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Abstract: In this work we present a methodology for intelligent path planning in an uncertain environment using vision-like sensors, i.e., sensors that allow the sensing of the environment non-locally. Examples would include a mobile robot exploring an unknown terrain or a micro-UAV navigating in a cluttered urban environment. We show that the problem of path planning in an uncertain environment, under certain assumptions, can be posed as the adaptive optimal control of an uncertain Markov decision process, characterized by a known, control-dependent system, and an unknown, control-independent environment. The strategy for path planning then reduces to computing the control policy based on the current estimate of the environment, also known as the “certainty-equivalence” principle in the adaptive control literature. Our methodology allows the inclusion of vision-like sensors into the problem formulation, which, as empirical evidence suggests, accelerates the convergence of the planning algorithms. Further we show that the path planning and estimation problems, as formulated in this paper, possess special structure which can be exploited to significantly reduce the computational burden of the associated algorithms. We apply this methodology to the problem of path planning of a mobile rover in a completely unknown terrain. [Copyright &y& Elsevier]

Introduces an approach for planning minimum-control optimal trajectories for a two-wheeled mobile robot in the absence of obstacles. Formulation of the trajectory planning in minimum-control, fixed-time and optimal control problems with no terminal cost; Presentation of some of the simulation results of the relaxation method; Solving of the two-point boundary value problem; Discussion of the procedure for constants of motion.

We are developing an autonomous mobile robotic system to emulate six degree of freedom relative spacecraft motion during proximity operations. A mobile omni-directional base robot provides x, y, and yaw planar motion with moderate accuracy through six independently driven motors. With a six degree of freedom micro-positioning Stewart platform on top of the moving base, six degree of freedom spacecraft motion can be emulated with high accuracy. This paper presents our approach to dynamic modeling, control, and simulation for the overall system. Compared with other simulations that introduced significant simplifications, we believe that our rigorous modeling approach is crucial for the high fidelity hardware in-the-loop emulation. [ABSTRACT FROM AUTHOR]