About Me

I am a graduate student in the department of electrical and computer engineering at the K.N. Toosi University of Technology, Tehran, Iran. My supervisor is Prof. Taghirad, and I am a member of Advanced Robotics and Automated Systems (ARAS). I received my BS degree in electrical engineering (control) from Amirkabir University of Technology, Tehran, Iran, where I worked under the supervision of Prof. Menhaj.

My current research focuses on the motion planning under uncertainty and estimation. Broadly speaking, my research interests are in the following areas:

  • Robotics (Motion planning, SLAM, Control)
  • Control Theory (Nonlinear, Optimal, Robust, Stochastic)
  • Machine Learning (Markov Decision Processes, Reinforcement Learning)
  • Computational Intelligence (Neural Networks, Fuzzy Systems, Evolutionary Computation)
Education
• M.Sc. in Electrical Engineering (Control), K. N. Toosi University of Technology

Thesis: “Mobile Robot Path Planning and Exploration with Uncertainty Using Feedback-Based Information Roadmap (FIRM)”
Advisor: Prof. Hamid D. Taghirad

• B.Sc. in Electrical Engineering (Control), Amirkabir University of Technology (Tehran Polytechnic)

Senior Design Project: “Path Planning and Formation Control of Multi-Robots in Leader-Follower Structure Using ARO and implementation on e-Puck robots”
Advisor: Prof. Mohammad Bagher Menhaj

Publications

Journal Papers


Conference Papers


Thesis

In this thesis, the multi-goal motion planning is done for specific goals such as environment exploration, search and coverage. However, the presence of uncertainties makes them challenging tasks. In order to achieve a reliable plan and decision, these uncertainties should be considered in the robot’s planning and decision making. Therefore, the path planning for the exploration and search is modeled as a asymmetric Travelling Salesman Problem (ATSP) in the belief space. Toward reducing the complexity of the aforementioned problem, the Feedback-based Information Roadmap (FIRM) is exploited. Using FIRM, the intractable travelling salesman optimization problem in the continuous belief space is changed to a simpler optimization problem on the TSP-FIRM graph. The optimal policy of the robot is obtained by finding the optimal path between each two goal points and solving the ATSP and then the policy is executed online.

Some algorithms are proposed to overcome the deviation from the path, kidnapping, finding new obstacles and becoming highly uncertain about the position which are possible situations in the online execution of the policy. Consequently, the robot should update its graph, map and policy online. The generic proposed algorithms are extended to the nonholonomic robots. In the online and offline phase, switching and LQG controllers as well as a Kalman filter for localization, are adopted. This algorithm can be implemented in practice and makes us one step closer to the solving Simultaneously Path planning, Localization and Mapping (SPLAM) problem.This algorithm is implemented in the Webots and also on a real robot (Melon robot). In both simulation and real implementation, we have used a vision-based localization based on the EKF.

Papers


Videos


Implementation in real environment

Implementation in Webots

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