Mohammadhussein Rafieisakhaei

Feedback motion planning under non-Gaussian uncertainty and non-convex state constraints

Planning under process and measurement uncertainties is a challenging problem. In its most general form, it can be modeled as a Partially Observed Markov Decision Process (POMDP) problem. However POMDPs are generally difficult to solve when the underlying spaces are continuous, particularly when beliefs are non-Gaussian, and the difficulty is further exacerbated when there are also non-convex constraints on states. Existing algorithms to address such challenging POMDPs are expensive in terms of computation and memory. In this paper, we provide a feedback policy in non-Gaussian belief space by solving a convex program for common non-linear observation models. The solution involves a Receding Horizon Control strategy using particle filters for the non-Gaussian belief representation. We develop a way of capturing non-convex constraints in the state space and adapt the optimization to incorporate such constraints, as well. A key advantage of this method is that it does not introduce additional variables in the optimization problem and is therefore more scalable than existing constrained problems in belief space. We demonstrate the performance of the method on different scenarios.

A system dynamics model on the reasons of car price shocks after economic sanctions

International Community can decide to establish several types of sanctions on the economy of a country. No matter the reasons of these sanctions, generally, they can have many adverse effects on the economy of the sanctioned country. This depends on the variety and extent of the sanctions. We propose a system dynamics model to capture some of the main reasons behind the economic instability in a sanctioned country. We consider a case study of the car price shocks occurred in Iran after the various economic sanctions that was established on 2012. Irans economy depends heavily on the oil production and in fact, their main exports consist of the crude oil products. In our model, we combine causal loops for prediction of oil price after sanctions with those on predicting the car price. Our simulation results are consistent with the real data of the most common cars price in Iran.

T-LQG: Closed-loop belief space planning via trajectory-optimized LQG

Planning under motion and observation uncertainties requires the solution of a stochastic control problem in the space of feedback policies. In this paper, by restricting the policy class to the linear feedback polices, we reduce the general (n2 + n)-dimensional belief space planning problem to an (n)-dimensional problem. As opposed to the previous literature that search in the space of open-loop optimal control policies, we obtain this reduction in the space of closed-loop policies by obtaining a Linear Quadratic Gaussian (LQG) design with the best nominal performance. Then, by taking the entire underlying trajectory of the LQG controller as the decision variable, we pose a coupled design of the trajectory and estimator (while keeping the design of the controller separate) as a NonLinear Program (NLP) that can be solved by a general NLP solver. We prove that under a first-order approximation and a careful usage of the separation principle, our approximations are valid. We provide an analysis on the existing major belief space planning methods and show that our algorithm keeps the lowest computational burden while searching in the policy space. Finally, we extend our solution to contain general state and control constraints. Our simulation results support our design.

MT-LQG: Multi-agent planning in belief space via trajectory-optimized LQG

Belief space planning is concerned with the problem of finding the control policy under process and measurement uncertainties. Formulated as a stochastic control problem, the solution of a general Decentralized Partially Observed Markov Decision Process (Dec-POMDP) is a collection of feedback policies for individual agents, maximizing a joint value function. In this paper, we design (m) number of Linear Quadratic Gaussian (LQG) policies for (m) number of agents maximizing the joint performance of the team. Casting the problem as a NonLinear Program (NLP), we propose a framework that reduces the optimization dimension from ((mn)2 + mn) to (mn) with (n) referring to the dimension of each individual agents state space. As a result, the proposed method reduces the formidable generic Dec-POMDP to a computationally tractable multi-agent planning under uncertainty. Our results in 2D and 3D environments demonstrate the performance of the algorithm and its ability to predict and avoid inter-agent collisions.

A simulation-based model of technology localization in developing countries

Technology localization is a popular policy in developing countries to reduce the dependency on foreign partners of the industrial manufactures. In this paper, we present a model to analyze the determinant parameters of technology localization process. The model is based on the system dynamics methodology. Our results show that the technology localization becomes stable after a period of time.