Vision-Based Obstacle Avoidance for UAVs

This paper describes a vision-based navigation and guidance design for UAVs for a combined mission of waypoint tracking and collision avoidance with unforeseen obstacles using a single 2-D passive vision sensor. An extended Kalman fllter (EKF) is applied to estimate a relative position of obstacles from vision-based measurements. The stochastic z-test value is used to solve a correspondence problem between the measurements and the estimates that have been already obtained by then. A collision cone approach is used as a collision criteria in order to examine if there is any obstacle that is critical to the vehicle. A guidance strategy for collision avoidance is designed based on a minimum-efiort guidance (MEG) method for multiple target tracking. The vision-based navigation and guidance designs suggested in this paper are integrated with realtime image processing algorithm and the entire vision-based control system are evaluated in the closed-loop 6 DoF ∞ight simulation.

[1]  C. Alvino,et al.  Active contours and optical flow for automatic tracking of flying vehicles , 2004, Proceedings of the 2004 American Control Conference.

[2]  Joseph Z. Ben-Asher,et al.  Advances in Missile Guidance Theory , 1998 .

[3]  Debasish Ghose,et al.  Obstacle avoidance in a dynamic environment: a collision cone approach , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[4]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .

[5]  A.J. Calise,et al.  Estimation and guidance strategies for vision based target tracking , 2005, Proceedings of the 2005, American Control Conference, 2005..

[6]  Eric N. Johnson,et al.  The Georgia Tech Unmanned Aerial Research Vehicle: GTMax , 2003 .

[7]  Max Donath,et al.  American Control Conference , 1993 .

[8]  George M. Siouris,et al.  Applied Optimal Control: Optimization, Estimation, and Control , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  Eric N. Johnson,et al.  Minimum-Effort Guidance for Vision-Based Collision Avoidance , 2006 .

[10]  Paul Zarchan,et al.  Fundamentals of Kalman Filtering: A Practical Approach , 2001 .

[11]  M. Degroot,et al.  Probability and Statistics , 2021, Examining an Operational Approach to Teaching Probability.

[12]  Y.K. Kwag,et al.  Obstacle awareness and collision avoidance radar sensor system for low-altitude flying smart UAV , 2004, The 23rd Digital Avionics Systems Conference (IEEE Cat. No.04CH37576).

[13]  Joseph Z. Ben-Asher.  Minimum-effort interception of multiple targets , 1992 .

[14]  Debasish Ghose,et al.  Radar-assisted collision avoidance/guidance strategy for planar flight , 2001 .

[15]  Paul Zarchan,et al.  Tactical and strategic missile guidance , 1990 .

[16]  Eric N. Johnson,et al.  Real-Time Vision-Based Relative Aircraft Navigation , 2007, J. Aerosp. Comput. Inf. Commun..

[17]  William Et.Al Hines,et al.  Probability and Statistics in Engineering , 2003 .

[18]  Eric N. Johnson,et al.  Adaptive Flight Control for an Autonomous Unmanned Helicopter , 2002 .