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Related Concept Videos

Controller Configurations01:22

Controller Configurations

84
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
84

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A Fuzzy Control Strategy for Multi-Goal Autonomous Robot Navigation.

Stavros Stavrinidis1, Paraskevi Zacharia1, Elias Xidias2

  • 1Department of Industrial Design and Production Engineering, University of West Attica, Egaleo, 12241 Athens, Greece.

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This study presents a novel robot navigation system for multi-goal tasks, optimizing paths using the Traveling Salesman Problem (TSP) and fuzzy logic for dynamic obstacle avoidance. The method ensures efficient and collision-free autonomous navigation in complex environments.

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CoppeliaSimTSPcollision avoidancefuzzy controllergenetic algorithmsmobile robotnavigationpath planningsensors

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Multi-goal robot navigation is challenging, often modeled as the NP-hard Traveling Salesman Problem (TSP).
  • Environments with static and dynamic obstacles require adaptive path planning and obstacle avoidance strategies.
  • Existing methods may struggle with real-time adjustments in unpredictable scenarios.

Purpose of the Study:

  • To develop an integrated framework for efficient multi-goal robot navigation.
  • To optimize collision-free path planning in environments with mixed obstacles.
  • To enhance autonomous robot adaptability and safety through real-time trajectory adjustments.

Main Methods:

  • A novel path planning algorithm based on the Bump-Surface concept for optimizing shortest collision-free paths among static obstacles.
  • A Genetic Algorithm (GA) to determine the optimal sequence of goal points for multi-goal navigation.
  • Two fuzzy controllers for real-time path tracking and dynamic obstacle avoidance, enabling adaptive trajectory adjustments.

Main Results:

  • The proposed method effectively optimizes multi-goal navigation paths, considering both static and dynamic obstacles.
  • Simulations in CoppeliaSim demonstrated robust navigation and successful collision avoidance in realistic, unpredictable environments.
  • The integrated system shows significant advancement in autonomous robot navigation capabilities.

Conclusions:

  • The combination of TSP optimization, Bump-Surface path planning, and fuzzy logic controllers provides a comprehensive solution for complex robot navigation tasks.
  • This approach enables robots to navigate autonomously and safely in dynamic environments by adapting trajectories in real-time.
  • The validated method offers a robust framework for future autonomous systems requiring intelligent path planning and obstacle management.