This paper proposes a technique of iterative dynamic programming to plan minimum energy consumption trajectories for robotic manipulators. The dynamic programming method is modijied to perform a series of dynamic programming passes over a small reconjgurable grid covering only a portion of the solution space at any one pass. Although strictly no longer a global optimization process, this iterat...

We develop an exact dynamic programming algorithm for partially observable stochastic games (POSGs). The algorithm is a synthesis of dynamic programming for partially observable Markov decision processes (POMDPs) and iterative elimination of dominated strategies in normal form games. We prove that it iteratively eliminates very weakly dominated strategies without first forming the normal form r...

In this paper, we presented an optimal iterative decision rule for minimizing total cost in designing a sampling plan for machine replacement problem using the approach of dynamic programming and Bayesian inferences. Cost of replacing the machine and cost of defectives produced by machine has been considered in model. Concept of control threshold policy has been applied for decision making. If ...

Obtaining the optimal control policy of a fedbatch bioreactor is a difficult task due to the low sensitivity of the feed rate on the objective function. In this work, iterative dynamic programming was selected for identification of optimal control policies of two fedbatch bioreactors for citric acid production. In the first application the raw material is molasses, and in the second is whey. Th...

Dynamic programming is one of the methods which utilize special structures of large-scale mathematical programming problems. Conventional dynamic programming, however, can hardly solve mathematical programming problems with many constraints. This paper proposes differential dynamic programming algorithms for solving largescale nonlinear programming problems with many constraints and proves thei...

Increasing attention has recently been paid to algorithms based on dynamic programming (DP) due to the suitability of DP for learning problems involving control. In stochastic environments where the system being controlled is only incompletely known, however, a unifying theoretical account of these methods has been missing. In this paper we relate DP-based learning algorithms to the powerful te...

A method for solving binocular and multi-view stereo matching problems is presented in this paper. A weak consistency constraint is proposed, which expresses the visibility constraint in the image space. It can be proved that the weak consistency constraint holds for scenes that can be represented by a set of 3D points. As well, also proposed is a new reliability measure for dynamic programming...

Dynamic programming is a powerful technique for efficiently solving recursive problems, but it’s hardly the end of the story. In many cases, once we have a basic dynamic programming algorithm in place, we can make further improvements to bring down the running time or the space usage. We saw one example in the Fibonacci number algorithm. Buried inside the naïve iterative Fibonacci algorithm is ...