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Obstacle avoidance planning of space manipulator end-effector based on improved ant colony algorithm.

Dongsheng Zhou1, Lan Wang1, Qiang Zhang1

  • 1Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian, 116622 China.

Springerplus
|May 18, 2016
PubMed
Summary

An improved ant colony algorithm enhances obstacle avoidance for space manipulator end-effectors. This method effectively navigates complex space environments, improving on-orbit servicing mission planning.

Keywords:
Improved ant colony algorithmObstacle avoidancePath planningSpace manipulator

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Area of Science:

  • Aerospace Engineering
  • Robotics
  • Artificial Intelligence

Background:

  • Space on-orbit servicing is increasingly important in aerospace engineering.
  • Obstacle avoidance for space manipulator end-effectors is a critical and complex challenge.
  • Effective path planning is essential for successful space missions.

Purpose of the Study:

  • To propose an improved ant colony algorithm for space manipulator obstacle avoidance.
  • To enhance the efficiency and effectiveness of path planning in complex space environments.
  • To address the limitations of classic algorithms in avoiding local optima.

Main Methods:

  • Developed kinematic models for space manipulators and valid path expressions.
  • Designed an improved ant colony algorithm with adjusted search strategies, transfer rules, and pheromone updates.
  • Compared the proposed algorithm with the classic ant colony algorithm through simulations.

Main Results:

  • The improved ant colony algorithm demonstrated effectiveness in obstacle avoidance.
  • The algorithm successfully avoided trapping into local optima, a common issue in path planning.
  • Simulation results validated the correctness and superior performance of the proposed method.

Conclusions:

  • The improved ant colony algorithm is a viable and effective solution for space manipulator obstacle avoidance.
  • This algorithm contributes to safer and more efficient on-orbit servicing operations.
  • The study highlights the potential of enhanced swarm intelligence algorithms in aerospace applications.