Workspace exploration and protection with multiple robots assisted by sensor networks

Kim, Jonghoek

doi:10.1177/1729881418792170

Cited by 10 publications

(6 citation statements)

References 52 publications

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“…There may be a case where the LOS between the rover and the landmark is blocked by an obstacle. At the moment when the LOS is blocked, the rover deploys a visible node utilizing the deployment approach in Kim [22,23]. When the node is deployed, we localize the node utilizing the rover at the node.…”

Section: Discussionmentioning

confidence: 99%

Autonomous rover guidance and localization by measuring the peak of a tall landmark

Kim

2022

Asian Journal of Control

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This paper handles locating an autonomous rover whose mission is to explore the unknown outdoor environments, while localizing itself. For localization, the rover occasionally measures the peak of a tall landmark which is known a priori. For the peak measurement, the rover utilizes a monocular camera which is stabilized utilizing a gimbal system. In addition, the rover uses object detection methods to detect a landmark in its camera view. We assume that the height of the landmark is known a priori. Also, we assume that the rover has an altimeter to measure its altitude. Under these assumptions, we present a method to make the rover estimate the relative position of the landmark. Suppose that the rover's target location is set, so that the rover needs to visit the target while avoiding collision with obstacles. Due to environmental disturbance, the rover may not maintain its desired course while it moves. We thus address a guidance control to make the rover approach the target, while compensating the environmental disturbance at the rover's position. As far as we know, this paper is novel in addressing the rover's guidance and localization by measuring the peak of a tall landmark which is known a priori. Furthermore, this paper is novel in utilizing gimbal camera for localization. Thus, we do not require heavy computational load caused by calculating the 6‐DOF transform between the camera and the rover's pose. The effectiveness of the proposed approach is verified utilizing MATLAB simulations.

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Section: Discussionmentioning

confidence: 99%

Autonomous rover guidance and localization by measuring the peak of a tall landmark

Kim

2022

Asian Journal of Control

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“…One of these methods is presented in Xin et al. [ 18 ] and Kim [ 17 ]. Xin et al presented a method that plans the paths in 3D space.…”

Section: Uav Flight Planning Procedures Using Sensors To Recognize mentioning

confidence: 99%

“…The construction of network proceeds in three phases and after these phases a net for a path planning is prepared. More complicated version of network construction plausible for path planning is presented by Kim [ 17 ]. In this article, multi-robot strategies of terrain exploration is presented in a cooperative manner.…”

Section: Uav Flight Planning Procedures Using Sensors To Recognize mentioning

confidence: 99%

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Determining UAV Flight Trajectory for Target Recognition Using EO/IR and SAR

Stecz

Gromada

2020

Sensors

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The paper presents the concept of planning the optimal trajectory of fixed-wing unmanned aerial vehicle (UAV) of a short-range tactical class, whose task is to recognize a set of ground objects as a part of a reconnaissance mission. Tasks carried out by such systems are mainly associated with an aerial reconnaissance using Electro-Optical/Infrared (EO/IR) systems and Synthetic Aperture Radars (SARs) to support military operations. Execution of a professional reconnaissance of the indicated objects requires determining the UAV flight trajectory in the close neighborhood of the target, in order to collect as much interesting information as possible. The paper describes the algorithm for determining UAV flight trajectories, which is tasked with identifying the indicated objectives using the sensors specified in the order. The presence of UAV threatening objects is taken into account. The task of determining the UAV flight trajectory for recognition of the target is a component of the planning process of the tactical class UAV mission, which is also presented in the article. The problem of determining the optimal UAV trajectory has been decomposed into several subproblems: determining the reconnaissance flight method in the vicinity of the currently recognized target depending on the sensor used and the required parameters of the recognition product (photo, film, or SAR scan), determining the initial possible flight trajectory that takes into account potential UAV threats, and planning detailed flight trajectory considering the parameters of the air platform based on the maneuver planning algorithm designed for tactical class platforms. UAV route planning algorithms with time constraints imposed on the implementation of individual tasks were used to solve the task of determining UAV flight trajectories. The problem was formulated in the form of a Mixed Integer Linear Problem (MILP) model. For determining the flight path in the neighborhood of the target, the optimal control algorithm was also presented in the form of a MILP model. The determined trajectory is then corrected based on the construction algorithm for determining real UAV flight segments based on Dubin curves.

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“…4,5,[12][13][14][15][16][17][18][19][20][21] For instance, a Voronoi diagram can be used to build a safe path for a robot in two dimensions. [12][13][14][15][16][17][18][19]22 However, a Voronoi diagram path may be unnecessarily long, especially in a situation where a passage between two obstacles is too wide.…”

Section: Introductionmentioning

confidence: 99%

Time-efficient path planning using two virtual robots

Kim

2019

International Journal of Advanced Robotic Systems

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This article introduces time-efficient path planning algorithms handling both path length and safety within a reasonable computational time. The path is planned considering the robot's size so that as the robot traverses the constructed path, it doesn't collide with an obstacle boundary. This article introduces two virtual robots deploying virtual nodes which discretize the obstacle-free space into a topological map. Using the topological map, the planner generates a safe and nearoptimal path within a reasonable computational time. It is proved that our planner finds a safe path to the goal in finite time. Using MATLAB simulations, we verify the effectiveness of our path planning algorithms by comparing it with the rapidly-exploring random tree (RRT)-star algorithm in three-dimensional environments.

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Workspace exploration and protection with multiple robots assisted by sensor networks

Cited by 10 publications

References 52 publications

Autonomous rover guidance and localization by measuring the peak of a tall landmark

Autonomous rover guidance and localization by measuring the peak of a tall landmark

Determining UAV Flight Trajectory for Target Recognition Using EO/IR and SAR

Time-efficient path planning using two virtual robots

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