Null space-based control with gain modulation applied to a MARV in backward movement
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel controller for multi-articulated robot vehicles (MARVs) moving backward. The controller effectively manages path following and obstacle avoidance, reducing jackknifing risk for improved maneuverability.
Area Of Science
- Robotics
- Control Systems
- Autonomous Navigation
Background
- Multi-articulated robot vehicles (MARVs) present unique control challenges due to their complex dynamics.
- Backward motion control and obstacle avoidance are critical for MARV applications in confined or dynamic environments.
- Jackknifing is a significant risk in articulated vehicle control, particularly during reverse maneuvers.
Purpose Of The Study
- To develop and validate a controller for guiding a MARV in reverse along a specified path.
- To integrate robust obstacle avoidance capabilities (both fixed and moving) into the MARV's control system.
- To mitigate the risk of jackknifing through adaptive control gain modulation.
Main Methods
- Utilized null space-based control techniques to manage concurrent path following and obstacle avoidance tasks.
- Implemented adaptive control gain modulation to prevent jackknifing during backward motion.
- Conducted laboratory-scale experiments with a MARV towing one and two trailers.
- Performed simulations with a MARV towing three trailers to assess scalability.
Main Results
- Experimental validation confirmed the controller's effectiveness in guiding a MARV with trailers.
- The controller successfully managed conflicting tasks of path following and obstacle avoidance.
- Demonstrated reduction in jackknifing risk through modulated control gains.
- Simulations indicated the controller's potential for larger articulated chains and avoidance of moving obstacles.
Conclusions
- The proposed controller enables stable and safe backward navigation for MARVs with multiple trailers.
- The null space-based approach effectively integrates path following and obstacle avoidance.
- The modulation of control gains is crucial for preventing jackknifing in articulated vehicles.
- The controller shows promise for real-world applications involving complex articulated robotic systems.
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