106 Int. J. Bio-Inspired Computation, Vol. 9, No. 2, 2017 Copyright © 2017 Inderscience Enterprises Ltd. Mobile robot path planning with modified ant colony optimisation Utkarsh Rajput* and Madhu Kumari Computer Science and Engineering Department, National Institute of Technology, Hamirpur-177005, Himachal Pradesh, India Email: utkarshrjpt@gmail.com Email: madhu.jaglan@gmail.com Email: madhu@nith.ac.in *Corresponding author Abstract: Automated navigation is a pivotal task of robotics research and the key challenge lies in robot motion on unknown dynamic terrain. The large number of solutions to robotic path planning, especially in unknown and dynamic environments, mainly rely on the heuristic methods. The most important factor for this choice is the fast convergence towards solution without supervision. In the proposed scheme we have used a modified version of ant colony optimisation. We incorporated the directional movement history of robot on a grid into a vector as a probability multiplication factor which helps to achieve a faster convergence and avoid unnecessary movements, e.g., looping. In this work we have devised a novel pheromone updation scheme. Along with this we have applied path smoothing to lessen the number of turns on the candidate optimal path. Effectiveness is shown through several extensive experiments and results clearly indicate the aptness of the proposed scheme. Keywords: mobile robot; ant colony optimisation; ACO; path planning; path smoothing. Reference to this paper should be made as follows: Rajput, U. and Kumari, M. (2017) ‘Mobile robot path planning with modified ant colony optimisation’, Int. J. Bio-Inspired Computation, Vol. 9, No. 2, pp.106–113. Biographical notes: Utkarsh Rajput received his Bachelor of Technology in 2012 from Gautam Buddh Technical University, Lucknow, Uttar Pradesh, India. He received his Master’s from National Institute of Technology, Hamirpur, Himachal Pradesh, India. Machine learning and robotics are his major interest areas. Madhu Kumari is currently working as an Assistant Professor in the Department of Computer Science and Engineering at National Institute of Technology, Hamirpur, Himachal Pradesh, India. She received her Master’s and doctoral degrees from Jawaharlal Nehru University, New Delhi. Her previous work mostly focused on exploration of different variants of reinforcement learning in simulated Robocop soccer domain. She has worked on different aspects of computational advertising based on sponsored search auctions. Her current research is more aligned towards the deployment of machine learning techniques on application milieu like web, text and social analytics. 1 Introduction Mobile robots have their applications in diverse fields ranging from dangerous environments, like mining and nuclear industry to daily life applications, e.g., autopilot robots for cars and household work. These robots can be successfully used in hazardous environments where human life can be endangered. For the development of mobile robots, effective and efficient path planning methods are necessary for the locomotion. Robot path planning is a pivotal research problem and researchers are continuously trying to develop methods that can be used an integrable solution for this problem in a manner that can find the optimal path in real time with least number of constraints. But there has been less success achieved in this area. Initially the main focus was on the path planning in static environments where the obstacles are static and robot is the only mobile entity. Major techniques which have been extensively used in static environments based path planning includes cell decomposition method (Choset, 2007), visibility graph method (Mitchell, 1986), Voronoi diagrams (Kalra et al., 2009), etc. In cell decomposition method (Choset, 2007) environment map is decomposed into different sized cells labelled as obstacle and free cell. A possible navigation route for robot is through connected free cells only. The incremental algorithm in Kalra et al. (2009) uses techniques for constructing and updating generalised Voronoi diagrams and thus finding the route through unknown static environment. Genetic algorithm with