Adel Ammar

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.

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.

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.

COROS: A Multi-Agent Software Architecture for Cooperative and Autonomous Service Robots

Building distributed applications for cooperative service robots systems is a very challenging task from software engineering perspective. Indeed, apart from the complexity of designing software components for the control of a single autonomous robot, cooperative multi-robot systems require additional care in the design of software components to ensure communication and coordination between the robotic agents. This chapter proposes COROS, a new multi-agent software architecture for cooperative and autonomous service robots with the objective to make easier the design and development of multi-robot applications. We present a high-level conceptual architecture for multi-agent robotics systems that represents a generic framework for cooperative multi-robot applications. Furthermore, we present an instantiation of this generic architecture with an implementation software architecture on top of the Robot Operating System (ROS) middleware. The proposed concrete software architecture follows a component-based approach to ensure modularity, software reuse, extensibility and scalability of the multi-robot operational software. In addition, one major added value of our architecture is that it provides a tangible solution to supporting multi-robot software development for the ROS middleware, as ROS was originally designed for single-robot applications. We also demonstrate a sample of real-world case studies of cooperative and autonomous service robots applications in an office-like environment, including discovery and courier delivery applications.

A Real Time Adaptive Intrusion Detection Alert Classifier for High Speed Networks

With the emergence of High Speed Network (HSN), the manual intrusion alert detection become an extremely laborious and time-consuming task since it requires an experienced skilled staff in security fields and need a deep analysis. In addition, the batch model of alert management is no longer adequate given that labeling is a continuous time process since incoming intrusion alerts are often collected continuously in time. Furthermore, the static model is no longer appropriate due to the fluctuation nature of the number of alerts incurred by Internet traffic fluctuation nature. This paper proposes an efficient real time adaptive intrusion detection alert classifier dedicated for high speed network. Our classifier is based an online self-trained SVM algorithm with several learning strategies and execution modes. We evaluate our classifier against three different data-sets and the performance study shows an excellent results in term of accuracy and efficiency. The predictive local learning strategy presents a good tradeoff between accuracy and time processing. In addition, it does not involve a human intervention which make it an excellent solution that satisfy high speed network alert management challenges.

Survey on Architectures and Communication Libraries dedicated for High Speed Networks

This paper studies the evolution of high performance computing (HPC) and its trends. It exposes the different architectures used in HPC, the common high-speed networks, the programming models, the communications models, and the communication libraries.

Wild-Inspired Intrusion Detection System Framework for High Speed Networks f

While the rise of the Internet and the high speed networks made information easier to acquire, faster to exchange and more flexible to share, it also made the cybernetic attacks and crimes easier to perform, more accurate to hit the target victim and more flexible to conceal the crime evidences. Although people are in an unsafe digital environment, they often feel safe. Being aware of this fact and this fiction, the authors draw in this paper a security framework aiming to build real-time security solutions in the very narrow context of high speed networks. This framework is called f|p since it is inspired by the elefant self-defense behavior which yields p 22 security tasks for 7 security targets.

RoadSense: Smartphone Application to Estimate Road Conditions Using Accelerometer and Gyroscope

Monitoring the road condition has acquired a critical significance during recent years. There are different reasons behind broadening research on this field: to start with, it will guarantee safety and comfort to different road users; second, smooth streets will cause less damage to the car. Our motivation is to create a real-time Android Application RoadSense that automatically predicts the quality of the road based on a tri-axial accelerometer and a gyroscope, show the road location trace on a geographic map using GPS, and save all recorded workout entries. C4.5 Decision tree classifier is applied on training data to classify road segments and to build our model. Our experimental results show consistent accuracy of 98.6%. Using this approach, we expect to visualize a road quality map of a selected region. Hence, we can provide constructive feedback to drivers and local authorities. Besides, road manager can benefit from this system to evaluate the state of their road network and make a checkup on road construction projects, whether they meet or not the required quality.

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