Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
On the Probabilistic Foundations of Probabilistic Roadmap Planning
International Journal of Robotics Research
Spatial Planning: A Configuration Space Approach
IEEE Transactions on Computers
The focussed D* algorithm for real-time replanning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Planning and acting in partially observable stochastic domains
Artificial Intelligence
The ultra-wide bandwidth indoor channel: from statistical model to simulations
IEEE Journal on Selected Areas in Communications
Sampling-based algorithms for optimal motion planning
International Journal of Robotics Research
LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information
International Journal of Robotics Research
Active exploration for robust object detection
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Motion planning under uncertainty using iterative local optimization in belief space
International Journal of Robotics Research
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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When a mobile agent does not know its position perfectly, incorporating the predicted uncertainty of future position estimates into the planning process can lead to substantially better motion performance. However, planning in the space of probabilistic position estimates, or belief space, can incur a substantial computational cost. In this paper, we show that planning in belief space can be performed efficiently for linear Gaussian systems by using a factored form of the covariance matrix. This factored form allows several prediction and measurement steps to be combined into a single linear transfer function, leading to very efficient posterior belief prediction during planning. We give a belief-space variant of the probabilistic roadmap algorithm called the belief roadmap (BRM) and show that the BRM can compute plans substantially faster than conventional belief space planning. We conclude with performance results for an agent using ultra-wide bandwidth radio beacons to localize and show that we can efficiently generate plans that avoid failures due to loss of accurate position estimation.