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PDF417 Generation In VBNET Using Barcode maker for Visual Studio NET Control to generate, create PDF 417 Under what conditions will CSO boundaries present manifolds We will show below that if a certain unrestrictive spatial relationship between the robot and obstacle is satis ed for example, under no condition is the robot immobilized, nor does it need to squeeze between two obstacles as in Figure 533 then FCS is uniformly locally connected (ULC Although the ULC property is not suf cient to ensure manifold boundaries for the general case, we will show that for the two-dimensional (2D) case the ULC property guarantees that FCS is bounded by manifolds in this case, simple closed curves We proceed as follows First, a general robot with d translational and/or revolute degrees of freedom will be de ned CS is de ned as a Euclidean space formed by the robot parameter variables.

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A physical obstacle is the interior of a connected compact point set in WS We will show that CSO is a closed subset of CS (Corollary 581) Then we will study the interaction between the robot and obstacles, and will de ne a set of conditions that correspond to certain undesirable degenerate situations (Conditions 581 to 584), such as when a part of or the whole robot is immobilized or when the robot can move between two obstacles only by simultaneously touching them both (Figure 533) We will show in Section 583 that after these situations are removed, CSO presents a uniformly locally connected subset of CS We will then show in Section 584 that ULC is a necessary condition for an open subset of a compact space to have manifold boundaries.

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This is also a suf cient condition for a 2D open subset of a compact space to have manifold boundaries that is, simple closed curves We will thus conclude that FCS of a 2-DOF robot is bounded by simple closed curves More details pertinent to the material in this section can be found in [107] 581 Workspace; Con guration Space As said above, kinematically a robot arm manipulator is an assembly of rigid links connected to each other by joints that permit the links motion relative to each other [8] Joints and links form kinematic pairs As in prior sections, without loss of generality we limit the types of kinematic pairs to either translational (prismatic) or rotational (revolute).

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The degrees of freedom (DOF) of a robot are often referred to as its mobility, which is the number of independent variables that must be speci ed in order to locate all the links relative to each other The 9-DOF robot shown in Figure 532 has nine joints and nine links The robot base is a link in which two translational joints l1 and l2 are implemented The number of degrees of freedom of a robot is not necessarily equal to the number of links or the number of joints Closed kinematic chains often have fewer DOF than the number of their links (joints) For example, a triangle-shaped planar closed kinematic chain with three links and three revolute joints has mobility zero We choose arbitrarily d independent joints, J1 , .

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Suppose that you and a group of friends are on a treasure nding mission You have knowledge of the approximate area of the treasure, but do not know exactly where it is located You want that treasure, or at least some part of it Among your friends you have agreed on some sharing mechanism so that all who have taken part in the search will be rewarded, but with the person who found the treasure getting a higher reward than all others, and the rest being rewarded based on distance from the treasure at the time when the rst one nds the treasure Each one in the group has a metal detector and can communicate the strength of the signal and his current location to the nearest neighbors Each person therefore knows whether one of his neighbors is nearer to the treasure than he is What actions will you take You basically have two choices: (1) Ignore your friends, and search for the treasure without any information that your friends may provide In this case, if you nd the treasure, it is all yours However, if you do not nd it rst, you get nothing (2) Make use of the information that you perceive from your neighboring friends, and move in the direction of your closest friend with the strongest signal By making use of local information, and acting upon it, you increase your chances of nding the treasure, or at least maximizing your reward This is an extremely simple illustration of he bene ts of cooperation in situations where you do not have global knowledge of an environment Individuals within the group interact to solve the global objective by exchanging locally available information, which in the end propagates through the entire group such that the problem is solved more e ciently than can be done by a single individual In loose terms, the group can be referred to as a swarm Formally, a swarm can be de ned as a group of (generally mobile) agents that communicate with each other (either directly or indirectly), by acting on their local environment [371] The interactions between agents result in distributive collective problem-solving strategies Swarm intelligence (SI) refers to the problem-solving behavior that emerges from the interaction of such agents, and computational swarm intelligence (CSI) refers to algorithmic models of such behavior More formally, swarm intelligence is the property of a system whereby the collective behaviors of unsophisticated agents interacting locally with their environment cause coherent functional global patterns to emerge [702] Swarm intelligence has also been referred to as collective intelligence 285.

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Suppose that you and a group of friends are on a treasure nding mission You have knowledge of the approximate area of the treasure, but do not know exactly where it is located You want that treasure, or at least some part of it Among your friends you have agreed on some sharing mechanism so that all who have taken part in the search will be rewarded, but with the person who found the treasure getting a higher reward than all others, and the rest being rewarded based on distance from the treasure at the time when the rst one nds the treasure Each one in the group has a metal detector and can communicate the strength of the signal and his current location to the nearest neighbors Each person therefore knows whether one of his neighbors is nearer to the treasure than he is What actions will you take You basically have two choices: (1) Ignore your friends, and search for the treasure without any information that your friends may provide In this case, if you nd the treasure, it is all yours However, if you do not nd it rst, you get nothing (2) Make use of the information that you perceive from your neighboring friends, and move in the direction of your closest friend with the strongest signal By making use of local information, and acting upon it, you increase your chances of nding the treasure, or at least maximizing your reward This is an extremely simple illustration of the bene ts of cooperation in situations where you do not have global knowledge of an environment Individuals within the group interact to solve the global objective by exchanging locally available information which in the end propagates through the entire group such that the problem is solved more e ciently than can be done by a single individual In loose terms, the group can be referred to as a swarm Formally, a swarm can be de ned as a group of (generally mobile) agents that communicate with each other (either directly or indirectly), by acting on their local environment [371] The interactions between agents result in distributive collective problem-solving strategies Swarm intelligence (SI) refers to the problem-solving behavior that emerges from the interaction of such agents, and computational swarm intelligence (CSI) refers to algorithmic models of such behavior More formally, swarm intelligence is the property of a system whereby the collective behaviors of unsophisticated agents interacting locally with their environment cause coherent functional global patterns to emerge [702] Swarm intelligence has also been referred to as collective intelligence 285.

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