An efficient mobile robot path planning using hierarchical roadmap representation in indoor environment

Park, Byungjae; Choi, Jinwoo; Chung, Wan Kyun

doi:10.1109/icra.2012.6225368

Cited by 30 publications

(25 citation statements)

References 23 publications

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“…As shown in Figure A, the local gridmap contains noisy data owing to the multipath effect of sonar sensors, which need to be suppressed in order to acquire navigable areas in a reliable manner. To do this, a sonar sensor measurement is modeled to evaluate the grid confidence of each occupied grid cell :

Conf false(i, j false) = \sum_{s \in S_{occ} (i, j)} P_{normals} false(r, θ false),

where s indicates a sensor measurement, S occ ( i , j ) is a set of sensor measurements that determine whether grid cell ( i , j ) is occupied, and P s is the sound pressure of a sonar sensor measurement . The grid confidence assesses the reliability of each occupied grid cell, as shown in Figure B.…”

Section: Region Roadmap Constructionmentioning

confidence: 99%

“…Using the tentative subregions divided by the normalized graph cut, convexity measures are computed to determine whether the RUR can be considered a single subregion, or whether it can be divided into two subregions. The convexities are calculated as shown in (4) and (5), respectively :

C_{1} = \frac{n (o \in Conv(RUR))}{{false\sum}_{i = 1}^{n} S q_{i}},

where o is an occupied confident grid cell, and Conv(RUR) is a convex hull of the RUR.

C_{1} = \frac{n false(o \in Conv (C l_{1}) false) + n false(o \in Conv (C l_{2}) false)}{\sum_{i = 1}^{n} S q_{i}},

where Cl 1 and Cl 2 are divided tentative subregions. The above convexity measures are defined as the ratio of the number of occupied confident grid cells to the total number of confident grid cells in each convex hull.…”

Section: Region Roadmap Constructionmentioning

confidence: 99%

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Incremental hierarchical roadmap construction for efficient path planning

Park

Choi²,

Chung

2018

ETRI Journal

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This paper proposes a hierarchical roadmap (HRM) and its construction process to efficiently represent navigable areas in an indoor environment. HRM is adopted to solve the path‐planning problems of mobile robots in indoor environments. HRM has a multi‐layered graphical structure that enables it to abstract and cover navigable areas using a smaller number of nodes and edges than a probabilistic roadmap. During the incremental process of constructing HRM, information on navigable areas is abstracted using a sonar gridmap when the mobile robot navigates an unexplored area. The HRM‐based planner efficiently searches for paths to answer queries by reducing the search space size using the multi‐layered graphical structure. The benefits of the proposed HRM are experimentally verified in real indoor environments.

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Conf false(i, j false) = \sum_{s \in S_{occ} (i, j)} P_{normals} false(r, θ false),

Section: Region Roadmap Constructionmentioning

confidence: 99%

C_{1} = \frac{n (o \in Conv(RUR))}{{false\sum}_{i = 1}^{n} S q_{i}},

where o is an occupied confident grid cell, and Conv(RUR) is a convex hull of the RUR.

C_{1} = \frac{n false(o \in Conv (C l_{1}) false) + n false(o \in Conv (C l_{2}) false)}{\sum_{i = 1}^{n} S q_{i}},

Section: Region Roadmap Constructionmentioning

confidence: 99%

Incremental hierarchical roadmap construction for efficient path planning

Park

Choi²,

Chung

2018

ETRI Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…As explained in [13] and [14], the approach is to construct a hierarchical route map which can abstract the traversable areas using the adequate number of nodes and edges of a graph. The path is searched using the graphs of the several layers.…”

Section: Related Workmentioning

confidence: 99%

Towards decentralized coordination of multi robot systems in industrial environments: A hierarchical traffic control strategy

Digani

Secchi

Fantuzzi

2013

2013 IEEE 9th International Conference on Intelligent Computer Communication and Processing (ICCP)

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This paper describes an innovative approach to manage multiple Automated Guided Vehicles (AGVs) in an industrial environment. The proposed approach is based on a two layer architecture for path planning. This architecture consists of a topological layer, composed by macro-cells, and of a route map layer in which the AGVs have to move along fixed paths. The traffic is managed in a decentralized manner. Each AGV computes autonomously its own path both at topological layer and at route map layer. The coordination among the AGVs is based on the negotiation of shared resources. An early phase of validation is provided by the simulation in a structured environment.

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“…The dimension of the multirobot space may be reduced by using a multilayer structure to represent the world. As explained in [9], the approach is to construct a hierarchical map which can abstract the traversable areas using the adequate number of nodes and edges of a graph. The path is searched using the graphs of the several layers.…”

Section: A Related Workmentioning

confidence: 99%

Ensemble Coordination Approach in Multi-AGV Systems Applied to Industrial Warehouses

Digani

Sabattini

Secchi

et al. 2015

IEEE Trans. Automat. Sci. Eng.

107

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This paper deals with a holistic approach to coordinate a fleet of automated guided vehicles (AGVs) in an industrial environment. We propose an ensemble approach based on a two layer control architecture and on an automatic algorithm for the definition of the roadmap. The roadmap is built by considering the path planning algorithm implemented on the hierarchical architecture and vice versa. In this way, we want to manage the coordination of the whole system in order to increase the flexibility and the global efficiency. Furthermore, the roadmap is computed in order to maximize the redundancy, the coverage and the connectivity. The architecture is composed of two layers. The low-level represents the roadmap itself. The high-level describes the topological relationship among different areas of the environment. The path planning algorithm works on both these levels and the subsequent coordination among AGVs is obtained exploiting shared resource (i.e., centralized information) and local negotiation (i.e., decentralized coordination). The proposed approach is validated by means of simulations and comparison using real plants

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An efficient mobile robot path planning using hierarchical roadmap representation in indoor environment

Cited by 30 publications

References 23 publications

Incremental hierarchical roadmap construction for efficient path planning

Incremental hierarchical roadmap construction for efficient path planning

Towards decentralized coordination of multi robot systems in industrial environments: A hierarchical traffic control strategy

Ensemble Coordination Approach in Multi-AGV Systems Applied to Industrial Warehouses

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