scholarly journals Active Exploration for Obstacle Detection on a Mobile Humanoid Robot

Actuators ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 205
Author(s):  
Luca Nobile ◽  
Marco Randazzo ◽  
Michele Colledanchise ◽  
Luca Monorchio ◽  
Wilson Villa ◽  
...  
Keyword(s):  
Humanoid Robot ◽  
Control Strategies ◽  
Obstacle Detection ◽  
Depth Information ◽  
Lidar Data ◽  
Optimization Strategy ◽  
Navigation Path ◽  
Active Exploration ◽  
The Given ◽  
Blind Spots

Conventional approaches to robot navigation in unstructured environments rely on information acquired from the LiDAR mounted on the robot base to detect and avoid obstacles. This approach fails to detect obstacles that are too small, or that are invisible because they are outside the LiDAR’s field of view. A possible strategy is to integrate information from other sensors. In this paper, we explore the possibility of using depth information from a movable RGB-D camera mounted on the head of the robot, and investigate, in particular, active control strategies to effectively scan the environment. Existing works combine RGBD-D and 2D LiDAR data passively by fusing the current point-cloud from the RGB-D camera with the occupancy grid computed from the 2D LiDAR data, while the robot follows a given path. In contrast, we propose an optimization strategy that actively changes the position of the robot’s head, where the camera is mounted, at each point of the given navigation path; thus, we can fully exploit the RGB-D camera to detect, and hence avoid, obstacles undetected by the 2D LiDAR, such as overhanging obstacles or obstacles in blind spots. We validate our approach in both simulation environments to gather statistically significant data and real environments to show the applicability of our method to real robots. The platform used is the humanoid robot R1.

scholarly journals FUSION OF HYPERSPECTRAL, MULTISPECTRAL, COLOR AND 3D POINT CLOUD INFORMATION FOR THE SEMANTIC INTERPRETATION OF URBAN ENVIRONMENTS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1899-1906 ◽  
Author(s):  
M. Weinmann ◽  
M. Weinmann
Keyword(s):  
3D Structure ◽  
Urban Environments ◽  
Hyperspectral Data ◽  
Semantic Interpretation ◽  
Lidar Data ◽  
Data Set ◽  
Feature Sets ◽  
Sensor Platforms ◽  
The Given ◽  
Color Imagery

<p><strong>Abstract.</strong> In this paper, we address the semantic interpretation of urban environments on the basis of multi-modal data in the form of RGB color imagery, hyperspectral data and LiDAR data acquired from aerial sensor platforms. We extract radiometric features based on the given RGB color imagery and the given hyperspectral data, and we also consider different transformations to potentially better data representations. For the RGB color imagery, these are achieved via color invariants, normalization procedures or specific assumptions about the scene. For the hyperspectral data, we involve techniques for dimensionality reduction and feature selection as well as a transformation to multispectral Sentinel-2-like data of the same spatial resolution. Furthermore, we extract geometric features describing the local 3D structure from the given LiDAR data. The defined feature sets are provided separately and in different combinations as input to a Random Forest classifier. To assess the potential of the different feature sets and their combination, we present results achieved for the MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set.</p>

Supervoxel Plane Segmentation and Multi-Contact Motion Generation for Humanoid Stair Climbing

2017 ◽  
Vol 14 (01) ◽  
pp. 1650022 ◽  
Author(s):  
Tianwei Zhang ◽  
Stéphane Caron ◽  
Yoshihiko Nakamura
Keyword(s):  
Motion Planning ◽  
Humanoid Robot ◽  
Real Life ◽  
Estimation Method ◽  
Humanoid Robots ◽  
Stair Climbing ◽  
Lidar Data ◽  
Motion Generation ◽  
Prior Models ◽  
Motion Generator

Stair climbing is still a challenging task for humanoid robots, especially in unknown environments. In this paper, we address this problem from perception to execution. Our first contribution is a real-time plane-segment estimation method using Lidar data without prior models of the staircase. We then integrate this solution with humanoid motion planning. Our second contribution is a stair-climbing motion generator where estimated plane segments are used to compute footholds and stability polygons. We evaluate our method on various staircases. We also demonstrate the feasibility of the generated trajectories in a real-life experiment with the humanoid robot HRP-4.

Validation, optimisation and comparison of carbon dioxide-based occupancy estimation algorithms

2019 ◽  
Vol 29 (6) ◽  
pp. 820-834
Author(s):  
Felix Nienaber ◽  
Sebastian Wolf ◽  
Mark Wesseling ◽  
Davide Calì ◽  
Dirk Müller ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Air Conditioning ◽  
Control Strategies ◽  
Fixed Time ◽  
Mass Balance Equation ◽  
Estimation Algorithms ◽  
Detection Algorithms ◽  
Occupancy Detection ◽  
Occupancy Estimation ◽  
The Given

The operation of heating, cooling and air-conditioning (HVAC) in buildings often adheres to fixed time schedules. However, associating HVAC schedules to the occupant’s presence patterns can save a significant amount of energy, reducing operation periods to the required minimum. Therefore, automated occupancy estimation provides valuable input to efficient building control strategies. This work discusses the validation and adjustment for two carbon dioxide-based occupancy detection algorithms based on data from ten multi-person offices. Both methods are based on a carbon dioxide mass balance equation. However, they follow two different philosophies. One model is deterministic and includes a more detailed representation of the system, whereas the other model includes stochastic elements and was based on fewer assumptions. Both approaches show similar and promising results. The advantages and drawbacks of each method are reviewed. Furthermore, adjustments of the algorithms to the given conditions and possible future improvements are discussed.

scholarly journals An Indoor Obstacle Detection System Using Depth Information and Region Growth

Sensors ◽  
2015 ◽  
Vol 15 (10) ◽  
pp. 27116-27141 ◽  
Author(s):  
Hsieh-Chang Huang ◽  
Ching-Tang Hsieh ◽  
Cheng-Hsiang Yeh
Keyword(s):  
Detection System ◽  
Obstacle Detection ◽  
Depth Information ◽  
Region Growth

THE DYNAMICAL BEHAVIOR OF NON-SMOOTH SYSTEM WITH IMPULSIVE CONTROL STRATEGIES

2012 ◽  
Vol 05 (03) ◽  
pp. 1260018 ◽  
Author(s):  
YUAN-SHUN TAN ◽  
JU-HUA LIANG ◽  
SAN-YI TANG
Keyword(s):  
Impulsive Control ◽  
Control Strategies ◽  
Dynamical Behavior ◽  
Economic Injury Level ◽  
Economic Threshold ◽  
Pest Population ◽  
Different Types ◽  
Economic Injury ◽  
The Given ◽  
Smooth System

Non-smooth system including impulsive strategies at both fixed and unfixed times are analyzed. For the model with fixed impulsive effects, the global stability of pest eradication periodic solution and the dominance of dynamic behavior are investigated. This indicates that the model with fixed moments has the potential to protect the natural enemies from extinction, but under some conditions may also serve to extinction of the pest. The second model is constructed according to the practices of IPM, that is, when the pest population reaches the economic injury level, a combination of biological, cultural, and chemical tactics that reduce pests to tolerable levels is used. Numerical investigations imply that there are several different types of periodic solutions and their maximum amplitudes are always less than the given economic threshold. The results also show that the time series at which the IPM strategies are applied are quite complex, which means that the application and realization of IPM in practice are very difficult.

Stability and Robustness of a 3D Slip Model for Walking Using Lateral Leg Placement Control

2012 ◽  
Author(s):  
Dominik Budday ◽  
Fabian Bauer ◽  
Justin Seipel
Keyword(s):  
Humanoid Robot ◽  
Performance Test ◽  
Control Mechanism ◽  
Sagittal Plane ◽  
Control Strategies ◽  
Center Of Mass ◽  
Three Dimensional ◽  
Three Dimensions ◽  
Slip Model ◽  
The Stability

The SLIP model has shown a way to easily represent the center of mass dynamics of human walking and running. For 2D motions in the sagittal plane, the model shows self-stabilizing effects that can be very useful when designing a humanoid robot. However, this self-stability could not be found in three-dimensional running, but simple control strategies achieved stabilization of running in three dimensions. Yet, 3D walking with SLIP has not been analyzed to the same extent. In this paper we show that three-dimensional humanoid SLIP walking is also unstable, but can be stabilized using the same strategy that has been successful for running. It is shown that this approach leads to the desired periodic solutions. Furthermore, the influence of different parameters on stability and robustness is examined. Using a performance test to simulate the transition from an upright position to periodic walking we show that the stability is robust. With a comparison of common models for humanoid walking and running it is shown that the simple control mechanism is able to achieve stable solutions for all models, providing a very general approach to this problem. The derived results point out preferable parameters to increase robustness promising the possibility of successfully realizing a humanoid walking robot based on 3D SLIP.

scholarly journals Catadioptric panoramic stereovision for humanoid robots

Robotica ◽  
2011 ◽  
Vol 30 (5) ◽  
pp. 799-811 ◽  
Author(s):  
C. Salinas ◽  
H. Montes ◽  
G. Fernandez ◽  
P. Gonzalez de Santos ◽  
M. Armada
Keyword(s):  
Visual Servoing ◽  
Humanoid Robot ◽  
Moving Objects ◽  
Control Strategies ◽  
Depth Estimation ◽  
Humanoid Robots ◽  
Robust Detection ◽  
Motion System ◽  
Wide Range ◽  
3D Information

SUMMARYThis paper proposes a novel design of a reconfigurable humanoid robot head, based on biological likeness of human being so that the humanoid robot could agreeably interact with people in various everyday tasks. The proposed humanoid head has a modular and adaptive structural design and is equipped with three main components: frame, neck motion system and omnidirectional stereovision system modules. The omnidirectional stereovision system module being the last module, a motivating contribution with regard to other computer vision systems implemented in former humanoids, it opens new research possibilities for achieving human-like behaviour. A proposal for a real-time catadioptric stereovision system is presented, including stereo geometry for rectifying the system configuration and depth estimation. The methodology for an initial approach for visual servoing tasks is divided into two phases, first related to the robust detection of moving objects, their depth estimation and position calculation, and second the development of attention-based control strategies. Perception capabilities provided allow the extraction of 3D information from a wide range of visions from uncontrolled dynamic environments, and work results are illustrated through a number of experiments.

Obstacle Detection for Autonomous Driving Vehicles With Multi-LiDAR Sensor Fusion

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
Mingcong Cao ◽  
Junmin Wang
Keyword(s):  
Data Processing ◽  
Autonomous Vehicles ◽  
Hybrid Electric Vehicle ◽  
Spatial Clustering ◽  
Autonomous Driving ◽  
Obstacle Detection ◽  
Lidar Data ◽  
Detection Accuracy ◽  
Clustering And Classification ◽  
Systematic Solution

Abstract In contrast to the single-light detection and ranging (LiDAR) system, multi-LiDAR sensors may improve the environmental perception for autonomous vehicles. However, an elaborated guideline of multi-LiDAR data processing is absent in the existing literature. This paper presents a systematic solution for multi-LiDAR data processing, which orderly includes calibration, filtering, clustering, and classification. As the accuracy of obstacle detection is fundamentally determined by noise filtering and object clustering, this paper proposes a novel filtering algorithm and an improved clustering method within the multi-LiDAR framework. To be specific, the applied filtering approach is based on occupancy rates (ORs) of sampling points. Besides, ORs are derived from the sparse “feature seeds” in each searching space. For clustering, the density-based spatial clustering of applications with noise (DBSCAN) is improved with an adaptive searching (AS) algorithm for higher detection accuracy. Besides, more robust and accurate obstacle detection can be achieved by combining AS-DBSCAN with the proposed OR-based filtering. An indoor perception test and an on-road test were conducted on a fully instrumented autonomous hybrid electric vehicle. Experimental results have verified the effectiveness of the proposed algorithms, which facilitate a reliable and applicable solution for obstacle detection.

PCA-Based Algorithms to Find Synergies for Humanoid Robot Motion Behavior

2016 ◽  
Vol 13 (02) ◽  
pp. 1550037 ◽  
Author(s):  
Gyanendra Nath Tripathi ◽  
Hiroaki Wagatsuma
Keyword(s):  
Goodness Of Fit ◽  
Humanoid Robot ◽  
Control Strategies ◽  
Principal Component ◽  
Sensor Data ◽  
Reconstructed Signal ◽  
Quantitative Basis ◽  
Qualitative Measure ◽  
Complex Movement ◽  
Motion Behavior

Applying principal component analysis (PCA) to find synergy signal for specific motion of Robot is a standard method. However, implementation of PCA gives synergy solely on quantitative basis. The algorithms proposed in this paper advocates the enhancement of qualitative measure of PCA to locate well-coordinated synergy signals. The two main control strategies of central nervous system (CNS) are taken into account for enhancement of algorithms. First one is the CNS strategy of separate synergy generation for individual limbs and second is the trajectory generation of complex movement using via-points. The proposed algorithms find the synergy without loss of generality of implementation. Humanoid robot NAO is used as a robotic platform to test the result of the algorithm. The synergy for a group of motors is calculated by implementing the algorithm on motors position sensor data of the robot corresponding to three motion pattern 1. Knee bend sitting–standing, 2. Sitting–standing on chair, and 3. Walking. The improvement in result is statistically measured by calculating error between original and reconstructed signal for proposed algorithms and applying Z-test tested on error signals. Another statistical measure of improvement is treated by calculating ‘Goodness of Fit’ for original and reconstructed signal.

A NEW MATHEMATICAL MODEL FOR OPTIMAL CONTROL STRATEGIES OF INTEGRATED PEST MANAGEMENT

2007 ◽  
Vol 15 (02) ◽  
pp. 219-234 ◽  
Author(s):  
XINZHU MENG ◽  
ZHITAO SONG ◽  
LANSUN CHEN
Keyword(s):  
Periodic Solution ◽  
Control Problem ◽  
Integrated Pest Management ◽  
Pest Management ◽  
Control Strategy ◽  
Impulsive Control ◽  
Control Strategies ◽  
Economic Damage ◽  
Pest Population ◽  
The Given

A state-dependent impulsive SI epidemic model for integrated pest management (IPM) is proposed and investigated. We shall examine an optimal impulsive control problem in the management of an epidemic to control a pest population. We introduce a small amount of pathogen into a pest population with the expectation that it will generate an epidemic and that it will subsequently be endemic such that the number of pests is no larger than the given economic threshold (ET), so that the pests cannot cause economic damage. This is the biological control strategy given in the present paper. The combination strategy of pulse capturing (susceptible individuals) and pulse releasing (infective individuals) is implemented in the model if the number of pests (susceptible) reaches the ET. Firstly, the impulsive control problem is to drive the pest population below a given pest level and to do so in a manner which minimizes a weighted sum of the cost of using the control. Hence, for a one time impulsive effect we obtain the optimal strategy in terms of total cost such that the number of pests is no larger than the given ET. Secondly, we show the existence of periodic solution with the number of pests no larger than ET, and by using the Analogue of the Poincaré Criterion we prove that it is asymptotically stable under a planned impulsive control strategy. Further, the period T of the periodic solution is calculated, which can be used to estimate how long the pest population will take to return back to its pre-control level. The main feature of the present paper is to apply an SI infectious disease model to IPM, and some pests control strategies are given.

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