Autonomous behaviors for interactive vehicle animations

ABSTRACT

We present a method for synthesizing animations of autonomous space, water, and land-based vehicles in games or other interactive simulations. Controlling the motion of such vehicles to achieve a desirable behavior is difficult due to the constraints imposed by the system dynamics. We combine real-time path planning and a simplified physics model to automatically compute control actions to drive a vehicle from an input state to desirable output states based on a behavior cost function. Both offline trajectory preprocessing and online search are used to build an animation framework suitable for interactive vehicle simulations. We demonstrate synthesized animations of spacecraft performing a variety of autonomous behaviors, including <i>Seek, Pursue, Avoid, Avoid Collision</i>, and <i>Flee</i>. We also explore several enhancements to the basic planning algorithm and examine the resulting tradeoffs in runtime performance and quality of the generated motion.

REFERENCES

• 1: David C. Brogan , Jessica K. Hodgins, Group Behaviors for Systems with Significant Dynamics, Autonomous Robots, v.4 n.1, p.137-153, March 1997 [doi>10.1023/A:1008867321648]
• 2: David C. Brogan , Jessica K. Hodgins, Simulation level of detail for multiagent control, Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1, July 15-19, 2002, Bologna, Italy [doi>10.1145/544741.544789]
• 3: {BLA02} Bayazit O. B., Lien J.-M., Amato N. M.: Better group behaviors using rule-based roadmaps. In Proc. Int. Wkshp. on Alg. Found. of Rob. (WAFR) (Nice, France, 2002).
• 4: {BV02} Bruce J., Veloso M.: Real-time randomized path planning for robot navigation. In Proc. Int. Conf. Intelligent Robots and Systems (IROS) (2002).
• 5: {FDF02} Frazzoli E., Dahleh M., Feron E.: Real-time motion planning for agile autonomous vehicles. AIAA Journal of Guidance, Control, and Dynamics 25, 1 (2002), 116--129.
• 6: Emilio Frazzoli , Munther A. Dahleh , Eric Feron, Robust hybrid control for autonomous vehicle motion planning, 2001
• 7: {GRD96} Gilks W., Richardson S., D. Spiegelhalter: Markov Chain Monte Carlo in Practice. Chapman and Hall, 1996.
• 8: {LaV98} Lavalle S. M.: Rapidly-exploring random trees: A new tool for path planning. TR 98-11, Computer Science Dept., Iowa State Univ., Oct. 1998.
• 9: {LJC01} Li T., Jeng J., Chang S.: Simulating virtual human crowds with a leader-follower model. In Proc. IEEE Int'l Conf. on Robotics and Automation (ICRA) (2001).
• 10: {LK99} Lavalle S., Kuffner J.: Randomized kinodynamic planning. In Proc. IEEE Int'l Conf. on Robotics and Automation (ICRA'99) (Detroit, MI, May 1999).
• 11: Craig W. Reynolds, Flocks, herds and schools: A distributed behavioral model, Proceedings of the 14th annual conference on Computer graphics and interactive techniques, p.25-34, August 1987
• 12: {Rey99} Reynolds C.: Steering behaviors for autonomous characters. In Game Developers Conference 1999 (1999).
• 13: Bohdana Ratitch , Doina Precup, Characterizing Markov Decision Processes, Proceedings of the 13th European Conference on Machine Learning, p.391-404, August 19-23, 2002
• 14: Csaba Szepesvári, Efficient approximate planning in continuous space Markovian decision problems, AI Communications, v.14 n.3, p.163-176, September 2001
• 15: {VSH*99} Vaughan R., Sumpter N., Henderson J., Frost A., Cameron S.: Experiments in automatic flock control. In Proc. RAS (1999).

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.3 COMPUTER GRAPHICS
      I.3.3 Picture/Image Generation

Keywords:
online search, path planning, real-time animation, steering behaviors, vehicle motion