Obstacle avoidance
Related papers: 20
About
Obstacle avoidance is the capability of a robot or autonomous system to detect objects in its environment and maneuver around them without collision. It encompasses both the perception of obstacles—using sensors such as ultrasonic range finders, cameras, or laser scanners—and the generation of safe motion commands in real time. Core techniques include artificial potential fields, which model obstacles as repulsive forces and goals as attractive ones; vector field histograms, which translate sensor readings into navigable directions; and velocity obstacle methods, which reason about collision risks directly in velocity space. These approaches apply equally to mobile ground robots, robotic manipulators, aerial vehicles, and assistive devices like powered wheelchairs. Modern methods increasingly leverage deep reinforcement learning and end-to-end learned policies to handle complex, dynamic, and unstructured environments. Obstacle avoidance is foundational to autonomous robotics because safe operation in real-world settings—among humans, moving objects, and unpredictable terrain—depends entirely on a robot's ability to perceive and react to hazards reliably and without human intervention.
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
Real-time obstacle avoidance for fast mobile robots
J. Borenstein, Yoram Koren
Citations: 1254 • 1989
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
CHOMP: Covariant Hamiltonian optimization for motion planning
Matt Zucker, Nathan Ratliff, Anca D. Dragan, Mihail Pivtoraiko, Matthew Klingensmith, Christopher M. Dellin, J. Andrew Bagnell, Siddhartha S Srinivasa
Citations: 738 • 2013
VFH+: reliable obstacle avoidance for fast mobile robots
Iwan Ulrich, J. Borenstein
Citations: 694 • 2002
Noise and the reality gap: The use of simulation in evolutionary robotics
Nick Jakobi, Phil Husbands, Inman Harvey
Citations: 618 • 1995
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
Swarm of micro flying robots in the wild
Xin Zhou, Xiangyong Wen, Zhepei Wang, Yuman Gao, Haojia Li, Qianhao Wang, Tiankai Yang, Haojian Lu, Yanjun Cao, Chao Xu, Fei Gao
Citations: 539 • 2022
High-speed navigation using the global dynamic window approach
Oliver Brock, Oussama Khatib
Citations: 536 • 2003
The curvature-velocity method for local obstacle avoidance
Reid Simmons
Citations: 527 • 2002
Real-time obstacle avoidance using harmonic potential functions
J.-O. Kim, P.K. Khosla
Citations: 519 • 1992
Temporal logic motion planning for dynamic robots
Georgios Fainekos, Antoine Girard, Hadas Kress‐Gazit, George J. Pappas
Citations: 460 • 2008
Deep reinforcement learning based mobile robot navigation: A review
Kai Zhu, Tao Zhang
Citations: 458 • 2021