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A Hybrid Path-Planning Strategy for Mobile Robots with Limited Sensor Capabilities.

Guilherme Carlos R de Oliveira1, Kevin B de Carvalho2, Alexandre S Brandão3

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Path planning is crucial for autonomous systems.
  • Limited sensor and processing capabilities pose challenges for mobile robots.
  • Existing algorithms often require prior environmental knowledge or significant computational resources.

Purpose of the Study:

  • To develop a novel, resource-efficient path planning strategy for platforms with constrained capabilities.
  • To create a hybrid approach integrating global and local planning methods.
  • To validate the algorithm's effectiveness in real-world, cluttered environments.

Main Methods:

  • A hybrid path planning algorithm combining a global planner for known map areas and a local planner for real-time obstacle avoidance.
  • The algorithm operates without prior environmental information, relying on an integrated mapping capability.
  • Utilized a Pioneer P3-DX robot equipped with an Xbox 360 Kinect sensor for experimental validation.

Main Results:

  • The proposed hybrid algorithm successfully generated sub-optimal, collision-free paths in a known map.
  • Real-time obstacle avoidance was achieved efficiently using the cost-effective local planner.
  • Experimental validation demonstrated the algorithm's practical applicability and efficiency in a cluttered environment.

Conclusions:

  • The developed hybrid path planning strategy is effective for resource-constrained platforms.
  • The integration of global and local planning offers a robust solution for autonomous navigation.
  • The algorithm's efficiency and adaptability make it suitable for real-world robotic applications.