Advances in Collision Avoidance and Motion Planning for Autonomous Robots

The field of autonomous robotics is moving towards more efficient and safe navigation in complex environments. Recent developments focus on improving collision avoidance and motion planning algorithms to enable smoother trajectories and stronger collision avoidance guarantees. Researchers are exploring the use of fuzzy logic controllers, energy-based artificial potential fields, and learning-based frameworks to overcome the limitations of traditional path planners. These innovative approaches are being applied to various robotic systems, including robotic manipulators and mobile robots, to achieve collision-free and time-efficient motion planning. Notable papers in this area include:

  • Improved Obstacle Avoidance for Autonomous Robots with ORCA-FLC, which proposes a fuzzy logic controller to improve obstacle avoidance in path planning.
  • Towards Safe Imitation Learning via Potential Field-Guided Flow Matching, which presents a novel approach to safe motion generation through imitation learning.
  • Reactive Model Predictive Contouring Control for Robot Manipulators, which introduces a framework for robot path-following that successfully avoids obstacles and singularities in dynamic environments.

Sources

Improved Obstacle Avoidance for Autonomous Robots with ORCA-FLC

Collision-Free Trajectory Planning and control of Robotic Manipulator using Energy-Based Artificial Potential Field (E-APF)

A Learning-Based Framework for Collision-Free Motion Planning

Deep Reinforcement Learning with anticipatory reward in LSTM for Collision Avoidance of Mobile Robots

Towards Safe Imitation Learning via Potential Field-Guided Flow Matching

Reactive Model Predictive Contouring Control for Robot Manipulators

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