Obstacle
Related papers: 20
About
An obstacle, in robotics and AI contexts, refers to any physical object, structure, or dynamic agent that occupies space within a robot's environment and must be detected, modeled, and avoided to ensure safe and effective operation. Obstacles can be static — such as walls, furniture, or terrain features — or dynamic, including moving people, vehicles, or other robots. Obstacle detection and avoidance are foundational challenges across virtually all robotic platforms, from mobile ground robots and aerial drones to robotic manipulators operating in cluttered workspaces. Robots address obstacles through a range of techniques, including artificial potential fields, vector field histograms, velocity obstacle methods, and deep reinforcement learning-based planners. Sensors such as ultrasonic rangers, LiDAR, and cameras provide real-time environmental data that feed into these algorithms. The goal is typically to compute collision-free trajectories or steering commands that allow the robot to reach its target efficiently while respecting kinematic and dynamic constraints. Reliable obstacle handling is critical because it directly determines whether a robotic system can operate safely alongside humans and other agents in unstructured, real-world environments — making it one of the most studied and practically significant problems in the field.
Top Researchers
Top Cited Papers
Real-Time Obstacle Avoidance for Manipulators and Mobile Robots
Oussama Khatib
Citations: 7533 • 1986
The vector field histogram-fast obstacle avoidance for mobile robots
J. Borenstein, Yoram Koren
Citations: 2278 • 1991
Motion Planning in Dynamic Environments Using Velocity Obstacles
Paolo Fiorini, Zvi Shiller
Citations: 1930 • 1998
Real-time obstacle avoidance for manipulators and mobile robots
Oussama Khatib
Citations: 1684 • 2005
Real-Time Obstacle Avoidance for Manipulators and Mobile Robots
Oussama Khatib
Citations: 1557 • 1986
Potential field methods and their inherent limitations for mobile robot navigation
Yoram Koren, J. Borenstein
Citations: 1552 • 2002
Real-time obstacle avoidance for fast mobile robots
J. Borenstein, Yoram Koren
Citations: 1254 • 1989
Robots that can adapt like animals
Antoine Cully, Jeff Clune, Danesh Tarapore, Jean-Baptiste Mouret
Citations: 948 • 2015
Obstacle avoidance and navigation in the real world by a seeing robot rover
Hans Moravec
Citations: 857 • 2018
Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation
Giuseppe Paolo, Ming Liu
Citations: 800 • 2017
Vision and navigation for the Carnegie-Mellon Navlab
C. Thorpe, Martial Hebert, Takeo Kanade, Steven A. Shafer
Citations: 771 • 1988
Randomized Kinodynamic Motion Planning with Moving Obstacles
David Hsu, R. Kindel, Jean‐Claude Latombe, Stephen M. Rock
Citations: 768 • 2002
Kinematics and the Implementation of an Elephant's Trunk Manipulator and Other Continuum Style Robots
M.W. Hannan, Ian D. Walker
Citations: 733 • 2003
VFH+: reliable obstacle avoidance for fast mobile robots
Iwan Ulrich, J. Borenstein
Citations: 694 • 2002
Controlling formations of multiple mobile robots
Jaydev P. Desai, James Ostrowski, Vijay Kumar
Citations: 687 • 2002
Noise and the reality gap: The use of simulation in evolutionary robotics
Nick Jakobi, Phil Husbands, Inman Harvey
Citations: 618 • 1995
A simple motion-planning algorithm for general robot manipulators
Tomás Lozano‐Pérez
Citations: 616 • 1987
A Study on CNN Transfer Learning for Image Classification
Mahbub Hussain, Jordan J. Bird, Diego R. Faria
Citations: 607 • 2018
Real-time obstacle avoidance using harmonic potential functions
J.-O. Kim, P.K. Khosla
Citations: 564 • 2002
Obstacle avoidance in a dynamic environment: a collision cone approach
Animesh Chakravarthy, Debasish Ghose
Citations: 545 • 1998