Chatterjee ◽

Faheem H. Zunjani ◽

Gora C. Nandi

Keyword(s):

Deep Learning ◽

Object Detection ◽

Real Time ◽

Humanoid Robots ◽

Detection And Recognition

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A Cloud Based Disaster Management System

Journal of Sensor and Actuator Networks ◽

10.3390/jsan9010006 ◽

2020 ◽

Vol 9 (1) ◽

pp. 6 ◽

Cited By ~ 4

Author(s):

Omar Cheikhrouhou ◽

Anis Koubaa ◽

Anis Zarrad

Keyword(s):

Real Time ◽

Disaster Management ◽

Sensor Nodes ◽

Sensor Data ◽

Wireless Sensor ◽

Game Engine ◽

New Paradigm ◽

Lossy Networks ◽

Training Environment ◽

3D Environment

The combination of wireless sensor networks (WSNs) and 3D virtual environments opens a new paradigm for their use in natural disaster management applications. It is important to have a realistic virtual environment based on datasets received from WSNs to prepare a backup rescue scenario with an acceptable response time. This paper describes a complete cloud-based system that collects data from wireless sensor nodes deployed in real environments and then builds a 3D environment in near real-time to reflect the incident detected by sensors (fire, gas leaking, etc.). The system’s purpose is to be used as a training environment for a rescue team to develop various rescue plans before they are applied in real emergency situations. The proposed cloud architecture combines 3D data streaming and sensor data collection to build an efficient network infrastructure that meets the strict network latency requirements for 3D mobile disaster applications. As compared to other existing systems, the proposed system is truly complete. First, it collects data from sensor nodes and then transfers it using an enhanced Routing Protocol for Low-Power and Lossy Networks (RLP). A 3D modular visualizer with a dynamic game engine was also developed in the cloud for near-real time 3D rendering. This is an advantage for highly-complex rendering algorithms and less powerful devices. An Extensible Markup Language (XML) atomic action concept was used to inject 3D scene modifications into the game engine without stopping or restarting the engine. Finally, a multi-objective multiple traveling salesman problem (AHP-MTSP) algorithm is proposed to generate an efficient rescue plan by assigning robots and multiple unmanned aerial vehicles to disaster target locations, while minimizing a set of predefined objectives that depend on the situation. The results demonstrate that immediate feedback obtained from the reconstructed 3D environment can help to investigate what–if scenarios, allowing for the preparation of effective rescue plans with an appropriate management effort.

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Real-Time Whole-Body Imitation by Humanoid Robots and Task-Oriented Teleoperation Using an Analytical Mapping Method and Quantitative Evaluation

Applied Sciences ◽

10.3390/app8102005 ◽

2018 ◽

Vol 8 (10) ◽

pp. 2005 ◽

Cited By ~ 1

Author(s):

Zhijun Zhang ◽

Yaru Niu ◽

Ziyi Yan ◽

Shuyang Lin

Keyword(s):

Real Time ◽

Humanoid Robots ◽

Mapping Method ◽

Whole Body ◽

Mode Switching ◽

Natural Mode ◽

And Performance ◽

Virtual Joints ◽

Task Oriented ◽

And Task

Due to the limitations on the capabilities of current robots regarding task learning and performance, imitation is an efficient social learning approach that endows a robot with the ability to transmit and reproduce human postures, actions, behaviors, etc., as a human does. Stable whole-body imitation and task-oriented teleoperation via imitation are challenging issues. In this paper, a novel comprehensive and unrestricted real-time whole-body imitation system for humanoid robots is designed and developed. To map human motions to a robot, an analytical method called geometrical analysis based on link vectors and virtual joints (GA-LVVJ) is proposed. In addition, a real-time locomotion method is employed to realize a natural mode of operation. To achieve safe mode switching, a filter strategy is proposed. Then, two quantitative vector-set-based methods of similarity evaluation focusing on the whole body and local links, called the Whole-Body-Focused (WBF) method and the Local-Link-Focused (LLF) method, respectively, are proposed and compared. Two experiments conducted to verify the effectiveness of the proposed methods and system are reported. Specifically, the first experiment validates the good stability and similarity features of our system, and the second experiment verifies the effectiveness with which complicated tasks can be executed. At last, an imitation learning mechanism in which the joint angles of demonstrators are mapped by GA-LVVJ is presented and developed to extend the proposed system.

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Immersive 3D Vehicle Simulation for Hardware-in-the-Loop Testing of Mobile Hydraulic Controls

ASME/BATH 2019 Symposium on Fluid Power and Motion Control ◽

10.1115/fpmc2019-1706 ◽

2019 ◽

Author(s):

Matthias Liermann ◽

Christian Feller ◽

Florian Lindinger ◽

Dirk Runge

Keyword(s):

Real Time ◽

Collision Detection ◽

Hydraulic Drive ◽

Hardware In The Loop ◽

High Complexity ◽

Vehicle Simulation ◽

3D Environment ◽

Multi Body ◽

Component Models ◽

Body Dynamic

Abstract The paper presents a HiL test setup for hydraulic propel systems that includes a multi-body dynamic simulation of a vehicle in a realistic 3D environment. It allows testing of driving scenarios under load conditions that would otherwise be very difficult to obtain. The hydraulic-mechanical part of the simulation is modeled in Simulink. An open-source C++ physics engine is used to model the vehicle multi-body mechanics and collision detection between the vehicle and the 3D environment. Despite the high complexity of the hydraulic drive train component models, the constraint of real-time execution of the simulation on a real-time target can be fulfilled.

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