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Tensors
Published in Mattias Blennow, Mathematical Methods for Physics and Engineering, 2018
For a large class of mechanical systems, the configuration space is a general space of N-dimensions, i.e., the spatial configuration of the system may be described by N coordinates ya (see Section 10.2.1). One typical example of this is the double pendulum shown in Fig. 2.10, the configuration of which may be fully described by the two angles φ1 and φ2. The kinetic energy T in such a general system can be written on the form () T=12Maby˙ay˙b,
Development Toward Autonomous Systems
Published in Ulrich Rembold, Robot Technology and Applications, 2020
The approach taken by Hörmann treats the arm (consisting of a shoulder, an upper arm, and a forearm) and the hand (with the payload) separately. His approach utilizes the configuration space (C-space) method. A point in the configuration space describes the position and the orientation of the robot (e.g., the joint space of a manipulator is a configuration space representation). Here the configuration space is the Cartesian C space for the first three joints of the arm. This method allows the decoupling of the links of the robot such that the C-space obstacles for each link can be computed separately. This is done to decrease the execution time of the algorithm.
Tools for a roboticist
Published in Arkapravo Bhaumik, From AI to Robotics, 2018
Configuration space or C-space is an important aspect for navigation and is defined as the the hypothetical space formed from all the possible configurations of the robot. The dimensions of configuration space is the number of degrees of freedom of the robot. For a mobile robot the configuration space is a two dimensional plane and this formalism considers the robot as a point and obstacles as ‘globs’ on a two-dimensional map, and thus reduces the problem of robot navigation to path planning in two dimensions.
RRT*N: an efficient approach to path planning in 3D for Static and Dynamic Environments
Published in Advanced Robotics, 2021
Hussein Mohammed, Lotfi Romdhane, Mohammad A. Jaradat
In all variations of the rapidly exploring random tree algorithm, the first and most important variable that needs to be known is the configuration space, Z. The set of all the possible transformations that can be applied to the robot is defined as its configuration space. This can also be linked to the dimension of the space in which the robot operates [20]. The occupied region in the configuration space, whether by obstacles or simply an inaccessible or undesirable region is denoted Zobs. That region is invalid for navigation hence often when running the algorithm, researchers might set it up such that no nodes will be generated in that region. Although, it does not actually matter since collision checks will be performed to make sure that no branch of the tree enters the region Zobs. Meanwhile, the remaining elements in the configuration space, which constitute the free or navigation-able space, Zfree, is the area in which the tree can grow. Then within Zfree the starting point, Zinit, and the goal point, Zgoal, are defined.
Using the Bees Algorithm for wheeled mobile robot path planning in an indoor dynamic environment
Published in Cogent Engineering, 2018
Ahmed Haj Darwish, Abdulkader Joukhadar, Mariam Kashkash
The configuration space is a set of allowed movements of the robot in the environment (Siegwart & Nourbakhsh, 2004). The position of a wheeled mobile robot is represented by . In the 2D configuration space, it is possible to represent the robot as a point. However, the surrounding area of obstacles should be increased proportionally to the geometric shape of the studied robot as shown in Figure 1. In order to keep tracking of dynamic environment the time should be added to the robot representation, then the position of a wheeled mobile robot is given as: .
MDHO: Mayfly Deer Hunting Optimization Algorithm for Optimal Obstacle Avoidance Based Path Planning Using Mobile Robots
Published in Cybernetics and Systems, 2023
Sakthitharan Subramanian, Sudha Rajesh, Preethika Immaculate Britto, Sakthivel Sankaran
The configuration space or the environment is composed with free space such that the space is filled with obstacles. Accordingly, predefined initial and the destination locations are placed in the free space. Path planning is referred as the process of determining finite set of possible movements in environment for navigating the robot among source and the target location. In general, there exists more than one path to reach the target, but it is more important to identify path with shortest length. In most, number of robots is traverses in the environment to search for the specified target. Assume set of robots as and it is formulated as, where, implies set of robots, shows individual robot lies in the group, represents group number, and implies total count of groups. It is observed that each robot is described as the free moving point in the environment and it is specified as a grid cell. The robots are equipped with sensors that is used to measure the distance of nearby obstacles to determine the distance for reaching the target point. However, the configuration of such sensor offers information about the circular region. A finite number of obstacles are occupied in the environmental cells such that the configuration of obstacle varies in terms of occupancy level, density, position and shape. Assume that each cell can be entirely filled with obstacles or may free from obstacles. However, there exist various counts of intermediate cells for each path. The problem is to find shortest, safest, as well as smoothest path available by considering multi-objective factors. Each robot reach the destination at minimum time by making minimum number of moves. The path planning is an optimization problem in variety of domains, like planetary exploration, video games, robotics mining, and so on. To solve issues and to determine the best path, this research considers an MDHO algorithm based on different objective factors to make feasible movements in free space. Figure 1 illustrates system model of multi-robot path planning.