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Obstacle Avoidance of UAV in Dynamic Environments Using Direction and Velocity-Adaptive Artificial Potential Field

Nikita Vaibhav Pavle, Shrreya Rajneesh, Rakesh Kumar Sahoo, Manoranjan Sinha

TL;DR

The paper addresses UAV collision avoidance in dynamic, cluttered airspace, where conventional APF struggles with local minima and moving obstacles. It introduces a Direction and Relative Velocity Weighted APF, with a bounded weight $ω(θ,v_e)$ that accounts for obstacle direction and relative speed, integrated into a Model Predictive Control framework to produce collision-free trajectories under kinematic constraints. Simulation results show the velocity-weighted APF resolves local minima, yields smoother avoidance maneuvers, and improves obstacle clearance and goal convergence compared to basic APF. This approach offers a practical, computationally efficient pathway to robust autonomous UAV navigation in complex, dynamic environments.

Abstract

The conventional Artificial Potential Field (APF) is fundamentally limited by the local minima issue and its inability to account for the kinematics of moving obstacles. This paper addresses the critical challenge of autonomous collision avoidance for Unmanned Aerial Vehicles (UAVs) operating in dynamic and cluttered airspace by proposing a novel Direction and Relative Velocity Weighted Artificial Potential Field (APF). In this approach, a bounded weighting function, $ω(θ,v_{e})$, is introduced to dynamically scale the repulsive potential based on the direction and velocity of the obstacle relative to the UAV. This robust APF formulation is integrated within a Model Predictive Control (MPC) framework to generate collision-free trajectories while adhering to kinematic constraints. Simulation results demonstrate that the proposed method effectively resolves local minima and significantly enhances safety by enabling smooth, predictive avoidance maneuvers. The system ensures superior path integrity and reliable performance, confirming its viability for autonomous navigation in complex environments.

Obstacle Avoidance of UAV in Dynamic Environments Using Direction and Velocity-Adaptive Artificial Potential Field

TL;DR

The paper addresses UAV collision avoidance in dynamic, cluttered airspace, where conventional APF struggles with local minima and moving obstacles. It introduces a Direction and Relative Velocity Weighted APF, with a bounded weight that accounts for obstacle direction and relative speed, integrated into a Model Predictive Control framework to produce collision-free trajectories under kinematic constraints. Simulation results show the velocity-weighted APF resolves local minima, yields smoother avoidance maneuvers, and improves obstacle clearance and goal convergence compared to basic APF. This approach offers a practical, computationally efficient pathway to robust autonomous UAV navigation in complex, dynamic environments.

Abstract

The conventional Artificial Potential Field (APF) is fundamentally limited by the local minima issue and its inability to account for the kinematics of moving obstacles. This paper addresses the critical challenge of autonomous collision avoidance for Unmanned Aerial Vehicles (UAVs) operating in dynamic and cluttered airspace by proposing a novel Direction and Relative Velocity Weighted Artificial Potential Field (APF). In this approach, a bounded weighting function, , is introduced to dynamically scale the repulsive potential based on the direction and velocity of the obstacle relative to the UAV. This robust APF formulation is integrated within a Model Predictive Control (MPC) framework to generate collision-free trajectories while adhering to kinematic constraints. Simulation results demonstrate that the proposed method effectively resolves local minima and significantly enhances safety by enabling smooth, predictive avoidance maneuvers. The system ensures superior path integrity and reliable performance, confirming its viability for autonomous navigation in complex environments.

Paper Structure

This paper contains 23 sections, 24 equations, 5 figures.

Table of Contents

  1. Introduction
  2. System Modeling and Control Architecture
  3. UAV Dynamic and Kinematic Modeling
  4. Linear Motion Dynamics
  5. Angular Motion Dynamics
  6. Continuous-Time Model
  7. Discrete-Time Model
  8. Model Predictive Control (MPC) Formulation
  9. State and Input Sequences
  10. Cost Function and Optimization
  11. Direction and Velocity Weighted Artificial Potential Field
  12. Classical Repulsive Potential Field
  13. Direction–Weighted APF
  14. Direction and Relative–Velocity Weighted APF
  15. Simulation and Performance Analysis
  16. ...and 8 more sections

Figures (5)

  • Figure 1: Comparison of local minima avoidance. (Left) Basic APF trajectory stalls/hesitates near the obstacle cluster (local minimum). (Right) Velocity-weighted APF successfully navigates around the cluster.
  • Figure 2: Comparison between the path of Basic APF and velocity-based APF.
  • Figure 3: Distance to obstacles over time. (Left) Basic APF. (Right) Velocity-weighted APF.
  • Figure 4: Distance to goal over time. (Left) Basic APF. (Right) Velocity-weighted APF.
  • Figure 5: 3D visualization of the velocity-weighted APF trajectory navigating around cylindrical obstacles (red). The UAV successfully maintains a fixed altitude (z-axis) while weaving a stable, collision-free path toward the blue target ring.