Robot Swarms in an Uncertain World: Controllable Adaptability, Page 187-196
Olga Bogatyreva & Alexandr Shillerov, The University of Bath, Mechanical Engineering Department, Bath, UK

Abstract: There is a belief that complexity and chaos are essential for adaptability. But life deals with complexity every moment, without the chaos that engineers fear so, by invoking goal-directed behaviour. Goals can be programmed. That is why living organisms give us hope to achieve adaptability in robots. In this paper a method for the description of a goal-directed, or programmed, behaviour, interacting with uncertainty of environment, is described. We suggest reducing the structural (goals, intentions) and stochastic components (probability to realise the goal) of individual behaviour to random variables with nominal values to apply probabilistic approach. This allowed us to use a Normalized Entropy Index to detect the system state by estimating the contribution of each agent to the group behaviour. The number of possible group states is 27. We argue that adaptation has a limited number of possible paths between these 27 states. Paths and states can be programmed so that after adjustment to any particular case of task and conditions, adaptability will never involve chaos. We suggest the application of the model to operation of robots or ther devices in remote and/or dangerous places.
Keywords: robots, swarm intelligence, entropy, complexity, adaptability

Hybrid Kalman Filter/Fuzzy Logic based Position Control of Autonomous Mobile Robot, Page 197 - 208
Rerngwut Choomuang & Nitin Afzulpurkar, Asian Institute of Technology, School of Advanced Technologies, Pathumthani, Thailand

Abstract: This paper describes position control of autonomous mobile robot using combination of Kalman filter and Fuzzy logic techniques. Both techniques have been used to fuse information from internal and external sensors to navigate a typical mobile robot in an unknown environment. An obstacle avoidance algorithm utilizing stereo vision technique has been implemented for obstacle detection. The odometry errors due to systematic-errors (such as unequal wheel diameter, the effect of the encoder resolution etc.) and/or non-systematic errors (ground plane, wheel-slip etc.) contribute to various motion control problems of the robot. During the robot moves, whether straight-line and/or arc, create the position and orientation errors which depend on systematic and/or non-systematic odometry errors. The main concern in most of the navigating systems is to achieve the real-time and robustness performances to precisely control the robot movements. The objective of this research is to improve the position and the orientation of robot motion. From the simulation and experiments, we prove that the proposed mobile robot moves from start position to goal position with greater accuracy avoiding obstacles.
Keywords: mobile filter, robot, extended Kalman fuzzy logic, obstacle avoidance, stereo vision system

Learning Innovative Routes for Mobile Robots in Dynamic Partially Unknown Environments, Page 209 - 222
Kristo Heero, Alvo Aabloo & Maarja Kruusmaa Institute of Technology, Tartu University, Estonia

Abstract: This paper examines path planning strategies in partially unknown dynamic environemnts and introduces an approach to learning innovative routes. The approach is verified against shortest path planning with a distance transform algorithm, local and global replanning and suboptimal route following in unknown, partially unknown, static and dynamic environments. We show that the learned routes are more reliable and when traversed repeatedly the robot’s behaviour becomes more predictable. The test results also suggest that the robot’s behaviour depends on knowledge about the environemnt but not about the path planning strategy used.
Keywords: path planning, mobile robots, dynamic environment, robot learning

Neural Processing of Auditory Signals and Modular Neural Control for Sound Tropism of Walking Machines, Page 223 - 234
Poramate Manoonpong; Frank Pasemann; Joern Fischer & Hubert Roth, Fraunhofer Institut Autonome Intelligente Systeme, Sankt Augustin, Germany and Institut für Regelungs und Steuerungstechnik (RST), Elektrotechnik und Informatik, Siegen, Germany

Abstract: The specialized hairs and slit sensillae of spiders (Cupiennius salei) can sense the airflow and auditory signals in a low-frequency range. They provide the sensor information for reactive behavior, like e.g. capturing a prey. In analogy, in this paper a setup is described where two microphones and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right. The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it.
Keywords: recurrent neural networks, neural control, auditory signal processing, autonomous robots, walking machines

Study of Self-Organization Model of Multiple Mobile Robot, Page 235 - 238
Ceng Xian-yi; Li Shu-qin & Xia De-shen, Department of Computer Science , NanJing University of Science & Technology P.R.China, State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, P.R.China and Computer Science & Communication Engineering Institute of Jiangsu University P.R. China

Abstract: A good organization model of multiple mobile robot should be able to improve the efficiency of the system, reduce the complication of robot interactions, and detract the difficulty of computation. From the sociology aspect of topology, structure and organization, this paper studies the multiple mobile robot organization formation and running mechanism in the dynamic, complicated and unknown environment. It presents and describes in detail a Hierarchical-Web Recursive Organization Model (HWROM) and forming algorithm. It defines the robot society leader; robotic team leader and individual robot as the same structure by the united framework and describes the organization model by the recursive structure. The model uses task-oriented and top-down method to dynamically build and maintain structures and organization. It uses market-based techniques to assign task, form teams and allocate resources in dynamic environment. The model holds several characteristics of self-organization, dynamic, conciseness, commonness and robustness.
Keywords: self-organization, mobile robot, agent

Optimal Point-to-Point Trajectory Tracking of Redundant Manipulators using Generalized Pattern Search, Page 239 - 244
Atef A. Ata & Thi Rein Myo, Mechatronics Engineering Department, Faculty of Engineering, International Islamic University Malaysia,Kuala Lumpur, Malaysia

Abstract: Optimal point-to-point trajectory planning for planar redundant manipulator is considered in this study. The main objective is to minimize the sum of the position error of the end-effector at each intermediate point along the trajectory so that the end-effector can track the prescribed trajectory accurately. An algorithm combining Genetic Algorithm and Pattern Search as a Generalized Pattern Search GPS is introduced to design the optimal trajectory. To verify the proposed algorithm, simulations for a 3-D-O-F planar manipulator with different end-effector trajectories have been carried out. A comparison between the Genetic Algorithm and the Generalized Pattern Search shows that the GPS gives excellent tracking performance.
Keywords: optimal trajectory, redundant, pattern search, genetic algorithms

Robotics Vision-based Heuristic Reasoning for Underwater Target Tracking and Navigation, Page 245-250
Chua Kia & Mohd Rizal Arshad, Underwater Robotics Research Group (URRG),´School of Electrical and Electronics Engineering, Universiti Sains Malaysia, Penang, Malaysia

Abstract: This paper presents a robotics vision-based heuristic reasoning system for underwater target tracking and navigation. This system is introduced to improve the level of automation of underwater Remote Operated Vehicles (ROVs) operations. A prototype which combines computer vision with an underwater robotics system is successfully designed and developed to perform target tracking and intelligent navigation. This study focuses on developing image processing algorithms and fuzzy inference system for the analysis of the terrain. The vision system developed is capable of interpreting underwater scene by extracting subjective uncertainties of the object of interest. Subjective uncertainties are further processed as multiple inputs of a fuzzy inference system that is capable of making crisp decisions concerning where to navigate. The important part of the image analysis is morphological filtering. The applications focus on binary images with the extension of gray-level concepts. An open-loop fuzzy control system is developed for classifying the traverse of terrain. The great achievement is the system’s capability to recognize and perform target tracking of the object of interest (pipeline) in perspective view based on perceived condition. The effectiveness of this approach is demonstrated by computer and prototype simulations. This work is originated from the desire to develop robotics vision system with the ability to mimic the human expert’s judgement and reasoning when maneuvering ROV in the traverse of the underwater terrain.
Keywords: fuzzylogic, underwater target tracking, autonomous underwater vehicles, artificial intelligence, simulation, robot navigation, vision system

Obstacle Avoidance in Groping Locomotion of a Humanoid Robot, Page 251-258
Hanafiah Yussof; Mitsuhiro Yamano; Yasuo Nasu; Kazuhisa Mitobe; Masahiro Ohka, Department of Complex Systems Science, Graduate School of Information Science Nagoya University, Nagoya, Japan, Department of Mechanical Systems Engineering, Faculty of Engineering Yamagata University, Jonan, Japan

Abstract: This paper describes the development of an autonomous obstacle-avoidance method that operates in conjunction with groping locomotion on the humanoid robot Bonten-Maru II. Present studies on groping locomotion consist of basic research in which humanoid robot recognizes its surroundings by touching and groping with its arm on the flat surface of a wall. The robot responds to the surroundings by performing corrections to its orientation and locomotion direction. During groping locomotion, however, the existence of obstacles within the correction area creates the possibility of collisions. The objective of this paper is to develop an autonomous method to avoid obstacles in the correction area by applying suitable algorithms to the humanoid robot’s control system. In order to recognize its surroundings, six-axis force sensors were attached to both robotic arms as end effectors for force control. The proposed algorithm refers to the rotation angle of the humanoid robot’s leg joints due to trajectory generation. The algorithm relates to the groping locomotion via the measured groping angle and motions of arms. Using Bonten-Maru II, groping experiments were conducted on a wall’s surface to obtain wall orientation data. By employing these data, the humanoid robot performed the proposed method autonomously to avoid an obstacle present in the correction area. Results indicate that the humanoid robot can recognize the existence of an obstacle and avoid it by generating suitable trajectories in its legs.
Keywords: humanoid robot, autonomous obstacle-avoidance method, groping locomotion, six-axis force sensor, trajectory generation

New Intelligent Transmission Concept for Hybrid Mobile Robot Speed Control, Page 259-263
Nazim Mir-Nasiri & Sulaiman Hussaini, Department of Mechatronics, Faculty of Engineering International Islamic University Malaysia, Kuala Lumpur, Malaysia

Abstract: This paper presents a new concept of a mobile robot speed control by using two degree of freedom gear transmission. The developed intelligent speed controller utilizes a gear box which comprises of epicyclic gear train with two inputs, one coupled with the engine shaft and another with the shaft of a variable speed dc motor. The net output speed is a combination of the two input speeds and is governed by the transmission ratio of the planetary gear train. This new approach eliminates the use of a torque converter which is otherwise an indispensable part of all available automatic transmissions, thereby reducing the power loss that occurs in the box during the fluid coupling. By gradually varying the speed of the dc motor a stepless transmission has been achieved. The other advantages of the developed controller are pulling over and reversing the vehicle, implemented by intelligent mixing of the dc motor and engine speeds. This approach eliminates traditional braking system in entire vehicle design. The use of two power sources, IC engine and battery driven DC motor, utilizes the modern idea of hybrid vehicles. The new mobile robot speed controller is capable of driving the vehicle even in extreme case of IC engine failure, for example, due to gas depletion.
Keywords: hybrid drive, intelligent gear transmission, fuzzy logic