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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Mechanical Engineering

Background:

  • Motion planning for articulated logistic vehicles, like tugger trains, is complex due to their multiarticulated kinematics.
  • Existing sampling-based motion planners struggle with high-dimensional systems and large maps, leading to long planning times.
  • Current hybrid approaches using discrete search heuristics can mislead planners by ignoring vehicle kinematic and dynamic constraints.

Purpose of the Study:

  • To develop an efficient and robust motion planning solution for articulated logistic vehicles.
  • To address the limitations of existing sampling-based and discrete search methods in handling complex vehicle dynamics.
  • To improve the reliability and speed of motion planning for autonomous robotic systems in logistics.

Main Methods:

  • A novel approach combining kinodynamic discrete planning with sampling-based motion planners is proposed.
  • The kinodynamic planning component provides informed heuristics to guide the sampling process.
  • The integrated system was tested on tricycle and quadricycle towing tractors with simple and synchronized shaft trailers.

Main Results:

  • The proposed kinodynamic discrete planning effectively biases sampling-based algorithms for articulated vehicles.
  • The method demonstrated improved efficiency and feasibility in motion planning tasks for complex vehicle configurations.
  • Successful application across different towing tractor and trailer combinations validates the system's adaptability.

Conclusions:

  • The presented kinodynamic discrete planning approach offers a significant advancement in motion planning for articulated logistic vehicles.
  • This method overcomes the limitations of traditional planners by incorporating vehicle-specific constraints.
  • The developed system provides a more efficient and reliable solution for service robotics in logistics and warehousing environments.