The complexity of robot motion planning
The complexity of robot motion planning
A geometric approach to error detection and recovery for robot motion planning with uncertainty
Artificial Intelligence - Special issue on geometric reasoning
The complexity of planar compliant motion planning under uncertainty
SCG '88 Proceedings of the fourth annual symposium on Computational geometry
An efficient algorithm for one-step planar complaint motion planning with uncertainty
SCG '89 Proceedings of the fifth annual symposium on Computational geometry
Robot motion planning with uncertainty in control and sensing
Artificial Intelligence
Computational aspects of compliant motion planning
Computational aspects of compliant motion planning
Robot Motion Planning
On Motion Planning with Uncertainty
On Motion Planning with Uncertainty
Representation and automatic synthesis of reaction plans
Representation and automatic synthesis of reaction plans
New lower bound techniques for robot motion planning problems
SFCS '87 Proceedings of the 28th Annual Symposium on Foundations of Computer Science
Learning Combinatorial Map Information from Permutations of Landmarks
International Journal of Robotics Research
Planning for provably reliable navigation using an unreliable, nearly sensorless robot
International Journal of Robotics Research
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To operate in the real world robots must deal with errors in control and sensing. Achieving goals despite these errors requires complex motion planning and plan monitoring. We present a reduced version of the general problem and a complete planner that solves it in polynomial time. The basic concept underlying this planner is that of a landmark. Within the field of influence of a landmark, robot control and sensing are perfect. Outside any such field control is imperfect and sensing is null. In order to make sure that the above assumptions hold, we may have to specifically engineer the robot workspace. Thus, for the first time, workspace engineering is seen as a means to make planning problems tractable. The planner was implemented and experimental results are presented. An interesting feature of the planner is that it always returns a universal plan in the form of a collection of reaction rules. This plan can be used even when the input problem has no guaranteed solution, or when unexpected events oceur during plan execution.