Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Odin: Team VictorTango's entry in the DARPA Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Caroline: An autonomously driving vehicle for urban environments
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part II
Driving with tentacles: Integral structures for sensing and motion
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part II
Little Ben: The Ben Franklin Racing Team's entry in the 2007 DARPA Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part II
A perception-driven autonomous urban vehicle
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part III
Motion planning in urban environments
Journal of Field Robotics
Survivability: measuring and ensuring path diversity
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Toward a deeper understanding of motion alternatives via an equivalence relation on local paths
International Journal of Robotics Research
Real-time informed path sampling for motion planning search
International Journal of Robotics Research
Integrated motion planning and control for graceful balancing mobile robots
International Journal of Robotics Research
Online obstacle avoidance at high speeds
International Journal of Robotics Research
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The goal of motion planning is to find a feasible path that connects two positions and is free from collision with obstacles. Path sets are a robust approach to this problem in the face of real-world complexity and uncertainty. A path set is collection of feasible paths and their corresponding control sequences. A path-set-based planner navigates by repeatedly tessting each of these robot-fixed pathes for collision wih obstacles. A heurisic function selects which of the surviving paths to follow next. At each step, the robot follows a small piece of each path selected while simultaneously planning the subsequent trajectory. A path set possesses hih path diversity if it performs well at obstacle-avoidance and goal-seeking behaviors. Previous work in path diversity has tacitly assumed that a correlation exists between this dynamic planning problem and a simpler, static path diversity problem: a robot placed randomly into an obstacle field evaluates its path set for collision a single time before following the chosen path in entirety. Although these problems might intuitively appear to be linked, this paper shows that static and dynamic path diversity are two distinct properties. After empirically demonsrating this fact, we discuss some of the factors that differentiate the two problems.