Exploratory Path Planning Using the Max-Min Ant System Algorithm

In the path planning problem for autonomous mobile robots, robots have to plan their path from the start position to the goal. In this paper, we investigate the application of the MMAS algorithm to the exploratory path planning problem, in which the robots should explore the environment at the same time they plan the path. Max-min ant system is an ant colony optimization algorithm that exploits the best solutions found. In addition, to analyze the quality of solutions obtained, we also analyze the traveled distance spent by robots in the first iteration of the algorithm. The environment is previously unknown to the robots, although it is represented by a topological map, that does not require precise information from the environment and provides a simple way to execute the navigation of the path. Thus, the paths are represented by a sequence of actions that the robots should execute to reach the goal. The navigation of the best solution found was implemented in a realistic robotic simulator. The proposed algorithm provides a very good performance in relation to a genetic algorithm and the well-known A* algorithm that deal with this problem

Exploratory Path Planning for Mobile Robots in Dynamic Environments with Ant Colony Optimization

In the path planning task for autonomous mobile robots, robots should be able to plan their trajectory to leave the start position and reach the goal, safely. There are several path planning approaches for mobile robots in the literature. Ant Colony Optimization algorithms have been investigated for this problem, giving promising results. In this paper, we propose the Max-Min Ant System for Dynamic Path Planning algorithm for the exploratory path planning task for autonomous mobile robots based on topological maps. A topological map is an environment representation whose focus is the main reference points of the environment and their connections. Based on this representation, the path can be composed by a sequence of state/actions pairs, which facilitates the navigability of the path, with no need to have the information of the complete map. The proposed algorithm was evaluated in static and dynamic envi- ronments, showing promising results in both of them. Experiments in dynamic environments show the adaptability of our proposal

H-ACO: A Heterogeneous Ant Colony Optimisation approach with Application to the Travelling Salesman Problem

This is the author accepted manuscript. The final version is available from the publisher via the link in this record.Ant Colony Optimization (ACO) is a field of study that mimics the behaviour of ants to solve computationally hard problems. The majority of research in ACO focuses on homogeneous artificial ants although animal behaviour research suggests that heterogeneity of behaviour improves the overall efficiency of ant colonies. Therefore, this paper introduces and analyses the effects of heterogeneity of behavioural traits in ACO to solve hard optimisation problems. The developed approach implements different behaviour by introducing unique biases towards the pheromone trail and local heuristic (the next hop distance) for each ant. The well-known Ant System (AS) and Max-Min Ant System (MMAS) are used as the base algorithms to implement heterogeneity and experiments show that this method improves the performance when tested using several Travelling Salesman Problem (TSP) instances particularly for larger instances. The diversity preservation introduced by this algorithm helps balance exploration-exploitation, increases robustness with respect to parameter settings and reduces the number of algorithm parameters that need to be set.We would like to thank the Faculty of Electronics and Computer Engineering (FKEKK), Technical University of Malaysia Malacca (UTeM) and the Ministry of Higher Education (MoHE) Malaysia for the financial support under the SLAB/SlAI program

Performance evaluation of Max-Min Ant System Algorithm for Robot Path Planning in Grid Environment

Path planning is an essential task for the robot to navigate and control its motion in any environment. The optimal path needs to be rerouted each time a new obstacle appears in front of the robot in a dynamic environment. This research focuses on the MAX-MIN Ant System Algorithm(MMAS) which is an Ant Colony Algorithm derived from Ant System(AS) and is different from it in terms of the pheromone deposition. The effectiveness of this algorithm to obtain a near optimal solution is illustrated by the means of experimental study. Using a greedier search than the Ant System algorithm is one of the specific characteristics of the MMAS, which will be studied in the research. The robot environment model is represented by a grid which has obstacles whose positions change in each map that is used. Local search routines and diversification mechanisms introduced by the previous researchers are used to enhance the performance of the MMAS algorithm. To implement the MMAS algorithm used in our research, the experiments are performed in Matlab development environment where a simulation program is designed, and the algorithm is implemented in grid maps of sizes starting from the smallest grid 10x10 to the grid of size 400x400. We implemented and analyzed the performance of the algorithm in larger grid environments to understand how it would perform when the search space is too huge; which would enable researchers to use the MMAS algorithm in experiments involving real-life environments. In our experiments, a new obstacle is added after every iteration of the algorithm which makes it challenging for the robots to find the near-optimal path. The performance evaluation of the MMAS algorithm is studied and is also compared to that of the ACO algorithm when implemented in differently sized grid maps

Heterogeneous Ant Colony Optimisation Methods and their Application to the Travelling Salesman and PCB Drilling Problems

Ant Colony Optimization (ACO) is an optimization algorithm that is inspired by the foraging behaviour of real ants in locating and transporting food source to their nest. It is designed as a population-based metaheuristic and have been successfully implemented on various NP-hard problems such as the well-known Traveling Salesman Problem (TSP), Vehicle Routing Problem (VRP) and many more. However, majority of the studies in ACO focused on homogeneous artificial ants although animal behaviour researchers suggest that real ants exhibit heterogeneous behaviour thus improving the overall efficiency of the ant colonies. Equally important is that most, if not all, optimization algorithms require proper parameter tuning to achieve optimal performance. However, it is well-known that parameters are problem-dependant as different problems or even different instances have different optimal parameter settings. Parameter tuning through the testing of parameter combinations is a computationally expensive procedure that is infeasible on large-scale real-world problems. One method to mitigate this is to introduce heterogeneity by initializing the artificial agents with individual parameters rather than colony level parameters. This allows the algorithm to either actively or passively discover good parameter settings during the search. The approach undertaken in this study is to randomly initialize the ants from both uniform and Gaussian distribution respectively within a predefined range of values. The approach taken in this study is one of biological plausibility for ants with similar roles, but differing behavioural traits, which are being drawn from a mathematical distribution. This study also introduces an adaptive approach to the heterogeneous ant colony population that evolves the alpha and beta controlling parameters for ACO to locate near-optimal solutions. The adaptive approach is able to modify the exploitation and exploration characteristics of the algorithm during the search to reflect the dynamic nature of search. An empirical analysis of the proposed algorithm tested on a range of Travelling Salesman Problem (TSP) instances shows that the approach has better algorithmic performance when compared against state-of-the-art algorithms from the literature

Robot Path Planning with IGA-MMAS and MMAS-IGA

Path Planning of mobile robots is one of the essential tasks in robotic research and studies with intelligent technologies. It helps in determining the path from a source to the destination. It has extended its roots from classic approaches to further improvements over time, such as evolutionary approaches. Ant Colony Optimization (ACO) and Genetic algorithm are well known evolutionary approaches in effective path planning. This research work focuses on the Max-Min Ant System (MMAS) derived from the ACO evolutionary approach of Ant System (AS) and Improved Genetic Algorithm (IGA) which is efficient over the classical Genetic Algorithm. In-order to study robot path planning two methods are combined in this research work combining MMAS and IGA as two-hybrid methods MMAS-IGA and IGA-MMAS . The results of the two-hybrid methods will be deriving the near optimal solution, demonstrated in the experimental study of this work. Grid maps are used for simulating the robot path planning environment which is modeled using the grid method. Genetic operators of IGA are combined with MMAS for the enhancement of the overall result of the methods IGA-MMAS and MMAS-IGA. The effectiveness of these two methods will be determined in the simulation modeled using MATLAB environment. The experimental results of these methods are done in a static environment and the results of MMAS-IGA and IGA-MMAS are compared to the path planning method GA-ACO

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing