• 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

Journal Papers

• A. Noormohammadi Asl, H.D. Taghirad “Multi-Goal Motion Planning Using Traveling Salesman Problem in Belief Space”; Information Sciences,  Elsevier Science
• A. Noormohammadi Asl, M.B. Menhaj and A. sajedin; “Control of Leader-Follower Formation and Path planning of Mobile Robots Using Asexual Reproduction Optimization(ARO)”; Applied Soft Computing, Elsevier Science
• A. Noormohammadi Asl, O. Esrafilian, H.D. Taghirad, M. Ahangar “System Identification and H∞-based Control of Quadrotor” [Under Review]
• A. Noormohammadi Asl, Jalal Najafi and M.B. Menhaj “Many Asexual Reproduction Algorithm With Guaranteed Parallelism for Optimization” [To be submitted]

Conference Papers

• A. Noormohammadi Asl, H. D. Taghirad, A. Tamjidi “Implementation of Multi-Goal Motion Planning Under Uncertainty on a Mobile Robot”; International Conference on Robotics and Mechatronics (ICROM 2017)
• A. Noormohammadi Asl, M.Saffari, M. Teshnehlab “Neural Control of Mobile Robot Motion Based on Feedback Error Learning and Mimetic Structure”; The 26th Iranian Conference on Electrical Engineering (ICEE-2018)
• A. Noormohammadi Asl, M.B. Menhaj and A. sajedin “Leader-Follower Formation and Path Planning of Nonholonomic Mobile Robots in Presence of Obstacle”, Iranian Fuzzy Systems Conference(IFSC), in Persian

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

• A. Noormohammadi Asl, H.D. Taghirad “Multi-Goal Motion Planning Using Traveling Salesman Problem in Belief Space”; IEEE Transactions on Automation Science and Engineering [Under review]
• A. Noormohammadi Asl, H. D. Taghirad, A. Tamjidi “Implementation of Multi-Goal Motion Planning Under Uncertainty on a Mobile Robot”; International Conference on Robotics and Mechatronics (ICROM 2017) [Accepted]

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