Biologically inspired interception: A comparison of pursuit and constant bearing strategies in the presence of sensorimotor delay

We investigate the effect of sensorimotor delay on the pursuit and interception control strategies in the context of robotics. A first-order lead-lag model is introduced to model interception as observed in nature. This model was studied via extensive simulations that incorporate both a sensorimotor delay and a compensatory feedforward controller, allowing examination of the effects of various sensorimotor delays over a range of target velocity to pursuer velocity ratios. It was found that, with appropriate tuning, both the pursuit and constant bearing strategies operate effectively over a wide range of sensorimotor delays. Additionally, for each strategy, the use of a generalised mean value for this gain provides near-optimal performance, compared to separately optimising the gain for each combination of velocity ratio and delay. Finally, it is demonstrated that the constant bearing approach intercepts the target approximately 20% faster, on average, than the pursuit strategy across varying delays.

[1]  Stephen P. Boyd,et al.  Receding Horizon Control , 2011, IEEE Control Systems.

[2]  Andrey V. Savkin,et al.  A biologically inspired method for vision-based docking of wheeled mobile robots , 2007, Robotics Auton. Syst..

[3]  W. H. Warren,et al.  Behavioral dynamics of intercepting a moving target , 2007, Experimental Brain Research.

[4]  N.E. Carey,et al.  Biologically inspired guidance for motion camouflage , 2004, 2004 5th Asian Control Conference (IEEE Cat. No.04EX904).

[5]  Cynthia F Moss,et al.  Echolocating Bats Use a Nearly Time-Optimal Strategy to Intercept Prey , 2006, PLoS biology.

[6]  R. Olberg,et al.  Prey pursuit and interception in dragonflies , 2000, Journal of Comparative Physiology A.

[7]  Anthony Leonardo,et al.  Internal models direct dragonfly interception steering , 2014, Nature.

[8]  Kimon P. Valavanis,et al.  Advances in Unmanned Aerial Vehicles: State of the Art and the Road to Autonomy , 2007 .

[9]  M. Srinivasan,et al.  Strategies for active camouflage of motion , 1995, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[10]  Mandyam V. Srinivasan,et al.  UAV Guidance: A Stereo-Based Technique for Interception of Stationary or Moving Targets , 2015, TAROS.

[11]  Kevin A. Wise,et al.  Optimal Control and the Linear Quadratic Regulator , 2013 .

[12]  Surya P. N. Singh,et al.  Integrated electro-aeromechanical structures for low-cost, self-deploying environment sensors and disposable UAVs , 2013, 2013 IEEE International Conference on Robotics and Automation.

[13]  T. Collett,et al.  Chasing behaviour of houseflies (Fannia canicularis) , 1974, Journal of comparative physiology.

[14]  Boumediene Belkhouche,et al.  Parallel navigation for reaching a moving goal by a mobile robot , 2006, Robotica.

[15]  Ludmila I. Kuzmina,et al.  Calculation of the pursuit curve length , 2013 .

[16]  Mandyam V Srinivasan,et al.  Visual control of navigation in insects and its relevance for robotics , 2011, Current Opinion in Neurobiology.

[17]  M. Rafie-Rad,et al.  Time-Optimal solutions of Parallel Navigation and Finsler geodesics , 2010, 1101.1537.

[18]  P. B. Sujit,et al.  A survey of autonomous landing techniques for UAVs , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).

[19]  Andreas Zell,et al.  An Onboard Monocular Vision System for Autonomous Takeoff, Hovering and Landing of a Micro Aerial Vehicle , 2012, Journal of Intelligent & Robotic Systems.

[20]  M. Egelhaaf,et al.  Chasing a dummy target: smooth pursuit and velocity control in male blowflies , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[21]  Gary D. Bernard,et al.  The pursuit response of the housefly and its interaction with the optomotor response , 2004, Journal of comparative physiology.

[22]  Rafael T. Yanushevsky,et al.  Modern Missile Guidance , 2007 .