Task Allocation and Route Planning for Robotic Service Networks with Multiple Depots in Indoor Environments

2019 ◽  
Author(s):  
Bharadwaj R. K. Mantha ◽  
Borja García de Soto
Keyword(s):  
Task Allocation ◽  
Route Planning ◽  
Indoor Environments ◽  
Service Networks ◽  
Multiple Depots

Generalized task allocation and route planning for robots with multiple depots in indoor building environments

2020 ◽  
Vol 119 ◽  
pp. 103359
Author(s):  
Bharadwaj R.K. Mantha ◽  
Min Kyu Jung ◽  
Borja García de Soto ◽  
Carol C. Menassa ◽  
Vineet R. Kamat
Keyword(s):  
Task Allocation ◽  
Route Planning ◽  
Multiple Depots

Task Allocation and Route Planning for Robotic Service Networks in Indoor Building Environments

2017 ◽  
Vol 31 (5) ◽  
pp. 04017038 ◽  
Author(s):  
Bharadwaj R. K. Mantha ◽  
Carol C. Menassa ◽  
Vineet R. Kamat
Keyword(s):  
Task Allocation ◽  
Route Planning ◽  
Service Networks

scholarly journals SoC FPGA Accelerated Sub-Optimized Binary Fully Convolutional Neural Network for Robotic Floor Region Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6133
Author(s):  
Chi-Chia Sun ◽  
Afaroj Ahamad ◽  
Pin-He Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Heterogeneous Computing ◽  
Robot Vision ◽  
Route Planning ◽  
Indoor Environments ◽  
Embedded Devices ◽  
Efficient Reduction ◽  
Segmentation Accuracy ◽  
Binarization Method

In this article, a new Binary Fully Convolutional Neural Network (B-FCN) based on Taguchi method sub-optimization for the segmentation of robotic floor regions, which can precisely distinguish floor regions in complex indoor environments is proposed. This methodology is quite suitable for robot vision in an embedded platform and the segmentation accuracy is up to 84.80% on average. A total of 6000 training datasets were used to improve the accuracy and reach convergence. On the other hand, to reach real-time computation, a PYNQ FPGA platform with heterogeneous computing acceleration was used to accelerate the proposed B-FCN architecture. Overall, robots would benefit from better navigation and route planning in our approach. The FPGA synthesis of our binarization method indicates an efficient reduction in the BRAM size to 0.5–1% and also GOPS/W is sufficiently high. Notably, the proposed faster architecture is ideal for low power embedded devices that need to solve the shortest path problem, path searching, and motion planning.

scholarly journals AUTOMATED DETECTION AND LAYOUT REGULARIZATION OF SIMILAR FEATURES IN INDOOR POINT CLOUD

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 631-638
Author(s):  
M. Previtali ◽  
L. Barazzetti ◽  
F. Roncoroni
Keyword(s):  
Point Cloud ◽  
Route Planning ◽  
Poor Quality ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Automated Identification ◽  
Essential Information ◽  
Indoor Space ◽  
High Level ◽  
Evacuation Route Planning

<p><strong>Abstract.</strong> Automated identification of high-level structures in unorganized point cloud of indoor spaces Indoor space is an important aspect of scene analysis that provides essential information for many applications, such as building digitization, indoor navigation and evacuation route planning. In addition, detection of repetition and regularities in the organization indoor environments, such as rooms, can be used to provide a contextual relationship in the reconstruction phase. However, retrieving high-level information is a challenging task due to the unorganized nature of the raw data, poor-quality of the input data that are in many cases contaminated with noise and outliers. in point benefit from the apparent regularities and strong contextual relationships in façades. The main observation exploited in this paper is the fact that building indoor is generally constituted by a set of basic shapes repeated several times in regular layouts. Building elements can be considered as similar if they share a set of features and elements in an idealized layout exhibiting some regularities. Starting from this main assumption a recursive adaptive partitioning of the indoor point cloud is carried out to automatically derive a flexible and hierarchical 3D representation of the building space. The presented methodology is tested on a synthetic dataset with Gaussian noise. The reconstructed pattern shows a close correspondence with the synthetic one showing the viability of the proposed approach.</p>

scholarly journals On the Right Track: Comfort and Confusion in Indoor Environments

2020 ◽  
Vol 9 (2) ◽  
pp. 132 ◽  
Author(s):  
Nina Vanhaeren ◽  
Laure De Cock ◽  
Lieselot Lapon ◽  
Nico Van de Weghe ◽  
Kristien Ooms ◽  
...  
Keyword(s):  
Focus Group ◽  
Online Survey ◽  
Local Level ◽  
Route Planning ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Navigation Systems ◽  
Online Study ◽  
Planning Algorithm ◽  
The Right

Indoor navigation systems are not well adapted to the needs of their users. The route planning algorithms implemented in these systems are usually limited to shortest path calculations or derivatives, minimalizing Euclidian distance. Guiding people along routes that adhere better to their cognitive processes could ease wayfinding in indoor environments. This paper examines comfort and confusion perception during wayfinding by applying a mixed-method approach. The aforementioned method combined an exploratory focus group and a video-based online survey. From the discussions in the focus group, it could be concluded that indoor wayfinding must be considered at different levels: the local level and the global level. In the online survey, the focus was limited to the local level, i.e., local environmental characteristics. In this online study, the comfort and confusion ratings of multiple indoor navigation situations were analyzed. In general, the results indicate that open spaces and stairs need to be taken into account in the development of a more cognitively-sounding route planning algorithm. Implementing the results in a route planning algorithm could be a valuable improvement of indoor navigation support.

scholarly journals Route Planning for Agricultural Machines with Multiple Depots: Manure Application Case Study

Agronomy ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1608
Author(s):  
Mahdi Vahdanjoo ◽  
Kun Zhou ◽  
Claus Aage Grøn Sørensen
Keyword(s):  
Route Planning ◽  
Route Optimization ◽  
Specific Load ◽  
Planning Tool ◽  
Routing Problem ◽  
Planning Approach ◽  
Multiple Depots ◽  
General Goal ◽  
Agricultural Machines

Capacitated field operations involve input/output material flows where there are capacity constraints in the form of a specific load that a vehicle can carry. As such, a specific normal-sized field cannot be covered in one single operation using only one load, and the vehicle needs to get serviced (i.e., refilling) from out-of-field facilities (depot). Although several algorithms have been developed to solve the routing problem of capacitated operations, these algorithms only considered one depot. The general goal of this paper is to develop a route planning tool for agricultural machines with multiple depots. The tool presented consists of two modules: the first one regards the field geometrical representation in which the field is partitioned into tracks and headland passes; the second one regards route optimization that is implemented by the metaheuristic simulated annealing (SA) algorithm. In order to validate the developed tool, a comparison between a well-known route planning approach, namely B-pattern, and the algorithm presented in this study was carried out. The results show that the proposed algorithm outperforms the B-pattern by up to 20.0% in terms of traveled nonworking distance. The applicability of the tool developed was tested in a case study with seven scenarios differing in terms of locations and number of depots. The results of this study illustrated that the location and number of depots significantly affect the total nonworking traversal distance during a field operation.

Route Planning Integrated Multi Objective Task Allocation for Reconfigurable Robot Teams Using Genetic Algorithm

2018 ◽  
Vol 15 (2) ◽  
pp. 627-636 ◽  
Author(s):  
K. Padmanabhan Panchu ◽  
M. Rajmohan ◽  
M. R. Sumalatha ◽  
R. Baskaran
Keyword(s):  
Genetic Algorithm ◽  
Task Allocation ◽  
Completion Time ◽  
Route Planning ◽  
Task Completion ◽  
Task Completion Time ◽  
Robot Teams ◽  
Reconfigurable Robot ◽  
Robot Tasks ◽  
Multi Robot

This research work aims at multi objective optimization of integrated route planning and multi-robot task allocation for reconfigurable robot teams. Genetic Algorithm based methodology is used to minimize the overall task completion time for all the multi-robot tasks and to minimize the cumulative running time of all the robots. A modified matrix based chromosome is used to accommodate the robot information and task information for route planning integrated task allocation. The experimental validation is done with 3 robots and 4 tasks. For larger number of robots and tasks were simulated to perform route planning for maximum of 20 robots that would attend the maximum of 40 different multi-robot tasks. The results shows that the average task completion time per robot and average travel time per robot, decreases exponentially with increase in number of robots for fixed number of tasks. This method finds its application in allocating a robot teams to tasks and finding the best sequence for robots that work in coordination for material handling in hospital management, warehouse operations, military operations, cleaning tasks etc.

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