Enhanced Artificial Potential Field Method with Fuzzy Control Integration

The enhanced artificial potential field method integrated with fuzzy control represents a novel control strategy applicable across various domains. The artificial potential field technique is a physics-inspired control approach that governs object movement by establishing virtual potential fields. In implementation, this typically involves calculating attractive forces toward targets and repulsive forces from obstacles using gradient descent algorithms, often implemented through functions like computeAttractiveForce() and computeRepulsiveForce(). Meanwhile, fuzzy control employs fuzzy logic principles to handle system uncertainties and ambiguities, commonly utilizing membership functions and rule-based inference systems. The integration of these two methodologies enables superior performance in complex environments and systems, significantly enhancing control effectiveness. For instance, in robotics applications, the enhanced artificial potential field method facilitates improved obstacle avoidance and path planning through potential field gradient calculations, while the fuzzy control component enhances the robot's adaptability across diverse scenarios by dynamically adjusting control parameters based on environmental fuzzy inputs. The combined approach typically involves fuzzy logic controllers modifying potential field parameters in real-time, creating a more robust and intelligent navigation system.