scholarly journals Torso height optimization for bipedal locomotion

2018 ◽  
Vol 15 (5) ◽  
pp. 172988141880444 ◽  
Author(s):  
Arne-Christoph Hildebrandt ◽  
Konstantin Ritt ◽  
Daniel Wahrmann ◽  
Robert Wittmann ◽  
Felix Sygulla ◽  
...  
Keyword(s):  
Real Time ◽  
Kinematic Model ◽  
Bipedal Locomotion ◽  
Full Potential ◽  
Normal Walking ◽  
Kinematic Range ◽  
Bipedal Robots ◽  
Uneven Terrain ◽  
2D Model

Bipedal robots can be better alternatives to other robots in certain applications, but their full potential can only be used if their entire kinematic range is cleverly exploited. Generating motions that are not only dynamically feasible but also take into account the kinematic limits as well as collisions in real time is one of the main challenges towards that goal. We present an approach to generate adaptable torso height trajectories to exploit the full kinematic range in bipedal locomotion. A simplified 2D model approximates the robot’s full kinematic model for multiple steps ahead. It is used to optimize the torso height trajectories while taking future motion kinematics into account. The method significantly improves the robot’s motion not only while walking in uneven terrain, but also during normal walking. Furthermore, we integrated the method in our framework for autonomous walking and we validated its real-time character in successfully conducted experiments.

scholarly journals Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

2021 ◽  
pp. 036119812199714
Author(s):  
Gabriel Wilkes ◽  
Roman Engelhardt ◽  
Lars Briem ◽  
Florian Dandl ◽  
Peter Vortisch ◽  
...  
Keyword(s):  
Real Time ◽  
Travel Demand ◽  
State Of The Art ◽  
Choice Model ◽  
Mode Choice ◽  
Attractive Alternative ◽  
Full Potential ◽  
Demand Model ◽  
Modeling Framework ◽  
Demand And Supply

This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.

scholarly journals Terrain-Perception-Free Quadrupedal Spinning Locomotion on Versatile Terrains: Modeling, Analysis, and Experimental Validation

2021 ◽  
Vol 8 ◽  
Author(s):  
Hongwu Zhu ◽  
Dong Wang ◽  
Nathan Boyd ◽  
Ziyi Zhou ◽  
Lecheng Ruan ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Center Of Mass ◽  
Kinematic Model ◽  
Linear Quadratic Regulator ◽  
Small Scale ◽  
Small Range ◽  
Linear Quadratic ◽  
Stability Margins ◽  
Uneven Terrain ◽  
Quadruped Robots

Dynamic quadrupedal locomotion over rough terrains reveals remarkable progress over the last few decades. Small-scale quadruped robots are adequately flexible and adaptable to traverse uneven terrains along the sagittal direction, such as slopes and stairs. To accomplish autonomous locomotion navigation in complex environments, spinning is a fundamental yet indispensable functionality for legged robots. However, spinning behaviors of quadruped robots on uneven terrain often exhibit position drifts. Motivated by this problem, this study presents an algorithmic method to enable accurate spinning motions over uneven terrain and constrain the spinning radius of the center of mass (CoM) to be bounded within a small range to minimize the drift risks. A modified spherical foot kinematics representation is proposed to improve the foot kinematic model and rolling dynamics of the quadruped during locomotion. A CoM planner is proposed to generate a stable spinning motion based on projected stability margins. Accurate motion tracking is accomplished with linear quadratic regulator (LQR) to bind the position drift during the spinning movement. Experiments are conducted on a small-scale quadruped robot and the effectiveness of the proposed method is verified on versatile terrains including flat ground, stairs, and slopes.

Lifting Operations with Wind Turbine Installation Jack-Ups Using Real-Time Data

2021 ◽  
Author(s):  
Kayo Vanderheggen ◽  
Joost Janssen ◽  
Nate Meredith
Keyword(s):  
Wind Turbine ◽  
Real Time ◽  
Support System ◽  
High Load ◽  
Full Potential ◽  
Catastrophic Failure ◽  
Time Data ◽  
Real Time Data ◽  
Jack Up ◽  
Turbine Installation

When a wind turbine installation jack-up performs a heavy lifting operation with the crane it affects the loads on the foundation. For these units the crane typically encircles a leg or is positioned close to it. Consequently, that leg attracts most of the loads due to crane operations. For each location jack-ups prove the capacity of the foundation by applying a controlled, high load at each of the footings before commencing operations. This process is known as preloading. The achieved preload at the jack-up’s foundation determines the operational limit. Exceedance of the preload value may result in foundation instability. Depending on the site’s foundation characteristics the consequences of such an exceedance range from negligible to catastrophic failure. GustoMSC has developed Operator Support System (OSS) software with the purpose to make the operator aware of the limitations imposed by the preloaded foundation. The application outlines operational limits based on real-time data from the jack-up, jacking system and crane which enables the operators to safely unlock the full potential of their wind turbine installation jack-up.

scholarly journals Pose Prediction of Autonomous Full Tracked Vehicle Based on 3D Sensor

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5120 ◽  
Author(s):  
Tao Ni ◽  
Wenhang Li ◽  
Hongyan Zhang ◽  
Haojie Yang ◽  
Zhifei Kong
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Experimental Result ◽  
Pose Prediction ◽  
Tracked Vehicle ◽  
Position Information ◽  
Cloud Data ◽  
Uneven Terrain ◽  
The Stability ◽  
Safety And Stability

Autonomous vehicles can obtain real-time road information using 3D sensors. With road information, vehicles avoid obstacles through real-time path planning to improve their safety and stability. However, most of the research on driverless vehicles have been carried out on urban even driveways, with little consideration of uneven terrain. For an autonomous full tracked vehicle (FTV), the uneven terrain has a great impact on the stability and safety. In this paper, we proposed a method to predict the pose of the FTV based on accurate road elevation information obtained by 3D sensors. If we could predict the pose of the FTV traveling on uneven terrain, we would not only control the active suspension system but also change the driving trajectory to improve the safety and stability. In the first, 3D laser scanners were used to get real-time cloud data points of the terrain for extracting the elevation information of the terrain. Inertial measurement units (IMUs) and GPS are essential to get accurate attitude angle and position information. Then, the dynamics model of the FTV was established to calculate the vehicle’s pose. Finally, the Kalman filter was used to improve the accuracy of the predicted pose. Compared to the traditional method of driverless vehicles, the proposed approach was more suitable for autonomous FTV. The real-world experimental result demonstrated the accuracy and effectiveness of our approach.

scholarly journals Road Profile Estimation Using a 3D Sensor and Intelligent Vehicle

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3676 ◽  
Author(s):  
Tao Ni ◽  
Wenhang Li ◽  
Dingxuan Zhao ◽  
Zhifei Kong
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Active Suspension ◽  
Suspension System ◽  
Global Navigation Satellite Systems ◽  
Cloud Data ◽  
Uneven Terrain ◽  
Road Profile ◽  
Data Points ◽  
Profile Estimation

Autonomous vehicles can achieve accurate localization and real-time road information perception using sensors such as global navigation satellite systems (GNSSs), light detection and ranging (LiDAR), and inertial measurement units (IMUs). With road information, vehicles can navigate autonomously to a given position without traffic accidents. However, most of the research on autonomous vehicles has paid little attention to road profile information, which is a significant reference for vehicles driving on uneven terrain. Most vehicles experience violent vibrations when driving on uneven terrain, which reduce the accuracy and stability of data obtained by LiDAR and IMUs. Vehicles with an active suspension system, on the other hand, can maintain stability on uneven roads, which further guarantees sensor accuracy. In this paper, we propose a novel method for road profile estimation using LiDAR and vehicles with an active suspension system. In the former, 3D laser scanners, IMU, and GPS were used to obtain accurate pose information and real-time cloud data points, which were added to an elevation map. In the latter, the elevation map was further processed by a Kalman filter algorithm to fuse multiple cloud data points at the same cell of the map. The model predictive control (MPC) method is proposed to control the active suspension system to maintain vehicle stability, thus further reducing drifts of LiDAR and IMU data. The proposed method was carried out in outdoor environments, and the experiment results demonstrated its accuracy and effectiveness.

scholarly journals An Autonomous Path Controller in a System on Chip for Shrimp Robot

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 441 ◽  
Author(s):  
Sergio Barrios-dV ◽  
Michel Lopez-Franco ◽  
Jorge D. Rios ◽  
Nancy Arana-Daniel ◽  
Carlos Lopez-Franco ◽  
...  
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Trajectory Tracking ◽  
Neural Control ◽  
Tracking System ◽  
Kinematic Model ◽  
System On Chip ◽  
Optimal Controller ◽  
Real Time System ◽  
On Chip

This paper presents a path planning and trajectory tracking system for a BlueBotics Shrimp III®, which is an articulate mobile robot for rough terrain navigation. The system includes a decentralized neural inverse optimal controller, an inverse kinematic model, and a path-planning algorithm. The motor control is obtained based on a discrete-time recurrent high order neural network trained with an extended Kalman filter, and an inverse optimal controller designed without solving the Hamilton Jacobi Bellman equation. To operate the whole system in a real-time application, a Xilinx Zynq® System on Chip (SoC) is used. This implementation allows for a good performance and fast calculations in real-time, in a way that the robot can explore and navigate autonomously in unstructured environments. Therefore, this paper presents the design and implementation of a real-time system for robot navigation that integrates, in a Xilinx Zynq® System on Chip, algorithms of neural control, image processing, path planning, and inverse kinematics and trajectory tracking.

scholarly journals Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4067 ◽  
Author(s):  
Fabio A. A. Andrade ◽  
Anthony Hovenburg ◽  
Luciano Netto de de Lima ◽  
Christopher Dahlin Rodin ◽  
Tor Arne Johansen ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Predictive Control ◽  
Kinematic Model ◽  
Search And Rescue ◽  
International Standards ◽  
Area Of Interest ◽  
Aerial Vehicles ◽  
Human Operators

Unmanned Aerial Vehicles (UAVs) have recently been used in a wide variety of applications due to their versatility, reduced cost, rapid deployment, among other advantages. Search and Rescue (SAR) is one of the most prominent areas for the employment of UAVs in place of a manned mission, especially because of its limitations on the costs, human resources, and mental and perception of the human operators. In this work, a real-time path-planning solution using multiple cooperative UAVs for SAR missions is proposed. The technique of Particle Swarm Optimization is used to solve a Model Predictive Control (MPC) problem that aims to perform search in a given area of interest, following the directive of international standards of SAR. A coordinated turn kinematic model for level flight in the presence of wind is included in the MPC. The solution is fully implemented to be embedded in the UAV on-board computer with DUNE, an on-board navigation software. The performance is evaluated using Ardupilot’s Software-In-The-Loop with JSBSim flight dynamics model simulations. Results show that, when employing three UAVs, the group reaches 50% Probability of Success 2.35 times faster than when a single UAV is employed.

Heading Estimation Based on Magnetic Markers for Intelligent Vehicles

2016 ◽  
Vol 138 (7) ◽  
Author(s):  
Yeun Sub Byun ◽  
Young Chol Kim
Keyword(s):  
Real Time ◽  
Autonomous Vehicle ◽  
Kinematic Model ◽  
Estimation Method ◽  
Intelligent Vehicles ◽  
Experimental Results ◽  
Main Concept ◽  
Heading Angle ◽  
Magnetic Markers ◽  
Heading Estimation

This paper presents a new real-time heading estimation method for an all-wheel steered single-articulated autonomous vehicle guided by a magnetic marker system. To achieve good guidance control for the vehicle, precise estimation of the position and heading angle during travel is necessary. The main concept of this study is to estimate the heading angle from the relative orientations of the magnetic markers and the vehicle motion. To achieve this, a kinematic model of the all-wheel steered vehicle is derived and combined with the motion of a magnetic ruler mounted near each axle underneath the vehicle. The position coordinates and polarities of the magnetic markers, which are provided a priori, are used to determine the vehicle position at every detection instance. A gyroscope is employed to assist real-time heading estimation at sample times when there are no marker detection data. The proposed method was tested on a real vehicle and evaluated by comparing the experimental results with those of the differential global positioning system (DGPS) in real-time kinematics (RTK) mode. Experimental results show that the proposed method exhibits good performance for heading estimation.

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