I.J. Intelligent Systems and Applications, 2015, 05, 24-30 Published Online April 2015 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijisa.2015.05.04 Copyright © 2015 MECS I.J. Intelligent Systems and Applications, 2015, 05, 24-30 Mobile Robot Path Planning by RRT* in Dynamic Environments Roudabe Seif Qazvin Islamic Azad University, Qazvin, Iran Email: seif_roudabe@yahoo.com Mohammadreza Asghari Oskoei Allameh Tabataba’i University, Tehran, Iran Email: oskoei@atu.ac.ir Abstract— Robot navigation is challenging for mobile robots technology in environments with maps. Since finding an optimal path for the agent is complicated and time consuming, path planning in robot navigation is an axial issue. The objective of this paper is to find a reasonable relation between parameters used in the path planning algorithm in a platform which a robot will be able to move from the start point in a dynamic environment with map and plan an optimal path to specified goal without any collision with moving and static obstacles. For this purpose, an asymptotically optimal version of Rapidly-exploring Random Tree RRT algorithm, named RRT* is used. The algorithm is based on an incremental sampling which covers the whole space and acts fast. Moreover this algorithm is computationally efficient, therefore it can be used in multidimensional environments. The obtained results indicate that a feasible path for mobile holomonic robot may be found in a short time by using this algorithm. Also different standard distances measurements like (Chebyshev, Euclidean, and City Block) are examined, and coordinated with sampling node number in order to reach the suitable result based on environment circumstances. Index Terms— Mobile Robot, Navigation, Path planning, Sampling-based Methods, RRT* I. INTRODUCTION This paper presents a new path planning method which helps the robot to reach the target from the beginning point without colliding fixed and mobile obstacles existing in search space. This method can be used in the environments where a camera can be installed above the searching space such as closed searching environments (large stores and factories).So it can use to guide worker robots from one point to a certain target in the manner that the robot reaches the destination without any collision with fixed and mobile obstacles in the environment. Obstacles are usually either fixed such as shelves of goods in the stores or mobile like other robots which hold specific duties. Solving the problem of finding the optimum path in map-equipped environment is a goal of search. Search problems are divided into classical and non-classical groups. Classical methods are based on Heuristic Searches. Random methods is a branch of Heuristic methods, called R* and a group of them is RRT. According to LaVall [1], the path planning algorithms are mainly divided into two groups: 1) Compound Path planning Algorithms; and 2) Sampling-based path planning algorithms. Compound path planning algorithms are complete and they have a complicated implementation. An algorithm is considered complete if for any input it correctly reports whether there is a solution in a finite amount of time. If a solution exists, it must return one in finite time [1]. The second group is not complete but it has a simple implementation. Many sampling-based approaches are based on random sampling, which is dense with “probability complete”. This leads to algorithms that with enough points, the probability that it finds an existing solution converges to one. The most relevant information, however, is the rate of convergence, which is usually very difficult to establish [1]. The second group is divided into two other groups, namely multiple query and single query. Multiple query methods like (PRM) Probabilistic Road Map method require preprocessing while single query methods like ACA (Adrian’s clew algorithm), ESP (Expansive Space Planner), Random Walker Planner and RDT (Rapidly Exploring Dense Tree) do not need any preprocessing and are suitable for dynamic environments. One type of RDTs is called RRT which is defined in the following. The sample-based technique for robot motion planning was introduced by Barraquand and Latombe [2]. The first sampling-based planners were not cost effective in view of operational load. Kavraki et al. and Overmars and ˇSvestka developed possible road map for path planning in configuration environments with high freedom levels [3,4,5]. Choset et al. and Hsu et al. made a comprehensive discussion on road map [6,7]. Song and Amato proved that PRM method is computationally efficient in static environments which are well-known [8]. On the other hand, this method is not suitable for dynamic environments. It does not consider vehicle dynamics of robot and it may not work in sharp rotations. Branicky et al. (2001) introduced LRM Lattice roadmap and quasi-PRM [9]. LRM was developed by Pivtoraiko et al. in order to consider dynamic restrictions [10]. RRT is one of the probabilistic path planners presented by Kuffner and Lavalle in order to solve differential