Paper WeB4.6
Machin, Timothy (Air Force Institute of Technology), Leishman, Robert (Air Force Institute of Technology)
Implementation of the Rapidly-Exploring Random Belief Tree and Statistical Analysis of Functionality
Scheduled for presentation during the Regular Session "Path Planning I" (WeB4), Wednesday, June 22, 2022,
17:10−17:30, Divona-2
2022 International Conference on Unmanned Aircraft Systems (ICUAS), June 21-24, 2022, Dubrovnik, Croatia
This information is tentative and subject to change. Compiled on March 28, 2024
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Keywords Path Planning, Autonomy, Navigation
Abstract
In this paper, we implement the Rapidly-exploring Random Belief Tree (RRBT) in a test environment from the original work. The algorithm is modified to utilize a more formal definition of domination when performing partial ordering of beliefs, improving the method for appending and rewiring the tree. We also consider the probabilistic guarantees of the Rapidly-exploring Random Tree (RRT) algorithm compared to those clearly stated in the RRBT development, and more importantly, which are not included. Monte Carlo simulation of the RRBT algorithm in the test environment provides evidence for the exponential decay in the probability of failure as the number of planning iterations increases. If this assertion would prove true universally, future work can focus on computing minimum number of iterations necessary for a valid solution, and thus a minimum time to compute for safe paths through complex environments while accounting for agent, measurement, and environment uncertainties.
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