Imen Chaari

smartPATH: A hybrid ACO-GA algorithm for robot path planning

Path planning is a critical combinatorial problem essential for the navigation of a mobile robot. Several research initiatives, aiming at providing optimized solutions to this problem, have emerged. Ant Colony Optimization (ACO) and Genetic Algorithms (GA) are the two most widely used heuristics that have shown their effectiveness in solving such a problem. This paper presents, smartPATH, a new hybrid ACO-GA algorithm to solve the global robot path planning problem. The algorithm consists of a combination of an improved ACO algorithm (IACO) for efficient and fast path selection, and a modified crossover operator for avoiding falling into a local minimum. Our system model incorporates a Wireless Sensor Network (WSN) infrastructure to support the robot navigation, where sensor nodes are used as signposts that help locating the mobile robot, and guide it towards the target location. We found out smartPATH outperforms classical ACO (CACO) and GA algorithms (as defined in the literature without modification) for solving the path planning problem both and Bellman-Ford shortest path method. We demonstrate also that smartPATH reduces the execution time up to 64.9% in comparison with Bellman-Ford exact method and improves the solution quality up to 48.3% in comparison with CACO.

Global path planning for mobile robots in large-scale grid environments using genetic algorithms

Global path planning is considered as a fundamental problem for mobile robots. In this paper, we investigate the capabilities of genetic algorithms (GA) for solving the global path planning problem in large-scale grid maps. First, we propose a GA approach for efficiently finding an (or near) optimal path in the grid map. We carefully designed GA operators to optimize the search process. We also conduct a comprehensive statistical evaluation of the proposed GA approach in terms of solution quality, and we compare it against the well-known A* algorithm as a reference. Extensive simulation results show that GA is able to find the optimal paths in large environments equally to A* in almost all the simulated cases.

Design and performance analysis of global path planning techniques for autonomous mobile robots in grid environments

This 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.

Coordination in a multi-robot surveillance application using Wireless Sensor Networks

Cooperative robots and their integration with Wireless Sensor Networks (WSNs) is an expanding area that still deserves significant research efforts. This paper presents a multi-robot surveillance application supported by a WSN. We investigate the problem of multi-robot coordination for target tracking and capturing. One key distinction of our problem model is to consider a WSN that supports the mission of the multi-robot team. We devised three mechanisms: centralized, distributed and market-based algorithms, which were extensively evaluated under the Player/Stage simulator. Simulation results show that the centralized approach is the most likely solution able to maintain an efficient system cost.

Robot Path Planning and Cooperation - Foundations, Algorithms and Experimentations

This book presents extensive research on two main problems in robotics: the path planning problem and the multirobot 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. Studies in Computational Intelligence

Different Approaches to Solve the MRTA Problem

The multi-robot task allocation problem is a fundamental problem in robotics research area. The problem roughly consists of finding an optimal allocation of tasks among several robots to reduce the mission cost to a minimum. As mentioned in Chap. 6, extensive research has been conducted in the area for answering the following question: Which robot should execute which task? In this chapter, we design different solutions to solve the MRTA problem. We propose four different approaches: an improved distributed market-based approach (IDMB), a clustering market-based approach (CM-MTSP), a fuzzy logic-based approach (FL-MTSP), and Move-and-Improve approach. These approaches must define how tasks are assigned to the robots. The IDBM, CM-MTSP, and Move-and-Improve approaches are based on the use of an auction process where bids are used to evaluate the assignment. The FL-MTSP is based on the use of the fuzzy logic algebra to combine objectives to be optimized.

Performance Analysis of the MRTA Approaches for Autonomous Mobile Robot

The multi-robot task allocation is a fundamental problem in robotics research area. Indeed, robots are typically intended to collaborate together to achieve a given goal. This chapter studies the performance of the IDBM, CM-MTSP, FL-MTSP, and Move-and-Improve approaches. In order to highlight the performance of the proposed schemes, we compared each one to appropriate existing ones. IDMB was compared with the RTMA [1], CM-MTSP was compared with single-objective and greedy algorithms, and FL-MTSP was compared with a centralized approach based on genetic algorithm and with NSGA-II algorithm. To validate the efficiency of the Move-and-Improve distributed algorithm, we first conducted extensive simulations and evaluated its performance in terms of the total traveled distance and the ratio of overlaped targets under different settings. The simulation results show that IDMB and Move-and-Improve algorithms produce near-optimal solutions. Also, CM-MTSP and FL-MTSP provide a good trade-off between conflicting objectives.

Introduction to Mobile Robot Path Planning

Robotic is now gaining a lot of space in our daily life and in several areas in modern industry automation and cyber-physical applications. This requires embedding intelligence into these robots for ensuring (near)-optimal solutions to task execution. Thus, a lot of research problems that pertain to robotic applications have arisen such as planning (path, motion, and mission), task allocation problems, navigation, tracking. In this chapter, we focused on the path planning research problem.

General Background on Multi-robot Task Allocation

Multi-robot systems (MRSss) face several challenges, but the most typical problem is the multi-robot tasks allocation (MRTA). It consists in finding the efficient allocation mechanism in order to assign different tasks to the set of available robots. Toward this objective, robots will work as cooperative agents. MRTA aims at ensuring an efficient execution of tasks under consideration and thus minimizing the overall system cost. Various research works have solved the MRTA problem using the multiple traveling salesman problem (MTSP) formulation. In this context, an overview on MRTA and MTSP is given in this chapter. Furthermore, a summary of the related works is presented.

Background on Artificial Intelligence Algorithms for Global Path Planning

In the literature, numerous path planning algorithms have been proposed. Although the objective of these algorithms is to find the shortest path between two positions A and B in a particular environment, there are several algorithms based on a diversity of approaches to find a solution to this problem. The complexity of algorithms depends on the underlying techniques and on other external parameters, including the accuracy of the map and the number of obstacles. It is impossible to enumerate all these approaches in this chapter, but we will shed the light on the most used approaches in the literature.