Design of an Autonomous Robotic Vehicle for Area Mapping and Remote Monitoring

Papoutsidakis, Michail; Kalovrektis, Κonstantinos; Drosos, Christos; Stamoulis, George

doi:10.5120/ijca2017914496

Cited by 16 publications

(13 citation statements)

References 4 publications

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“…The exploration-based approach on neural networks differs considerably from the frontier-based approach in terms of environment perception and control system. Visual sensors (cameras) scan a place for further computation using image-processing algorithms [33]. The output of the calculation is the interaction of the robot with the environment.…”

Section: Related Workmentioning

confidence: 99%

Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

et al. 2019

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In this paper, we used multi-objective optimization in the exploration of unknown space. Exploration is the process of generating models of environments from sensor data. The goal of the exploration is to create a finite map of indoor space. It is common practice in mobile robotics to consider the exploration as a single-objective problem, which is to maximize a search of uncertainty. In this study, we proposed a new methodology of exploration with two conflicting objectives: to search for a new place and to enhance map accuracy. The proposed multiple-objective exploration uses the Multi-Objective Grey Wolf Optimizer algorithm. It begins with the initialization of the grey wolf population, which are waypoints in our multi-robot exploration. Once the waypoint positions are set in the beginning, they stay unchanged through all iterations. The role of updating the position belongs to the robots, which select the non-dominated waypoints among them. The waypoint selection results from two objective functions. The performance of the multi-objective exploration is presented. The trade-off among objective functions is unveiled by the Pareto-optimal solutions. A comparison with other algorithms is implemented in the end.

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Section: Related Workmentioning

confidence: 99%

Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

et al. 2019

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“…The geometry and kinematic parameters of the differential mobile robot is defined. The position/ orientation vector of the mobile robot and its speed are, respectively [1][2][3][4][5][6][7][8][28][29][30][31][32][33]:…”

Section: The Geometry and Kinematic Of A Mobile Robotmentioning

confidence: 99%

“…Such an error can be corrected using the FGS-PD controller. The PD control system using fuzzy gain scheduling is that the method is determined by fuzzy logic with gain scheduling to decide the parameters of PD control [28][29][30][31].…”

Section: The Design Of Fuzzy Gain Scheduling Of the Pd Controllermentioning

confidence: 99%

“…Therefore, such problems can be solved through the selection of a suitable curvature and application of a gradual fuzzy gain scheduling of PD control (FGS-PD) control process based on the relationship between the current location of the mobile robot and the target points. Moreover, by controlling the velocities of each of the two wheels attached to the mobile robot through a FGS-PD controller, it is possible to compensate for the slip phenomenon based on the inertial and centrifugal forces acting on the mobile robot as it moves, while minimizing the estimation errors [26][27][28][29][30][31][32][33]. For validating the proposed method, the functions are evaluated by conducting an experiment on the estimation of the sound sources moving in real time in two different situations (straight trajectory and S-curved trajectory) [1][2][3][4][5][6][7][8][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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Tracking Control of Moving Sound Source Using Fuzzy-Gain Scheduling of PD Control

Han

2019

Electronics

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This paper proposes fuzzy gain scheduling of proportional differential control (FGS-PD) system for tracking mobile robot to moving sound sources. Given that the target positions of the real-time moving sound sources are dynamic, the mobile robots should be able to estimate the target points continuously. In such a case, the robots tend to slip owing to abnormal velocities and abrupt changes in the tracking path. The selection of an appropriate curvature along which the robot follows a sound source makes it possible to ensure that the robot reaches the target sound source precisely. For enabling the robot to recognize the sound sources in real time, three microphones are arranged in a straight formation. In addition, by applying the cross correlation algorithm to the time delay of arrival base, the received signals can be analyzed for estimating the relative positions and velocities of the mobile robot and the sound source. Even if the mobile robot is navigating along a curved path for tracking the sound source, there could be errors due to the inertial and centrifugal forces resulting from the motion of the mobile robot. Velocities of both robot wheels are controlled using FGS-PD control to compensate for slippage and to minimize tracking errors. For experimentally verifying the efficacy of the proposed the control system with sound source estimation, two mobile robots were fabricated. It was demonstrated that the proposed control method effectively reduces the tracking error of a mobile robot following a sound source.

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“…Human-machine interactive navigation refers to the process through which machines and operator cooperate with each other to control the movement of devices and realize interactive navigation [1]. This type of navigation has been widely used in indoor service robots [2,3], self-driving cars [4,5], automatic guided vehicles (AGV) [6,7], and so on [8,9]. Among them, the most important part between the operator and machines is the map.…”

Section: Introductionmentioning

confidence: 99%

Topological Map Construction Based on Region Dynamic Growing and Map Representation Method

et al. 2019

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In the human–machine interactive scene of the service robot, obstacle information and destination information are both required, and both kinds of information need to be saved and used at the same time. In order to solve this problem, this paper proposes a topological map construction pipeline based on regional dynamic growth and a map representation method based on the conical space model. Based on the metric map, the construction pipeline can initialize the region growth point on the trajectory of the mobile robot. Next, the topological region is divided by the region dynamic growth algorithm, the map structure is simplified by the minimum spanning tree, and the similar region is merged by the region merging algorithm. After that, the parameter TM (topological information in the map) and the parameter OM (occupied information in the map) are used to represent the topological information and the occupied information. Finally, a topological map represented by the colored picture is saved by converting to color information. It is highlighted that the topological map construction pipeline is not limited by the structure of the environment, and can be automatically adjusted according to the actual environment structure. What’s more, the topological map representation method can save two kinds of map information at the same time, which simplifies the map representation structure. The experimental results show that the map construction method is flexible, and that resources such as calculation and storage are less consumed. The map representation method is convenient to use and improves the efficiency of the map in preservation.

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Design of an Autonomous Robotic Vehicle for Area Mapping and Remote Monitoring

Cited by 16 publications

References 4 publications

Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

Tracking Control of Moving Sound Source Using Fuzzy-Gain Scheduling of PD Control

Topological Map Construction Based on Region Dynamic Growing and Map Representation Method

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