Vision-Based Autonomous Navigation System Using ANN and FSM Control

Sales, Daniel Oliva; Shinzato, Patrick Y.; Pessin, Gustavo; Wolf, Denis F.; Osorio,

doi:10.1109/lars.2010.26

Cited by 11 publications

(12 citation statements)

References 16 publications

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“…In [18] there is an ANN which performs the navigation of a robot in a simulated three-dimensional environment; the ANN inputs consist of information collected from sensors (localization, orientation, distance to obstacles) while the outputs are the speed and angle which have to be applied to the linear and angular motors, respectively. In addition, the study [22] discusses an ANN that is used to classify navigable and non-navigable regions in images; the ANN inputs are attributes of color, formed by the average of color channels and entropy. Other studies using ANNs can be seen in [15,25].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Evaluating the impact of the number of access points in mobile robots localization using artificial neural networks

Pessin

Osório

Ueyama

et al. 2011

Proceedings of the 5th International Conference on Communication System Software and Middleware

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Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Evaluating the impact of the number of access points in mobile robots localization using artificial neural networks

Pessin

Osório

Ueyama

et al. 2011

Proceedings of the 5th International Conference on Communication System Software and Middleware

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“…In a recent work, [16] proposed an ANN integrated into a vision system, capable of recognizing different environment situations. These situations were used to generate a sequence of control actions, defining the FSM control.…”

Section: Related Workmentioning

confidence: 99%

“…The main topics of this work are: the integration of an ANN into the FSM control used in patrolling task, improving the work as presented by [10]; the Topological Map Localization and Navigation, based on the use of a laser ranger-finder, allowing a more robust navigation and improving the proposal presented by [16] (which uses a more simpler vision system); the possibility of defining different patrolling strategies as those proposed by [2] since different sequences of actions can be represented and coded into the FSMs; and the multi-agent coordinated response to this using an adapted A* algorithm to surround the target; the integration of these different AI techniques (ANN, FSM, Intrusion detection, A*, Multiagents coordination) composing a unique solution for autonomous mobile robots based patrolling tasks.…”

Section: Related Workmentioning

confidence: 99%

“…Navigation in this environment consists basically to follow a defined path, based on a previously defined map of the environment, and also considering its sensors data [19] [16].…”

Section: Introductionmentioning

confidence: 99%

“…This navigation/localization system is based in a combination of an ANN [7] with FSM control [16]. The robots behavior and the path to be followed can be easily described as a sequence of states in a FSM: the path is a sequence of key-points to be visited, and the behavior is a response to the sensors when an agent is detected.…”

Section: Introductionmentioning

confidence: 99%

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Multi-agent Autonomous Patrolling System Using ANN and FSM Control

Sales

Feitosa

Osorio

et al. 2012

2012 Second Brazilian Conference on Critical Embedded Systems

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The multi-agent patrolling problem has recently received growing attention from the community due to the wide range of potential applications. This work presents an autonomous patrolling system composed by 4 intelligent robots that can freely move through an indoor environment and detect intruders. The robots use a localization/navigation system composed of an artificial neural network (ANN) trained to detect key features of the environment. These features are used to identify context changes, being used as input of a finite state machine (FSM), allowing a topological map localization and navigation of the robot in the environment. When an intruder is detected, a broadcast message with its position is sent, making all other robots execute a multi-agent version of a coordinated A* algorithm in order to determine the best path to reach that position and to surround the target. Then, the robots autonomously navigate through this defined path until reach the goal. Experiments were performed in the player/stage environment in order to evaluate the multi-agent system. The localization/navigation system with intruder detection was evaluated in the real world with a Pioneer P3-AT mobile robot.

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