Browsing by Author "Youssef, Habib"
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- An Analytical Hierarchy Process-Based Approach to Solve the Multi-Objective Multiple Traveling Salesman ProblemPublication . Trigui, Sahar; Cheikhrouhou, Omar; Koubâa, Anis; Zarrad, Anis; Youssef, HabibWe consider the problem of assigning a team of autonomous robots to target locations in the context of a disaster management scenario while optimizing several objectives. This problem can be cast as a multiple traveling salesman problem, where several robots must visit designated locations. This paper provides an analytical hierarchy process (AHP)-based approach to this problem, while minimizing three objectives: the total traveled distance, the maximum tour, and the deviation rate. The AHP-based approach involves three phases. In the first phase, we use the AHP process to define a specific weight for each objective. The second phase consists in allocating the available targets, wherein we define and use three approaches: market-based, robot and task mean allocation-based, and balanced-based. Finally, the third phase involves the improvement in the solutions generated in the second phase. To validate the efficiency of the AHP-based approach, we used MATLAB to conduct an extensive comparative simulation study with other algorithms reported in the literature. The performance comparison of the three approaches shows a gap between the market-based approach and the other two approaches of up to 30%. Further, the results show that the AHP-based approach provides a better balance between the objectives, as compared to other state-of-the-art approaches. In particular, we observed an improvement in the total traveled distance when using the AHP-based approach in comparison with the distance traveled when using a clustering-based approach.
- A clustering market-based approach for multi-robot emergency response applicationsPublication . Trigui, Sahar; Koubâa, Anis; Cheikhrouhou, Omar; Qureshi, Basit; Youssef, HabibIn this paper, we address the problem of multi-robot systems in emergency response applications, where a team of robots/drones has to visit affected locations to provide rescue services. In the literature, the most common approach is to assign target locations individually to robots using centralized or distributed techniques. The problem is that the computation complexity increases significantly with the number of robots and target locations. In addition, target locations may not be assigned uniformly among the robots. In this paper, we propose, CMMTSP, a clustering market-based approach that first groups locations into clusters, then assigns clusters to robots using a market-based approach. We formulate the problem as multipledepot MTSP and address the multi-objective optimization of three objectives namely, the total traveled distance, the maximum traveled distance and the mission time. Simulations show that CM-MTSP provides a better balance among the three objectives as compared to a single objective optimization, in particular an enhancement of the mission time, and reduces the execution time to at least 80% as compared to a greedy approach.
- Cyber-physical systems clouds: A surveyPublication . Rihab, Chaari; Ellouze, Fatma; Koubâa, Anis; Qureshi, Basit; Pereira, Nuno; Youssef, Habib; Tovar, EduardoCyber-Physical Systems (CPSs) represent systems where computations are tightly coupled with the physical world, meaning that physical data is the core component that drives computation. Industrial automation systems, wireless sensor networks, mobile robots and vehicular networks are just a sample of cyber-physical systems. Typically, CPSs have limited computation and storage capabilities due to their tiny size and being embedded into larger systems. With the emergence of cloud computing and the Internet-of-Things (IoT), there are several new opportunities for these CPSs to extend their capabilities by taking advantage of the cloud resources in different ways. In this survey paper, we present an overview of research efforts on the integration of cyber-physical systems with cloud computing and categorize them into three areas: (1) remote brain, (2) big data manipulation, (3) and virtualization. In particular, we focus on three major CPSs namely mobile robots, wireless sensor networks and vehicular networks.
- Demo abstract: RadiaLE: a framework for benchmarking link quality estimatorsPublication . Baccour, Nouha; Jamâa, Maissa Ben; Rosário, Denis do; Koubâa, Anis; Alves, Mário; Becker, Leandro B.; Youssef, Habib; Fotouhi, HosseinLink quality estimation is a fundamental building block for the design of several different mechanisms and protocols in wireless sensor networks (WSN). A thorough experimental evaluation of link quality estimators (LQEs) is thus mandatory. Several WSN experimental testbeds have been designed ([1–4]) but only [3] and [2] targeted link quality measurements. However, these were exploited for analyzing low-power links characteristics rather than the performance of LQEs. Despite its importance, the experimental performance evaluation of LQEs remains an open problem, mainly due to the difficulty to provide a quantitative evaluation of their accuracy. This motivated us to build a benchmarking testbed for LQE - RadiaLE, which we present here as a demo. It includes (i.) hardware components that represent the WSN under test and (ii.) a software tool for the set up and control of the experiments and also for analyzing the collected data, allowing for LQEs evaluation.
- Design and performance analysis of global path planning techniques for autonomous mobile robots in grid environmentsPublication . Chaari, Imen; Koubâa, Anis; Bennaceur, Hachemi; Ammar, Adel; Alajlan, Maram; Youssef, HabibThis article presents the results of the 2-year iroboapp research project that aims at devising path planning algorithms for large grid maps with much faster execution times while tolerating very small slacks with respect to the optimal path. We investigated both exact and heuristic methods. We contributed with the design, analysis, evaluation, implementation and experimentation of several algorithms for grid map path planning for both exact and heuristic methods. We also designed an innovative algorithm called relaxed A-star that has linear complexity with relaxed constraints, which provides near-optimal solutions with an extremely reduced execution time as compared to A-star. We evaluated the performance of the different algorithms and concluded that relaxed A-star is the best path planner as it provides a good trade-off among all the metrics, but we noticed that heuristic methods have good features that can be exploited to improve the solution of the relaxed exact method. This led us to design new hybrid algorithms that combine our relaxed A-star with heuristic methods which improve the solution quality of relaxed A-star at the cost of slightly higher execution time, while remaining much faster than A* for large-scale problems. Finally, we demonstrate how to integrate the relaxed A-star algorithm in the robot operating system as a global path planner and show that it outperforms its default path planner with an execution time 38% faster on average.
- F-LQE: a Fuzzy Link Quality Estimator for wireless sensor networksPublication . Baccour, Nouha; Koubâa, Anis; Youssef, Habib; Jamâa, Maissa Ben; Rosário, Denis do; Alves, Mário; Becker, Leandro B.Radio Link Quality Estimation (LQE) is a fundamental building block for Wireless Sensor Networks, namely for a reliable deployment, resource management and routing. Existing LQEs (e.g. PRR, ETX, Fourbit, and LQI ) are based on a single link property, thus leading to inaccurate estimation. In this paper, we propose F-LQE, that estimates link quality on the basis of four link quality properties: packet delivery, asymmetry, stability, and channel quality. Each of these properties is defined in linguistic terms, the natural language of Fuzzy Logic. The overall quality of the link is specified as a fuzzy rule whose evaluation returns the membership of the link in the fuzzy subset of good links. Values of the membership function are smoothed using EWMA filter to improve stability. An extensive experimental analysis shows that F-LQE outperforms existing estimators.
- FL-MTSP: a fuzzy logic approach to solve the multi-objective multiple traveling salesman problem for multi-robot systemsPublication . Trigui, Sahar; Cheikhrouhou, Omar; Koubâa, Anis; Baroudi, Uthman; Youssef, HabibThis paper considers the problem of assigning target locations to be visited by mobile robots. We formulate the problem as a multiple-depot multiple traveling salesman problem (MD-MTSP), an NP-Hard problem instance of the MTSP. In contrast to most previous works, we seek to optimize multiple performance criteria, namely the maximum traveled distance and the total traveled distance, simultaneously. To address this problem, we propose, FL-MTSP, a new fuzzy logic approach that combines both metrics into a single fuzzy metric, reducing the problem to a single-objective optimization problem. Extensive simulations show that the proposed fuzzy logic approach outperforms an existing centralized Genetic Algorithm (MDMTSP_GA) in terms of providing a good trade-off of the two performance metrics of interest. In addition, the execution time of FL-MTSP was shown to be always faster than that of the MDMTSP_GA approach, with a ratio of 89 %.
- FL-MTSP: a fuzzy logic approach to solve the multi-objective multiple traveling salesman problem for multi-robot systemsPublication . Trigui, Sahar; Cheikhrouhou, Omar; Koubâa, Anis; Baroudi, Uthman; Youssef, HabibThis paper considers the problem of assigning target locations to be visited by mobile robots. We formulate the problem as a multiple-depot multiple traveling salesman problem (MD-MTSP), an NP-Hard problem instance of the MTSP. In contrast to most previous works, we seek to optimize multiple performance criteria, namely the maximum traveled distance and the total traveled distance, simultaneously. To address this problem, we propose, FL-MTSP, a new fuzzy logic approach that combines both metrics into a single fuzzy metric, reducing the problem to a single-objective optimization problem. Extensive simulations show that the proposed fuzzy logic approach outperforms an existing centralized Genetic Algorithm (MDMTSP_GA) in terms of providing a good trade-off of the two performance metrics of interest. In addition, the execution time of FL-MTSP was shown to be always faster than that of the MDMTSP_GA approach, with a ratio of 89 %.
- Global robot Path Planning using GA for Large Grid Maps: Modelling, Performance and ExperimentationPublication . Alajlan, Maram; Chaari, Imen; Koubâa, Anis; Bennaceur, Hachemi; Ammar, Adel; Youssef, HabibIn this paper, the efficiency of genetic algorithm (GA) approach to address the problem of global path planning for mobile robots in large-scale grid environments is revisited and assessed. First, an efficient GA path planner to find an (or near) optimal path in a grid map is proposed. In particular, large maps instances are considered in this work, as small maps are easy to address by typical linear-time exact algorithms, in contrast to large maps, which require more intensive computations. The operators of the GA planner were carefully designed for optimizing the search process. Also, extensive simulations to evaluate the GA planner are conducted, and its performance is compared to that of the A algorithm considered as benchmarking reference. We found out that the GA planner can find optimal solutions in the same way as A in large grid maps in most cases, but A is faster than the GA. This is because GA is not a constructive path planner and heavily relies on initial population to explore the space of solutions in contrast to A that builds its solution progressively towards the target. It was concluded that, although GA can provide an alternative to A technique, it is likely that they are not efficient enough to beat exact methods such as A when used with a consistent heuristic. The GA planner is integrated in the global path planning modules of the Robot Operating System (ROS), its feasibility is demonstrated, and its performance is compared against the default ROS planner.
- On the Robot Path Planning using Cloud Computing for Large Grid MapsPublication . Chaari, Imen; Koubâa, Anis; Qureshi, Basit; Youssef, Habib; Severino, Ricardo; Tovar, EduardoGlobal path planning consists in finding the optimal path for a mobile robot with the lowest cost in the minimum amount of time, without colliding with the obstacles scattered in the workspace. In this paper, we investigate the benefits of offloading path planning algorithms to be executed in the cloud rather than in the robot. The contribution consists in developing a vertex-centric implementation of RA∗ [1], a version of A∗ that we developed for grid maps and that was proven to be much faster than A∗, using the distributed graph processing framework Giraph that rely on Hadoop. We also developed a centralized cloud-based C++ implementation of the algorithm for benchmarking and comparison purposes. Experimental results on a real cloud shows that the distributed graph processing Giraph fails to provide faster execution as compared to centralized C++ implementation for different map sizes and configuration due to non-real time properties of Hadoop.