Sahar Trigui

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.

smartPATH: An Efficient Hybrid ACO-GA Algorithm for Solving the Global Path Planning Problem of Mobile Robots

Path planning is a fundamental optimization problem that is crucial for the navigation of a mobile robot. Among the vast array of optimization approaches, we focus in this paper on Ant Colony Optimization (ACO) and Genetic Algorithms (GA) for solving the global path planning problem in a static environment, considering their effectiveness in solving such a problem. Our objective is to design an efficient hybrid algorithm that takes profit of the advantages of both ACO and GA approaches for the sake of maximizing the chance to find the optimal path even under real-time constraints. In this paper, we present smartPATH, a new hybrid ACO-GA algorithm that relies on the combination of an improved ACO algorithm (IACO) for efficient and fast path selection, and a modified crossover operator to reduce the risk of falling into a local minimum. We demonstrate through extensive simulations that smartPATH outperforms classical ACO (CACO), GA algorithms. It also outperforms the Dijkstra exact method in solving the path planning problem for large graph environments. It improves the solution quality up to 57% in comparison with CACO and reduces the execution time up to 83% as compared to Dijkstra for large and dense graphs. In addition, the experimental results on a real robot shows that smartPATH finds the optimal path with a probability up to 80% with a small gap not exceeding 1m in 98%.

On the Adequacy of Tabu Search for Global Robot Path Planning Problem in Grid Environments

Abstract This paper investigates the capabilities of tabu search for solving the global path planning problem in grid maps. Accordingly, a tabu search system model is designed and a tabu search planner algorithm for solving the path planning problem is proposed. A comprehensive simulation study is conducted using the proposed model and algorithm, in terms of solution quality and execution time. A comparison between our results with those of A* and genetic algorithms (GA) is presented for small, medium and large-scale grid maps. Simulation results show that the tabu search planner is able to find the optimal solution for small scale environments. However, for large scale maps, it provides near-optimal solutions with small gap while ensuring shorter execution times as compared to the A* Algorithm. A discussion about the advantages and limitations of TS for solving a path planning problem is also presented.

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.

A Clustering Market-Based Approach for Multi-robot Emergency Response Applications

In 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 multiple-depot 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.

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.