Collision avoidance
Related papers: 20
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Collision avoidance is the set of methods and algorithms that enable robots and autonomous systems to detect, predict, and evade potential impacts with static or moving obstacles in their environment. Techniques range from classical approaches—such as artificial potential fields, which model obstacles as repulsive forces, and the Dynamic Window Approach, which constrains motion commands to dynamically feasible velocities—to geometric methods like velocity obstacles and collision cones that reason about future trajectories in velocity space. In robotics, collision avoidance is applied across mobile ground robots, robotic manipulators sharing workspaces with humans, aerial vehicles navigating cluttered environments, and multi-robot systems requiring coordinated, interference-free motion. Modern implementations increasingly leverage deep reinforcement learning to handle complex, dynamic, and socially constrained scenarios. The field matters because it is foundational to safe autonomous operation: without reliable collision avoidance, robots cannot function effectively alongside people, infrastructure, or other machines, making it a prerequisite for deployment in real-world industrial, medical, and service applications.
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Real-Time Obstacle Avoidance for Manipulators and Mobile Robots
Oussama Khatib
Citations: 7533 • 1986
The dynamic window approach to collision avoidance
D. Fox, Wolfram Burgard, Sebastian Thrun
Citations: 3622 • 1997
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
Reciprocal n-Body Collision Avoidance
Jur van den Berg, Stephen J. Guy, Ming–Chieh Lin, Dinesh Manocha
Citations: 1811 • 2011
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
Full control of a quadrotor
Samir Bouabdallah, Roland Siegwart
Citations: 796 • 2007
Collision Detection and Safe Reaction with the DLR-III Lightweight Manipulator Arm
Alessandro De Luca, Alin Albu‐Schäffer, Sami Haddadin, Gerd Hirzinger
Citations: 763 • 2006
Safety Barrier Certificates for Collisions-Free Multirobot Systems
Li Wang, Aaron D. Ames, Magnus Egerstedt
Citations: 747 • 2017
Socially aware motion planning with deep reinforcement learning
Yu Fan Chen, Michael Everett, Miao Liu, Jonathan P. How
Citations: 715 • 2017
Collision Detection and Reaction: A Contribution to Safe Physical Human-Robot Interaction
Sami Haddadin, Alin Albu‐Schäffer, Alessandro De Luca, Gerd Hirzinger
Citations: 560 • 2008
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
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Pinxin Long, Tingxiang Fanl, Xinyi Liao, Wenxi Liu, Hao Zhang, Jia Pan
Citations: 533 • 2018
The curvature-velocity method for local obstacle avoidance
Reid Simmons
Citations: 527 • 2002
Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey
Michael Hoy, Alexey S. Matveev, Andrey V. Savkin
Citations: 466 • 2014
Obstacle avoidance with ultrasonic sensors
J. Borenstein, Yoram Koren
Citations: 456 • 1988
Nearness Diagram (ND) Navigation: Collision Avoidance in Troublesome Scenarios
Javier Mínguez, Luis Montano
Citations: 453 • 2004