Browsing by Author "Bennaceur, Hachemi"
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- 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.
- 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.
- Move and Improve: a Market-Based Mechanism for the Multiple Depot Multiple Travelling Salesmen ProblemPublication . Koubâa, Anis; Cheikhrouhou, Omar; Bennaceur, Hachemi; Sriti, Mohamed-Foued; Javed, Yasir; Ammar, AdelConsider the problem of having a team of cooperative and autonomous robots to repeatedly visit a set of target locations and return back to their initial locations. This problem is known as multi-robot patrolling and can be cast to the multiple depot multiple traveling salesman problem (MD-MTSP), which applies to several mobile robots applications. As an NP-Hard problem, centralized approaches using meta-heuristic search are typically used to solve it, but such approaches are computation-intensive and cannot effectively deal with the dynamic nature of the system. This paper provides a distributed solution based on a market-based approach, called Move-and-Improve. It involves the cooperation of the robots to incrementally allocate targets and remove possible overlap. The concept is simple: in each step, a robot moves and attempts to improve its solution while communicating with its neighbors. Our approach consists of four main phases: (1) initial target allocation, (2) tour construction, (3) negotiation of conflicting targets, (4) solution improvement. To validate the efficiency of the Move-and-Improve distributed algorithm, we first conducted extensive simulations using Webots and evaluated its performance in terms of total traveled distance, maximum tour length, and ratio of overlapped targets, under different settings. We also demonstrated through MATLAB simulations the benefits of using our decentralized approach as compared to a centralized Genetic Algorithm approach to solve the MD-MTSP problem. Finally, we implemented Move-and-Improve using ROS and deployed it on real robots.
- Move and Improve: a Market-Based Mechanism for the Multiple Depot Multiple Travelling Salesmen ProblemPublication . Koubâa, Anis; Cheikhrouhou, Omar; Bennaceur, Hachemi; Sriti, Mohamed-Foued; Javed, Yasir; Ammar, AdelConsider the problem of having a team of cooperative and autonomous robots to repeatedly visit a set of target locations and return back to their initial locations. This problem is known as multi-robot patrolling and can be cast to the multiple depot multiple traveling salesman problem (MD-MTSP), which applies to several mobile robots applications. As an NP-Hard problem, centralized approaches using meta-heuristic search are typically used to solve it, but such approaches are computation-intensive and cannot effectively deal with the dynamic nature of the system. This paper provides a distributed solution based on a market-based approach, called Move-and-Improve. It involves the cooperation of the robots to incrementally allocate targets and remove possible overlap. The concept is simple: in each step, a robot moves and attempts to improve its solution while communicating with its neighbors. Our approach consists of four main phases: (1) initial target allocation, (2) tour construction, (3) negotiation of conflicting targets, (4) solution improvement. To validate the efficiency of the Move-and-Improve distributed algorithm, we first conducted extensive simulations using Webots and evaluated its performance in terms of total traveled distance, maximum tour length, and ratio of overlapped targets, under different settings. We also demonstrated through MATLAB simulations the benefits of using our decentralized approach as compared to a centralized Genetic Algorithm approach to solve the MD-MTSP problem. Finally, we implemented Move-and-Improve using ROS and deployed it on real robots.
- Relaxed Dijkstra and A* with linear complexity for robot path planning problems in large-scale grid environmentsPublication . Ammar, Adel; Bennaceur, Hachemi; Châari, Imen; Koubâa, Anis; Alajlan, MaramAlthough there exist efficient methods to determine an optimal path in a graph, such as Dijkstra and A* algorithms, large instances of the path planning problem need more adequate and efficient techniques to obtain solutions in reasonable time. We propose two new time-linear relaxed versions of Dijkstra (RD) and A* (RA*) algorithms to solve the global path planning problem in large grid environments. The core idea consists in exploiting the grid-map structure to establish an accurate approximation of the optimal path, without visiting any cell more than once. We conducted extensive simulations (1290 runs on 43 maps of various types) for the proposed algorithms, both in four-neighbor and eight-neighbor grid environments, and compared them against original Dijkstra and A* algorithms with different heuristics. We demonstrate that our relaxed versions exhibit a substantial gain in terms of computational time (more than 3 times faster in average), and in most of tested problems an optimal solution (in at least 97 % of cases for RD and 82 % for RA*) or a very close one is reached (at most 9 % of extra length, and less than 2 % in average). Besides, the simulations also show that RA* provides a better trade-off between solution quality and execution time than previous bounded relaxations of A* that exist in the literature.
- Robot Path Planning and CooperationPublication . Koubaa, Anis; Bennaceur, Hachemi; Chaari, Imen; Trigui, Sahar; Ammar, Adel; Sriti, Mohamed-Foued; Alajlan, Maram; Cheikhrouhou, Omar; Javed, YasirThis book presents extensive research on two main problems in robotics: the path planning problem and the multi-robot task allocation problem. It is the first book to provide a comprehensive solution for using these techniques in large-scale environments containing randomly scattered obstacles. The research conducted resulted in tangible results both in theory and in practice. For path planning, new algorithms for large-scale problems are devised and implemented and integrated into the Robot Operating System (ROS). The book also discusses the parallelism advantage of cloud computing techniques to solve the path planning problem, and, for multi-robot task allocation, it addresses the task assignment problem and the multiple traveling salesman problem for mobile robots applications. In addition, four new algorithms have been devised to investigate the cooperation issues with extensive simulations and comparative performance evaluation. The algorithms are implemented and simulated in MATLAB and Webots.