Real-Time Path Planning and Navigation for a Web-Based Mobile Robot Using a Modified Ant Colony Optimization Algorithm KUAN-YU CHEN*, CHIA-YUN LIN, CHENG-CHIN CHIEN, JING-HUEI TSAI, YU-CHING LIU Department of Mechanical Engineering Chung Yuan Christian University 200, Chungpei Rd., Chungli City, Taoyuan County, Taiwan 32023 R.O.C. gychen@cycu.edu.tw Abstract: - This paper presents the use of a modified ant colony optimization algorithm and interactive web technologies for the problems of real-time path planning and navigation for an autonomous mobile robot. Assume that the mobile robot serves in an office building for delivering documents and packages, and all staff in different locations can assign tasks to the robot via a web-based application. Therefore, how to rapidly determine or update the path planning for the robot is a top priority. In this paper, we firstly apply interactive web technologies to develop the web-based application including two interfaces of client-side and server-side. The client-side is a graphical user interface for task assignments and monitoring real-time state of the mobile robot; moreover, the server-side interface combines the computation kernel of a modified ant colony optimization algorithm for generating a feasible path planning with a database management system for recording purpose. Secondly, a precise indoor localization system for the mobile robot using wireless sensor network and binocular vision is also presented. Finally, simulation results show that our proposed approach allows users to assign tasks to the mobile robot via the Internet, and then the mobile robot can complete these tasks along a feasible path that is generated by a modified ant colony optimization algorithm. Key-Words: - Ant colony optimization, mobile robot, path planning, web-based interface, wireless sensor network localization 1 Introduction The problems of real-time path planning and navigation have become key issues for extending the use of mobile robots. Furthermore, the Internet technology provides a convenient way for users to interact with a mobile service robot for assigning tasks and online monitoring of its status. Therefore, the purpose of this paper is to integrate both realtime path planning and interactive web technologies into an autonomous mobile robot based on wireless sensor network (WSN) localization for delivering documents and packages in an office environment. In general, in order to achieve an autonomous navigation capability, a mobile robot must be able to plan a feasible path from a start to a destination in a working environment. Meanwhile, path planning can also be viewed as a constrained dynamic optimization problem. A number of solutions have been recently proposed in the literature to address this problem, such as neural networks [1], [2], fuzzy logic [3], [4], genetic algorithms [5]-[7], hybrid approaches [8], [9], and swarm intelligence-based optimization algorithms. Here, swarm intelligence is a form of artificial intelligence based upon the study of collective behavior in many kinds of animal groups, especially for social insects such as ants and bees [10]. Ant colony optimization (ACO) [11]-[14] is one of the popular swarm inspired methods and has been proved to be highly effective and successful for many types of optimization problems and the robot path planning problem [15]-[19]. However, we should notice that the precise localization capability of mobile robots is central to path planning and navigation. Nowadays, the precise localization it still remains a challenge, especially in the indoor environment. For solving this problem, WSN is a less power consuming and much cheaper wireless technique than other types. There are several methods for locating wireless sensor nodes, such as angle of arrival (AOA), time of arrival (TOA), and received signal strength indicator (RSSI) [20]-[24]. The advantage of employing the RSSI values is that no extra hardware is needed for network-centric localization [25]. But according to the results have been done in the literature, the positioning error is still above 1 m. Therefore, we propose a modified binocular vision algorithm used with the WSN localization system in this paper for reducing the positioning error to less than 20 cm within the range of 2 m [26]. ISBN: 978-1-61804-177-7 179
In addition, controlling mobile robots remotely through the Internet has increased the value of their application [27]-[30]. In this paper, we apply interactive web technologies to develop the webbased application including two interfaces of clientside and server-side. The client-side is a graphical user interface (GUI) for task assignments and monitoring real-time state of the mobile robot, and the server-side interface combines the computation kernel of a modified ACO algorithm for generating a feasible path planning with a database management system for recording purpose. The remainder of this paper is organized as follows. Section 2 briefly describes the proposed modified ACO algorithm. Section 3 presents the architecture of the web-based mobile robot, including the interactive interface, the localization system based on WSN and stereo vision. Simulation results and performance of the proposed scheme are presented and discussed in Section 4. Finally, conclusions are given in Section 5. 2 The Ant Colony Optimization Ant colony optimization (ACO) is a kind of metaheuristics inspired by the collective behavior of real ants in seeking the shortest paths between their nest and a source of food. ACO has been applied to solve many different types of optimization problems, such as the traveling salesman problem, the quadratic assignment problem, the vehicle routing problem, the job-shop scheduling problem, and the optimal path planning problem for mobile robots. In this paper, the proposed path planning algorithm for a mobile robot is a modification of the original ACO proposed by Dorigo et al. [11]-[14] for solving the traveling salesman problem. The major difference is the constraint of space in indoor environment. Here, assume that a mobile service robot serves in an office building for delivering documents and packages, and Fig. 1 shows the planar graph of this working environment. There are many offices connect by an approximately squareshaped corridor. If two tasks assigned to the robot at the same time, one is from room 207 to room 205 and the other is from room 206 to room 202. Obviously, the shortest path is from room 207 to room 202 and stops at room 206 and room 202, not the path firstly starting from room 207 to room 205 and then moving back to room 206 and finally reaching at room 202. The modified ACO algorithm for the path finding problem in this paper basically consists of 4 steps: (1) Initialization: Initialize the pheromone Fig. 1 The planar graph of the working environment. trails and set all parameters; (2) Iteration: While stopping criteria is not met do the following steps: (i) Build a solution and record the visited nodes by the ant in its tabu table; (ii) Improve the solution by local search; (iii) Update the pheromone trails by the solution; (3) Checking constraints: The solution must satisfy the space constraints as shown in Fig. 1; (4) Termination: Return the best solution found. Here, an ant located at node selects a path to go to another node is given by the transition probability:, if, (1) 0, otherwise, where denotes the pheromone trail intensity, is the inverse of the distance between nodes and and known as visibility, is the set of nodes not yet visited by ant, and are the positive parameters that determine the relative importance of path versus visibility. Moreover, in order to avoid the ACO algorithm falling into the local optimal solution, local pheromone updating rule is implemented after each ant completes its solution. The pheromone value on each path will be updated by 1 1, (2) where is the number of ants, is the evaporation rate ( 01) and is the amount of pheromone deposited by the th ant going from node to node and defined as, if, solution done by ant, (3) 0, otherwise, where is the length of the solution done by ant s and denotes a constant that controls the magnitude of the pheromone contribution. When every ant has found its solution, the globally best ISBN: 978-1-61804-177-7 180
solution will be used to update the pheromone according to the global updating rule, as given by 1 1, (4) where is and defined as 1, if, globally best solution, (5) 0, otherwise. Here, is the total length of the globally best solution. 3 The Web-Based Mobile Robot In this paper, we present a new implementation of a web-based mobile robot as shown in Fig. 2. The robot consists of three major components: a webbased GUI, an indoor WSN-based localization system, and a modified stereo vision model for improving the accuracy of the indoor localization system. In this paper, a web-based GUI for remotely controlling and monitoring real-time state of the mobile robot is programmed following AJAX and ASP.NET server architecture as shown in Fig. 3. The left block provides users to add mission to the robot and shows the list of all missions. Furthermore, the right block provides the planar graph of the working environment and shows the real-time state of the robot. 3.2 WSN-based indoor localization system Indoor localization plays an important role in applications of WSNs. In this paper, we construct an indoor localization system for the mobile robot relying on the RSSI which is provided by the ZigBee platform. 3.1 Web-based interactive interface Currently there are many different web technologies which enable a robot to be controlled remotely through the Internet. Web technologies are generally what connects the interface between the servers and the clients and is made up of HTML (hypertext markup language), client-side and server-side scripts, and languages. Nowadays, web 2.0 is the latest trends in web design and the usage of web technologies [31]. The most characteristic feature of web 2.0 is dynamic content updating and AJAX (asynchronous JavaScript and XML) is becoming more and more popular with the prosperity of web 2.0. AJAX can not only retrieve data from a server asynchronously without interfering with the display and behavior of the existing page, but also greatly shorten network delay and basically change the interactive mode of web-based applications [32]. (a) (b) (a) (b) Fig. 2 Photograph of the web-based mobile robot with binocular vision: (a) Front left view and (b) back left view. (c) Fig. 3 Photograph of the web-based interface: (a) The starting page, (b) assigning a task and (c) the task shown on the map. ISBN: 978-1-61804-177-7 181
RSSI is a technique of estimating the distance between a transmitter and a receiver by calculating the attenuation of emitted signal strength being received. In free space, the received signal power at distance from the transmitter can be calculated using the empirical equation 10 log, (6) where is the known reference power at the short reference distance from the transmitter, is the path-loss exponent decided by the ambient environment and the building construction, and is the Gaussian distribution random error with the zero-mean and -standard variance [33]. In practice, and must be measured according to the real environment. Therefore, wireless sensor nodes can calculate distance from transmitter through their own received RSSI value. 3.3 Binocular vision-based localization In this paper, a modified model of stereo vision is presented to develop a more accurate algorithm for computing three-dimensional information from a stereo pair of images by modification of the stereo vision model, as shown in Fig. 4 [26]. Two cameras are separated by a distance in the -direction and both optical axes are parallel. For convenience, the coordinate system centered between two cameras is called the world coordinate system. The goal is to find the coordinates,, of the world point having corresponding points, and, in left and right images, respectively. From the similar triangles of imaging as shown in Figs. 4(a) and 4(b), we have 2 2 2, (7) 2 2 where and are the horizontal positions and is the vertical position of the object in the two images (in pixels), and are the image width and height (in centimeters), respectively. In Eq. (7), we may note that only the units of,, and are in pixels. Here, we assumed that the image resolution is (in pixels). After being converted pixels to centimeters, Eq. (7) can be expressed in the form 2 2. (8) 2 2 2 Then, the world coordinates,, may be computed as follows:, (9) 2 Optical axis,, World point Optical axis, /2 /2 World coordinate system CCD(L) CCD(R), /2 (a),, World point (b) Fig. 4 The modified model of binocular vision: (a) Top view and (b) right view. 2 2, (10). (11) In this paper, special signs are placed near every office entrance in the mobile robot environment as position markers for positioning error compensation. Namely, when the robot arrives near the office, it can find the special sign by its stereo vision system. Then the robot can accurately move to the front of the office entrance by using the proposed depth estimation method. 4 Simulation and Evaluation Due to space limitations, we can only present an example of simulation in this section to demonstrate the effectiveness of the proposed scheme. Fig. 5 shows the simulation result of the case of three tasks assigned to the web-based mobile robot concurrently, including Task 1: from room 206 to room 218, Task 2: from room 204 to room 203, and Task 3: from room 201 to room 205. We can see a flexible path generated by the proposed modified ACO algorithm shown in the bottom of the web- /2 Image plane Lens center /2 CCD Image planes, ISBN: 978-1-61804-177-7 182
based GUI, i.e. 207206204201203218. Here, we let the mobile robot be standby at the front of room 207. Subsequently, users can see a brown dot on the map representing the robot s current position in the working environment will move along the path correctly. 5 Conclusion In this paper an approach based on a modified ACO algorithm and interactive web technologies for the problem of real-time path planning has been applied to a web-based mobile robot. With this capability the mobile robot can be more flexible and effective in dealing with the dynamic task assignment in the working environment. Only simulation results have been carried out to demonstrate the effectiveness of the proposed scheme so far. Therefore, we will pay more attention to implementation of experiments in actual working environment in the near future. Acknowledgement: This work was supported by the National Science Council of Taiwan (R.O.C.) under grant number NSC 101-2221-E-033-005. References: [1] S. X. Yang and C. Luo, A neural network approach to complete coverage path planning, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, Vol. 34, No. 1, 2004, pp. 718-725. [2] H. Qu, S. X. Yang, A. R. Willms, and Z. Yi, Real-time robot path planning based on a modified pulse-coupled neural network model, IEEE Transactions on Neural Networks, Vol. 20, No. 11, 2009, pp. 1724-1739. [3] G. Antonelli, S. Chiaverini, and G. Fusco, A fuzzy-logic-based approach for mobile robot path tracking, IEEE Transactions on Fuzzy Systems, Vol. 15, No. 2, 2007, pp. 211-221. [4] M. Wang and J. N. K. Liu, Fuzzy logic based robot path planning in unknown environment, in Proceedings of the 4th International Conference on Machine Learning and Cybernetics, Guangzhou, China, Aug. 18-21, 2005, pp. 813-818. [5] O. Castillo and L. Trujillo, Multiple objective optimization genetic algorithms for path planning in autonomous mobile robots, International Journal of Computers, Systems and Signals, Vol. 6, No. 1, 2005, pp. 48-63. 6 1 5 2 Fig. 5 The simulation result of a set of tasks. [6] S. C. Yun, V. Ganapathy, and L. O. Chong, Improved genetic algorithms based optimum path planning for mobile robot, in Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision, Singapore, Dec. 7-10, 2010, pp. 1565-1570. [7] H. Zhang, M. Liu, R. Liu, and T. Hu, Path planning of robot in three-dimensional grid environment based on genetic algorithms, in Proceedings of the 7th World Congress on Intelligent Control and Automation, Chongqing, China, Jun. 25-27, 2008, pp. 1010-1014. [8] P. Li, X. Huang, and M. Wang, A hybrid method for dynamic local path planning, in Proceedings of the International Conference on Networks Security, Wireless Communications and Trusted Computing, Hubei, China, Apr. 25-26, 2009, Vol. 1, pp. 317-320. [9] H. C. Huang and C. C. Tsai, Global path planning for autonomous robot navigation using hybrid metaheuristic GA-PAO algorithm, in Proceedings of the SICE Annual Conference, Tokyo, Japan, Sep. 13-18, 2011, pp. 1338-1343. [10] E. Bonabeau, M. Dorigo and G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, New York: Oxford University Press, 1999. [11] A. Colorni, M. Dorigo, and V. Maniezzo, Distributed optimization by ant colonies, in Proceedings of the 1st European Conference on Artificial Life, Paris, France, Dec. 1-3, 1991, pp. 134-142. [12] M. Dorigo, Optimization, Learning and Natural Algorithms, PhD Thesis, Politecnico di Milano, Italy, 1992. [13] M. Dorigo, V. Maniezzo, and A. Colorni, Ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, Vol. 26, No. 1, 1996, pp. 29-41. 4 3 ISBN: 978-1-61804-177-7 183
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