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This brief presents a framework for input-optimal navigation under state constraints for vehicles exhibiting stochastic behavior. The resulting stochastic control law is implementable in real time on vehicles with limited computational power. When control actuation is unconstrained, then convergence with probability 1 can be theoretically guaranteed. When inputs are bounded, the probability of convergence...
This paper suggests a method for numerically constructing almost globally converging artificial potential fields for motion planning, in a way that ensures that the resulting gradient field is compatible with the dynamics of the navigating robot. Convergence to an arbitrarily small destination set can be guaranteed, and the size of the destination set can be reduced at the expense of additional off-line...
This paper addresses the problem of optimal control of a unicycle-type robot perturbed with stochastic noise in an environment with sparsely populated obstacles. The objective is that the robot pose converges to a neighborhood of a desired position and orientation. A feedback control law is constructed such that it is compatible with the differential constraints of the unicycle. The construction is...
The analysis in this paper applies to robots with dynamics described by a stochastic differential equation, which need to navigate in constrained environments. The approach offers a method to calculate the probability that a feedback control policy designed for the drift component of the dynamics, will succeed in allowing the robot to avoid collisions and converge to its navigation goal in the presence...
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