Mobile Robots Navigation

Edited by
July 2020
298 pages
  • ISBN978-3-03928-670-6 (Hardback)
  • ISBN978-3-03928-671-3 (PDF)

This book is a reprint of the Special Issue Mobile Robots Navigation that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Environmental & Earth Sciences
Physical Sciences

The presence of mobile robots in diverse scenarios is considerably increasing to perform a variety of tasks. Among them, many developments have occurred in the fields of ground, underwater, and flying robotics. Independent of the environment where they move, navigation is a fundamental ability of mobile robots so that they can autonomously complete high-level tasks. This problem can be efficiently addressed through the following actions: First, it is necessary to perceive the environment in which the robot has to move, and extract some relevant information (mapping problem). Second, the robot must be able to estimate its position and orientation within this environment (localization problem). With this information, a trajectory toward the target points must be planned (path planning), and the vehicle must be reactively guided along this trajectory considering either possible changes or interactions with the environment or with the user (control). Given this  information, this book introduces current frameworks in these fields (mapping, localization, path planning, and control) and, in general, approaches to any problem related to the navigation of mobile robots, such as odometry, exploration, obstacle avoidance, and simulation.

  • Hardback
© 2020 by the authors; CC BY licence
visual-inertial odometry; cubature information filter; navigation; IMU; RGBD camera; mapping; localization; clustering; omnidirectional images; global appearance descriptors; autonomous navigation; dynamic environments; Deep Reinforcement Learning; geometrical path planner; coverage flight path planning; footprints sweep; waypoint graph; navigation; urban environments; unmanned aerial vehicles; artificial potential field; path planning; obstacle avoidance; dynamic window; danger index; quadruped robot; energy model; foot force distribution; cubic spline interpolation; terrain classification; image infilling method; multilegged robot; autonomous navigation; mobile robots; Monte Carlo localization; SLAM; GNSS; planning; control; Kalman filter; distance map; incremental algorithms; canonical ordering; path planning; subgoal graph; industrial robotics; robotic deburring; tool path planning; process parameter control; dexterous manipulation; space robotics; redundant; free-floating base; multiple tasks; trajectory planning; genetic algorithm; multi-robot systems; multi-objective optimization; grey wolf optimizer; waypoints; exploration; uncertainties; unknown environment; mapping; grid map occupancy; reinforcement learning; multi-robots; cooperation; Deep q learning; Convolution Neural Network; unmanned surface vehicles; path planning; hydrodynamics; electronic navigation chart; numerical simulation; water depth risk; n/a