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Intelligent Systems
Published in R.S. Chauhan, Kavita Taneja, Rajiv Khanduja, Vishal Kamra, Rahul Rattan, Evolutionary Computation with Intelligent Systems, 2022
J. Senthil Kumar, G. Sivasankar
Pose of the robot sensor changes based on the robot hardware configuration, ROS with relative coordinate transformation (TF), most useful parameters to describe the robot parts as well as objects and obstacles. The pose of a robot can be described as a combination of positions and orientations. Robot position estimation is performed using the given map and based on the encoder, IMU sensor, and the distance sensor. The Monte Carlo localization (MCL)-based pose estimation algorithm method, namely, particle filter, is widely used in the field of robot pose estimation. AMCL, an improved version of Monte Carlo pose estimation method, is used in TurtleBot3 to estimate the robot’s current pose. Various AMCL parameters are found in “amcl.launch.xml” file.
Localization Protocols for Wireless Sensor Networks
Published in Mohammed Usman, Mohd Wajid, Mohd Dilshad Ansari, Enabling Technologies for Next Generation Wireless Communications, 2020
Ash Mohammad Abbas, Hamzah Ali Abdul Rahman Qasem
A range-based version of Monte Carlo Localization (rMCL) is proposed in Dil, Dulman, and Havinga (2006) that improves the accuracy of localization by using the range information obtained from anchors that are lying at a distance of not more than two hops from the sensor to be localized. Therein, only the well-connected nodes are considered, i.e., nodes that have heard location information from three or more anchors. However, the improvement in accuracy is at the cost of spending more energy in communication with one another for forwarding positions of anchors.
Mobile-Robot Motion Control
Published in Wyatt S. Newman, A Systematic Approach to Learning Robot Programming with ROS, 2017
AMCL is a probabilistic localization system for a robot moving in 2D. It implements the adaptive (or KLD-sampling) Monte Carlo localization approach (as described by Dieter Fox), which uses a particle filter to track the pose of a robot against a known map.
Posture evaluation for mobile manipulators using manipulation ability, tolerance on grasping, and pose error of end-effector
Published in Advanced Robotics, 2021
Satoshi Suzuki, Daisuke Endo, Kimitoshi Yamazaki
To calculate the amount of error correction corresponding to the procedure described above, the Monte Carlo method is formulated. The one-pose variable, called particle, is sampled according to the probability distribution, and the particle of another distribution is generated using it. This is a part of a procedure used in the field of mobile robots and referred to as Monte Carlo localization [19]. However, the suggested procedure does not include the step of calculating the posterior distribution by weighting the particles. Therefore, we refer to this procedure as the Monte Carlo method.