Dr. Mahdi Hassan received his Mechanical Engineering degree with first class honours in 2013 from the University of Technology Sydney (UTS). He undertook his Ph.D studies at UTS Centre for Autonomous Systems (CAS) and received his Ph.D in early 2018. Since early 2017, he has been working as a research associate at CAS.
His research is in robotics; and his research interests in robotics include cooperative complete coverage in complex 3D environments; autonomous industrial robots; path, motion and task planning; multi-robot collaboration and coordination; and optimization-based algorithms.
He has been involved in projects such as:
- Autonomous grit-blasting robots for steel structure maintenance (the robots have been deployed in the Sydney Harbour Bridge, and commercialised by SABRE Autonomous Solutions)
- Bio-inspired climbing robots for inspection of complex structures in confined spaces (deployed in the Sydney Harbour Bridge)
- An intelligent robotic system for underwater structure maintenance
Undergraduate: Dynamics and Control;
Postgraduate: Design Optimisation for Manufacturing;
Hassan, M, Liu, D & Xu, D 2019, 'A Two-Stage Approach to Collaborative Fiber Placement through Coordination of Multiple Autonomous Industrial Robots', Journal of Intelligent and Robotic Systems.View/Download from: UTS OPUS or Publisher's site
hassan, M, liu, D & Paul, G 2018, 'Collaboration of Multiple Autonomous Industrial Robots through Optimal Base Placements', Journal of Intelligent and Robotic Systems, vol. 90, no. 1-2, pp. 113-132.View/Download from: UTS OPUS or Publisher's site
Multiple autonomous industrial robots can be of great use in manufacturing applications, particularly if the environment is unstructured and custom manufacturing is required. Autonomous robots that are equipped with manipulators can collaborate to carry out manufacturing tasks such as surface preparation by means of grit-blasting, surface coating or spray painting, all of which require complete surface coverage. However, as part of the collaboration process, appropriate base placements relative to the environment and the target object need to be determined by the robots. The problem of finding appropriate base placements is further complicated when the object under consideration is large and has a complex geometric shape, and thus the robots need to operate from a number of base placements in order to obtain complete coverage of the entire object. To address this problem, an approach for Optimization of Multiple Base Placements (OMBP) for each robot is proposed in this paper. The approach aims to optimize base placements for multi-robot collaboration by taking into account task-specific objectives such as makespan, fair workload division amongst the robots, and coverage percentage; and manipulator-related objectives such as torque and manipulability measure. In addition, the constraint of robots maintaining an appropriate distance between each other and relative to the environment is taken into account. Simulated and real-world experiments are carried out to demonstrate the effectiveness of the approach and to verify that the simulated results are accurate and reliable.
Hassan, M & Liu, D 2017, 'Simultaneous area partitioning and allocation for complete coverage by multiple autonomous industrial robots', Autonomous Robots, vol. 41, no. 8, pp. 1609-1628.View/Download from: UTS OPUS or Publisher's site
© 2017, Springer Science+Business Media New York. For tasks that require complete coverage of surfaces by multiple autonomous industrial robots, it is important that the robots collaborate to appropriately partition and allocate the surface areas amongst themselves such that the robot team's objectives are optimized. An approach to this problem is presented, which takes into account unstructured and complex 3D environments, and robots with different capabilities. The proposed area partitioning and allocation approach utilizes Voronoi partitioning to partition objects' surfaces, and multi-objective optimization to allocate the partitioned areas to the robots whilst optimizing robot team's objectives. In addition to minimizing the overall completion time and achieving complete coverage, which are objectives particularly useful for applications such as surface cleaning, manipulability measure and joint's torque are also optimized so as to help autonomous industrial robots to operate better in applications such as spray painting and grit-blasting. The approach is validated using six case studies that consist of comparative studies, complex simulated scenarios as well as real scenarios using data obtained from real objects and applications.
Hassan, M & Liu, D 2018, 'Performance Evaluation of an Evolutionary Multiobjective Optimization Based Area Partitioning and Allocation Approach', IEEE/ASME International Conference on Advanced Intelligent Mechatronics, IEEE, Auckland, New Zealand, pp. 527-532.View/Download from: UTS OPUS or Publisher's site
An Area Partitioning and Allocation (APA) approach was presented in . The approach focused on optimizing the coverage performance of Autonomous Industrial Robots (AIRs) using multiple conflicting objectives and Voronoi partitioning. However, questions related to the optimality, convergence, and consistency of the Pareto solutions were not studied in details. In this paper, Inverted Generational Distance (IGD) metric is used to verify the convergence of the Pareto front towards Pareto optimal front (PF*). The consistency in obtaining similar Pareto fronts for independent optimization runs is studied. The computational complexity of the approach with respect to the size of the coverage area and the number of AIRs is also discussed. Two application scenarios are used in this research.
Hassan, M & Liu, D 2018, 'A Deformable Spiral Based Algorithm to Smooth Coverage Path Planning for Marine Growth Removal', International Conference on Intelligent Robots and Systems, IEEE, Madrid, Spain, pp. 1913-1918.View/Download from: UTS OPUS or Publisher's site
Marine growths that flourish on the surfaces of underwater structures, such as bridge pylons, make the inspection and maintenance of these structures challenging. A robotic solution, using an Intervention Autonomous Underwater Vehicle (I-AUV), is developed for removing marine growth. This paper presents a Deformable Spiral Coverage Path Planning (DSCPP) algorithm for marine growth removal. DSCPP generates smooth paths to prevent damage to the surfaces of the structures and to avoid frequent or aggressive decelerations and accelerations due to sharp turns. DSCPP generates a spiral path within a circle and analytically maps the path to a minimum bounding rectangle which encompasses an area of a surface with marine growth. It aims to achieve a spiral path with minimal length while preventing missed areas of coverage. Several case studies are presented to validate the algorithm. Comparison results show that DSCPP outperforms the popular boustrophedon-based coverage approach when considering the requirements for the application under consideration.
Hassan, M, Liu, DL & Paul, GP 2016, 'Modeling and Stochastic Optimization of Complete Coverage under Uncertainties in Multi-Robot Base Placements', Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE (Institute of Electrical and Electronics Engineers), Daejeon, Korea, pp. 2978-2984.View/Download from: UTS OPUS or Publisher's site
Uncertainties in base placements of mobile, autonomous industrial robots can cause incomplete coverage in tasks such as grit-blasting and spray painting. Sensing and localization errors can cause such uncertainties in robot base placements. This paper addresses the problem of collaborative complete coverage under uncertainties through appropriate base placements of multiple mobile and autonomous industrial robots while aiming to optimize the performance of the robot team. A mathematical model for complete coverage under uncertainties is proposed and then solved using a stochastic multi-objective optimization algorithm. The approach aims to concurrently find an optimal number and sequence of base placements for each robot such that the robot team's objectives are optimized whilst uncertainties are accounted for. Several case studies based on a real-world application using a real-world object and a complex simulated object are provided to demonstrate the effectiveness of the approach for different conditions and scenarios, e.g. various levels of uncertainties, different numbers of robots, and robots with different capabilities.
Gürel, C., Hassan, M., Zadeh, G. & Erden, A. 2015, 'Rose stem branch point detection and cutting point location for rose harvesting robot', 21st Mechatronics and Machine Vision in Practice, M2VIP 2015.
© 2015, Mechatronics and Machine Vision in Practice. All rights reserved. The primary objective of the study is to develop a method that can locate the proper location of cutting point of rose stem for robotic rose harvesting. Stem tracking and cutting operation are achieved using with the help of stereo vision techniques. The cutting point has a vital importance for cut rose harvesting in greenhouse which affects the efficiency of new shoot from the cut stem. In agricultural process, the cutting point depends on thickness of stem and eye on stem. Locating the eye is difficult by image processing thus another relation for cut point estimation was performed. The relation between thickness and cut length can lead to relation for cut point estimation. 239 data is collected from a greenhouse and linear relation with some deviation was acquired. To perform this relation, branch point location algorithm was developed. The proposed algorithm has three steps: branch point detection; thickness calculation; and cutting length estimation. The algorithm for branch detection performs quite well to detect and locate the position of the point when there occur no overlapping. The results of branch point detection are adequate for implementing to the robot.
Hassan, M, Liu, D, Paul, G & Huang, S 2015, 'An Approach to Base Placement for Effective Collaboration of Multiple Autonomous Industrial Robots', Proceedings - IEEE International Conference on Robotics and Automation, IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers (IEEE), Washington State Convention Center in Seattle, Washington, USA, pp. 3286-3291.View/Download from: UTS OPUS or Publisher's site
There are many benefits for the deployment of multiple autonomous industrial robots to carry out a task, particularly if the robots act in a highly collaborative manner. Collaboration can be possible when each robot is able to autonomously explore the environment, localize itself, create a map of the environment and communicate with other robots. This paper presents an approach to the modeling of the collaboration problem of multiple robots determining optimal base positions and orientations in an environment by considering the team objectives and the information shared amongst the robots. It is assumed that the robots can communicate so as to share information on the environment, their operation status and their capabilities. The approach has been applied to a team of robots that are required to perform complete surface coverage tasks such as grit-blasting and spray painting in unstructured environments. Case studies of such applications are presented to demonstrate the effectiveness of the approach.
Hassan, M, Liu, D, Huang, S & Dissanayake, G 2014, 'Task Oriented Area Partitioning and Allocation for Optimal Operation of Multiple Industrial Robots in Unstructured Environments', 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, International Conference on Control, Automation, Robotics and Vision, IEEE, Marina Bay Sands, Singapore, pp. 1184-1189.View/Download from: UTS OPUS or Publisher's site
When multiple industrial robots are deployed in field applications such as grit blasting and spray painting of steel bridges, the environments are unstructured for robot operation and the robot positions may not be arranged accurately. Coordination of these multiple robots to maximize productivity through area partitioning and allocation is crucial. This paper presents a novel approach to area partitioning and allocation by utilizing multiobjective optimization and voronoi partitioning. Multiobjective optimization is used to minimize: (1) completion time, (2) proximity of the allocated area to the robot, and (3) the torque experienced by each joint of the robot during task execution. Seed points of the voronoi graph for voronoi partitioning are designed to be the design variables of the multiobjective optimization algorithm. Results of three different simulation scenarios are presented to demonstrate the effectiveness of the proposed approach and the advantage of incorporating robots' torque capacity.