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1: 1948-ELSIE (Electro-mechanical robot, Light Sensitive with Internal and External stability)-W. Grey Walter (Source: cyberneticzoo.com)
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In this report, we propose a decentralised motion control algorithm for the mobile robots to intercept an intruder entering (k-intercepting) or escaping (e-intercepting) a protected region. In continuation, we propose a decentralized navigation strategy (dynamic-intercepting) for a multi-robot team known as predators to intercept the intruders or i...
Contexts in source publication
Context 1
... introduce í µí±(í µí±) for any point í µí± ∈ í µí± $ ∨ í µí± ∈ í µí± & such that í µí±(í µí±) is the length of the curved shape portion of the segment í µí± between the points í µí±1 and í µí±; see Fig.1.5. ...
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... R be a closed convex planar region with a piecewise smooth boundary where the robots moving on to siege the intruder. Furthermore, let í µí± $ and í µí± & be segments of the boundary of the region í µí± between points í µí± $ ↺ í µí± & and í µí± & ↺ í µí± $ respectively where ↺ denotes the clockwise direction; see Fig.1 ...
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... to Fig.10.2, if the robot moved from location í µí²ª $ to location í µí²ª • , with the heading í µí»¼ $ based on ℓ $ • d and the length of í µí´© $•É then the distance between the robot and the obstacle í µí± É would be greater than í µí¼. In the same way, if the leader moved to location í µí²ª ' with the heading ...
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... vector ℒ i ú jjj⃗ denotes the resultant vector of í µí´© ⃗ $•É and í µí´© ⃗ $•" (see Fig. ...
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... the proposed navigational model resulted in decreased energy consumption and increased battery life because the robots used the sensors less frequently to find the safest path in the region. Fig.10.3 shows how the multi-robot team moved in the region while avoiding obstacles. ...
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... Fig.10.4, the solid line represents the distance of robot 1 to obstacle 1. The dashed lines show the distance of robot 2 to obstacles 1, 2 and 3, and the dotted lines represent the distance of robot 3 to obstacles 2, 3 and 4 during their mission to trap the target. ...
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... dashed lines show the distance of robot 2 to obstacles 1, 2 and 3, and the dotted lines represent the distance of robot 3 to obstacles 2, 3 and 4 during their mission to trap the target. As Fig10.4 shows, each robot moved between two switching points with no computation, which means that each robot moved blindly between any two switching points. ...
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... í µí¼℘ • of the obstacles and the robot as well (see. Fig.11.1), considered to be a particle then the virtual repulsive force is defined as follows: Figure 11.1: Robot's sensing and measurements Where ℱ ¥ represents the resultant force of the all virtual repulsive forces exerted from each point í µí³ D • í µí¼℘ • of the obstacles to the robot. ...
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... í µí¼℘ • of the obstacles and the robot as well (see. Fig.11.1), considered to be a particle then the virtual repulsive force is defined as follows: Figure 11.1: Robot's sensing and measurements Where ℱ ¥ represents the resultant force of the all virtual repulsive forces exerted from each point í µí³ D • í µí¼℘ • of the obstacles to the robot. ...
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... we need to find the virtual attractive forces, exerted from the obstacles to the robot. As shown in Fig.11 ...
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... this section, MATLAB simulations confirms the validation of the proposed algorithm. Fig.11.3 (a, b, c, d It is obvious that, the robot chooses the best direction with a proper velocity to avoid the collision regardless the motion direction of the obstacles, while using the proposed navigation algorithm. ...
Context 12
... introduce í µí±(í µí±) for any point í µí± ∈ í µí± $ ∨ í µí± ∈ í µí± & such that í µí±(í µí±) is the length of the curved shape portion of the segment í µí± between the points í µí±1 and í µí±; see Fig.1.5. ...
Context 13
... R be a closed convex planar region with a piecewise smooth boundary where the robots moving on to siege the intruder. Furthermore, let í µí± $ and í µí± & be segments of the boundary of the region í µí± between points í µí± $ ↺ í µí± & and í µí± & ↺ í µí± $ respectively where ↺ denotes the clockwise direction; see Fig.1 ...
Context 14
... to Fig.10.2, if the robot moved from location í µí²ª $ to location í µí²ª • , with the heading í µí»¼ $ based on ℓ $ • d and the length of í µí´© $•É then the distance between the robot and the obstacle í µí± É would be greater than í µí¼. In the same way, if the leader moved to location í µí²ª ' with the heading ...
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... vector ℒ i ú jjj⃗ denotes the resultant vector of í µí´© ⃗ $•É and í µí´© ⃗ $•" (see Fig. ...
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... the proposed navigational model resulted in decreased energy consumption and increased battery life because the robots used the sensors less frequently to find the safest path in the region. Fig.10.3 shows how the multi-robot team moved in the region while avoiding obstacles. ...
Context 17
... Fig.10.4, the solid line represents the distance of robot 1 to obstacle 1. The dashed lines show the distance of robot 2 to obstacles 1, 2 and 3, and the dotted lines represent the distance of robot 3 to obstacles 2, 3 and 4 during their mission to trap the target. ...
Context 18
... dashed lines show the distance of robot 2 to obstacles 1, 2 and 3, and the dotted lines represent the distance of robot 3 to obstacles 2, 3 and 4 during their mission to trap the target. As Fig10.4 shows, each robot moved between two switching points with no computation, which means that each robot moved blindly between any two switching points. ...
Context 19
... í µí¼℘ • of the obstacles and the robot as well (see. Fig.11.1), considered to be a particle then the virtual repulsive force is defined as follows: Figure 11.1: Robot's sensing and measurements Where ℱ ¥ represents the resultant force of the all virtual repulsive forces exerted from each point í µí³ D • í µí¼℘ • of the obstacles to the robot. ...
Context 20
... í µí¼℘ • of the obstacles and the robot as well (see. Fig.11.1), considered to be a particle then the virtual repulsive force is defined as follows: Figure 11.1: Robot's sensing and measurements Where ℱ ¥ represents the resultant force of the all virtual repulsive forces exerted from each point í µí³ D • í µí¼℘ • of the obstacles to the robot. ...
Context 21
... we need to find the virtual attractive forces, exerted from the obstacles to the robot. As shown in Fig.11 ...
Context 22
... this section, MATLAB simulations confirms the validation of the proposed algorithm. Fig.11.3 (a, b, c, d It is obvious that, the robot chooses the best direction with a proper velocity to avoid the collision regardless the motion direction of the obstacles, while using the proposed navigation algorithm. ...
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