Robot Localization and Map Building Part 6 docx

KeyElementsforMotionPlanningAlgorithms 169 The interactions may involve objects in the simulation environment pushing, striking, or smashing other objects. Detecting collisions and determining contact points is a crucial step in portraying these interactions accurately. The most challenging problem in a simulation, namely the collision phase, can be separated into three parts: collision detection, contact area determination, and collision response. 4.1 Rapid version 2.01 RAPID is a robust and accurate polygon interference detection library for large environments composed of unstructured models ( http://www.cs.unc.edu/~geom/OBB/OBBT.html).  It is applicable to polygon soups - models which contain no adjacency information, and obey no topological constraints. The models may contain cracks, holes, self- intersections, and nongeneric (e.g. coplanar and collinear) configurations.  It is numericaly robust - the algorithm is not subject to conditioning problems, and requires no special handling of nongeneric cases (such as parallel faces). The RAPID library is free for non-commercial use. Please use this request form to download the latest version. It has a very simple user interface: the user need noncommercial use. Be familiar with only about five function calls. A C++ sample client program illustrates its use. The fundamental data structure underlying RAPID is the OBBTree, which is a hierarchy of oriented bounding boxes (a 3D analog to the "strip trees" of Ballard). (Gottschalk et al., 1996). 5. GEMPA: Graphic Environment for Motion Planning Algorithms Computer graphics has grown phenomenally in recent decades, progressing from simple 2- D graphics to complex, high-quality, three-dimensional environments. In entertainment, computer graphics is used extensively in movies and computer games. Animated movies are increasingly being made entirely with computers. Even no animated movies depend heavily on computer graphics to develop special effects. The capabilities of computer graphics in personal computers and home game consoles have now improved to the extent that low-cost systems are able to display millions of polygons per second. The representation of different environments in such a system is used for a widely researched area, where many different types of problems are addressed, related to animation, interaction, and motion planning algorithms to name a few research topics. Although there are a variety of systems available with many different features, we are still a long way from a completely integrated system that is adaptable for many types of applications. This motivates us to create and build a visualization tool for planners capable of using physics-based models to generate realistic-looking motions. The main objective is to have a solid platform to create and develop algorithms for motion planning methods that can be launched into a digital environment. The developed of these tools allows to modify or to adapt the visualization tool for different kind of problems (Benitez & Mugarte, 2009). 5.1 GEMPA Architecture GEMPA architecture is supported by necessary elements to represent objects, geometric transformation tools and visualization controls. These elements are integrated to reach initial goals of visualization and animation applied to motion planning problems. Fig. 11. Several modules are coupled to integrate the initial GEMPA architecture which offer interesting functionalities; visualization 3-D environments as well as animation of motion planning algorithms. 5.2 Recovering Objects Representation People focus to solve problems using computer graphics, virtual reality and simulation of motion planning techniques used to recover information related to objects inside the environment through files which can storage information about triangle meshes. Hence, several objects can be placed on different positions and orientations to simulate a three- dimensional environment. There exist different formats to represent objects in three- dimensional spaces (3-D), however, two conventions used for many tools to represent triangle meshes are the most popular; objects based on off - files and objects based on txt - files. In motion planning community there exist benchmarks represented through this kind of files. GEMPA is able to load the triangle meshes used to represent objects from txt or off – files. On the other hand, GEMPA allows the user to built news environments using predefined figures as spheres, cones, cubes, etc. These figures are chosen from a option menu and the user build environments using translation, rotation and scale transformations. Each module on GEMPA architecture is presented in Figure 11 There, we can see that initially, the main goal is the visualization of 3-D environments and the animation of motion planning algorithms. In the case of visualization of 3-D environments, information is recovered form files and the user can navigate through the environment using mouse and keyboard controls. In the second case, the animation of motion planning algorithms, GEMPA needs information about the problem. This problem is described by two elements; the first one is called workspace, where obstacles (objects), robot representation and configuration (position and orientation) is recovered from files; the second, a set of free RobotLocalizationandMapBuilding170 collision configuration conform a path, this will be used to animate the robot movement from initial to goal configuration. An example of 2D environment can be seen in Figure 12. Fig. 12. Two different views of two-dimensional environment since the X-Y plane are painted. 5.3 GUI and Navigation Tools GEMPA has incorporated two modes to paint an object; wire mode and solid mode. Next, Lambert illumination is implemented to produce more realism, and finally transparency effects are used to visualize the objects. Along the GUI, camera movements are added to facilitate the navigation inside the environment to display views from different locations. In Figure 13. Two illumination techniques are presented when GEMPA recover information since off-files to represent a human face. Fig. 13. Light transparency. In the left side, an object is painted using Lambert illumination, in the right side, transparency effect is applied on the object. Both features are used to give more realism the environment. 5.4 Simulation of Motion Planning Algorithms Initially, only PRM for free flying objects are considered as an initial application of GEMPA. Taking into account this assumption, the workspace is conformed by a set of obstacles (objects) distributed on the environment, these objects has movement restrictions that mean that, the obstacles can not change their position inside the environment. In addition, an object that can move through the workspace is added to the environment and is called robot. The robot can move through the workspace using the free collision path to move from the initial configuration to the goal configuration. For PRM for free flying objects, only a robot can be defined and the workspace can include any obstacles as the problem need. GEMPA also includes the capability to recover from an environment – file information about the position and orientation for each object inside a workspace including the robot configuration. Hence, GEMPA can draw each element to simulate the workspace associated. Therefore, initially GEMPA can recover information about the workspace, an example of this file can be see in Figure 14, where the environment file (left side), include initial and goal configuration for the robot, beside includes x,y,z parameter for position and (α, β, γ) parameters for orientation for every objects inside the workspace. Along with this environment file, a configuration - file can also be loaded to generate the corresponding animation of the free collision path. This configuration - file has the form presented in Figure 14 (right side). This file is conformed by n six-tuples (x, y, z,α, β, γ) to represent each configuration included in the free collision path. Fig. 14. On the left side, an example of environment - file (robot and obstacles representations) is presented, and on the right side a configuration file (free collision path) is shown. Once GEMPA has recovered information about workspace and collision free path, the tool allows the user to display the animation on three different modes. Mode 1: Animation painting all configurations. Mode 2: Animation painting configurations using a step control. Mode 3: Animation using automatic step. From Figures 15 to Figure 18, we can see four different samples of motion planning problems which are considered as important cases. For each one, different views are KeyElementsforMotionPlanningAlgorithms 171 collision configuration conform a path, this will be used to animate the robot movement from initial to goal configuration. An example of 2D environment can be seen in Figure 12. Fig. 12. Two different views of two-dimensional environment since the X-Y plane are painted. 5.3 GUI and Navigation Tools GEMPA has incorporated two modes to paint an object; wire mode and solid mode. Next, Lambert illumination is implemented to produce more realism, and finally transparency effects are used to visualize the objects. Along the GUI, camera movements are added to facilitate the navigation inside the environment to display views from different locations. In Figure 13. Two illumination techniques are presented when GEMPA recover information since off-files to represent a human face. Fig. 13. Light transparency. In the left side, an object is painted using Lambert illumination, in the right side, transparency effect is applied on the object. Both features are used to give more realism the environment. 5.4 Simulation of Motion Planning Algorithms Initially, only PRM for free flying objects are considered as an initial application of GEMPA. Taking into account this assumption, the workspace is conformed by a set of obstacles (objects) distributed on the environment, these objects has movement restrictions that mean that, the obstacles can not change their position inside the environment. In addition, an object that can move through the workspace is added to the environment and is called robot. The robot can move through the workspace using the free collision path to move from the initial configuration to the goal configuration. For PRM for free flying objects, only a robot can be defined and the workspace can include any obstacles as the problem need. GEMPA also includes the capability to recover from an environment – file information about the position and orientation for each object inside a workspace including the robot configuration. Hence, GEMPA can draw each element to simulate the workspace associated. Therefore, initially GEMPA can recover information about the workspace, an example of this file can be see in Figure 14, where the environment file (left side), include initial and goal configuration for the robot, beside includes x,y,z parameter for position and (α, β, γ) parameters for orientation for every objects inside the workspace. Along with this environment file, a configuration - file can also be loaded to generate the corresponding animation of the free collision path. This configuration - file has the form presented in Figure 14 (right side). This file is conformed by n six-tuples (x, y, z,α, β, γ) to represent each configuration included in the free collision path. Fig. 14. On the left side, an example of environment - file (robot and obstacles representations) is presented, and on the right side a configuration file (free collision path) is shown. Once GEMPA has recovered information about workspace and collision free path, the tool allows the user to display the animation on three different modes. Mode 1: Animation painting all configurations. Mode 2: Animation painting configurations using a step control. Mode 3: Animation using automatic step. From Figures 15 to Figure 18, we can see four different samples of motion planning problems which are considered as important cases. For each one, different views are RobotLocalizationandMapBuilding172 presented to show GEMPA’s functionalities. Besides, we have presented motion planning problems with different levels of complexity. In Figure 15. (Sample 1) The collision free path is painted as complete option and as animation option. In this sample a tetrahedron is considered as the robot. Next, Figure 16: (Sample 2). A cube is presented as the robot for this motion planning problem. Here, GEMPA presents the flat and wire modes to paint the objects. In Figure 17: (Sample 3). Presents a robot which has a more complex for and the problem becomes difficult to solve because the motion planning method needs to compute free configuration in the narrow corridor. Finally in Figure 18: (Sample 4). Animation painting all configurations (left side), and animation using automatic step (right side) are displayed. Although the robot has not a more complex form, there are various narrow corridors inside the environment. Fig. 15. Sample 1. The robot is presented as a tetrahedron. Fig. 16. Sample 2. The robot is presented as a cube. Fig. 17. Sample 3. The robot’s form is more complex. Fig. 18. Sample 4. More complex environment where various narrow corridors are presented. 6. References Amato, N.; Bayazit, B. ; Dale, L.; Jones, C. &. Vallejo, D. (1998). Choosing good distance metrics and local planer for probabilistic roadmap methods. In in Procc.IEEE Int. Conf. Robot. Autom. (ICRA), pages 630–637. Amato, N.; Bayazit, B. ; Dale, L.; Jones, C. &. Vallejo, D. (1998). Obprm: An obstaclebased prm for 3d workspaces. In in Procc. Int. Workshop on Algorithmic Fundation of Robotics (WAFR), pages 155–168. Amato, N. M. & Wu, Y. (1996). A randomized roadmap method for path and manipulation planning. In In IEEE Int. Conf. Robot. and Autom., pages 113–120. Amato, N. Motion ning puzzels benchmarks. Benitez, A. & Mugarte, A. (2009). GEMPA:Graphic Environment for Motion Planning Algorithm. In Research in Computer Science, Advances in Computer Science and Engineering. Volumen 42. Benitez, A. & Vallejo, D. (2004). New Technique to Improve Probabilistic Roadmap Methods. In proceedings of Mexican International Conference on Artificial Intelligence. (IBERAMIA) Puebla City, November 22-26, pag. 514-526. KeyElementsforMotionPlanningAlgorithms 173 presented to show GEMPA’s functionalities. Besides, we have presented motion planning problems with different levels of complexity. In Figure 15. (Sample 1) The collision free path is painted as complete option and as animation option. In this sample a tetrahedron is considered as the robot. Next, Figure 16: (Sample 2). A cube is presented as the robot for this motion planning problem. Here, GEMPA presents the flat and wire modes to paint the objects. In Figure 17: (Sample 3). Presents a robot which has a more complex for and the problem becomes difficult to solve because the motion planning method needs to compute free configuration in the narrow corridor. Finally in Figure 18: (Sample 4). Animation painting all configurations (left side), and animation using automatic step (right side) are displayed. Although the robot has not a more complex form, there are various narrow corridors inside the environment. Fig. 15. Sample 1. The robot is presented as a tetrahedron. Fig. 16. Sample 2. The robot is presented as a cube. Fig. 17. Sample 3. The robot’s form is more complex. Fig. 18. Sample 4. More complex environment where various narrow corridors are presented. 6. References Amato, N.; Bayazit, B. ; Dale, L.; Jones, C. &. Vallejo, D. (1998). Choosing good distance metrics and local planer for probabilistic roadmap methods. In in Procc.IEEE Int. Conf. Robot. Autom. (ICRA), pages 630–637. Amato, N.; Bayazit, B. ; Dale, L.; Jones, C. &. Vallejo, D. (1998). Obprm: An obstaclebased prm for 3d workspaces. In in Procc. Int. Workshop on Algorithmic Fundation of Robotics (WAFR), pages 155–168. Amato, N. M. & Wu, Y. (1996). A randomized roadmap method for path and manipulation planning. In In IEEE Int. Conf. Robot. and Autom., pages 113–120. Amato, N. Motion ning puzzels benchmarks. Benitez, A. & Mugarte, A. (2009). GEMPA:Graphic Environment for Motion Planning Algorithm. In Research in Computer Science, Advances in Computer Science and Engineering. Volumen 42. Benitez, A. & Vallejo, D. (2004). New Technique to Improve Probabilistic Roadmap Methods. In proceedings of Mexican International Conference on Artificial Intelligence. (IBERAMIA) Puebla City, November 22-26, pag. 514-526. RobotLocalizationandMapBuilding174 Benitez, A.; Vallejo, D. & Medina, M.A. (2004). Prms based on obstacle’s geometry.In In Proc. IEEE The 8th Conference on Intelligent Autonomous Systems, pages 592–599. Boor, V.; Overmars, N. H. & van der Stappen, A. F. (1999). The gaussian sampling strategy for probabilistic roadmap planners. In In IEEE Int. Conf. Robot. And Autom., pages 1018–1023. Chang, H. & Li, T. Y. (1995). Assembly maintainability study with motion planning. In In Proc. IEEE Int. Conf. on Rob. and Autom., pages 1012–1019. Christoph, M. Hoffmann. Solid modeling. (1997). In Handbook of Discrete and ComputationalGeometry, pages 863,880. In Jacob E. Goodman and Joseph ORourke, editors Press, Boca Raton New York. Goodman, J. & O’Rourke, J. (1997). Handbook of Discrete and Computational Geometry. CRC Press.In Computer Graphics (SIGGRAPH’94)., pages 395–408. Kavraki, L. & Latombe, J.C. (1994). Randomized preprocessing of configuration space for path planning. In IEEE Int. Conf. Robot. and Autom, pages 2138–2145. Kavraki, L. & Latombe, J.C. (1994). Randomized preprocessing of configuration space for fast path planning. In IEEE International Conference on Robotics and Automation, San Diego (USA), pp. 2138-2245. Kavraki, L. E.; Svestka, P.; Latombe, J C. & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. In IEEE Trans. Robot. & Autom, pages 566–580 . Kavraki, L.; Kolountzakis, L. & Latombe, JC. (1996). Analysis of probabilistic roadmaps for path planning. In IEEE International Conference on Robotics and Automation, Minneapolis (USA), pp. 3020-3025. Kavraki, L.E, J.C. Latombe, R. Motwani, & P. Raghavan. (1995). Randomized preprocessing of configuration space for path planning. In Proc. ACM Symp. on Theory of Computing., pages 353–362. Koga, Y.; Kondo, K.; Kuffner, J. & Latombe, J.C. (1994). Planning motions with intentions. Latombe, J.C. (1991). Robot Motion Planning. Kluwer Academic Publishers, Boston, MA. Laumond, J. P. & Siméon, T. (2000). Notes on visibility roadmaps and path planning. In In Proc. Int. Workshop on Algorithmic Foundation of Robotics (WAFR), pages67–77. M. LaValle and J. J. Kuffner. (1999). Randomized kinodynamic planning. In IEEE Int. Conf. Robot. and Autom. (ICRA), pages 473–479. M. LaValle, J.H. Jakey, and L.E. Kavraki. (1999). A probabilistic roadmap approach for systems with closed kinematic chains. In IEEE Int. Conf. Robot. and Autom. Overmars, M. & Svestka, P. (1995). A Probabilistic learning approach to motion Planning. In Algorithmic Foundations of Robotics of (WAFR94), K. Goldberg et al (Eds), pp. 19-37, AK Peters. Overmars, M. & Svestka, P. (1994). A probabilistic learning approach to motion planning. In Proc. Workshop on Algorithmic Foundations of Robotics., pages 19–37. Russell, S. & Norvig, P. (2003). Articial Intelligence: A Modern Approach. Pearson Education, Inc., Upper Saddle River, NJ. Steven, M. LaValle. (2004). Planning Algorithms. Tombropoulos, R.Z.; Adler, J.R. & Latombe, J.C. Carabeamer. (1999). A treatment planner for a robotic radiosurgical system with general kinematics. In Medical Image Analysis, pages 237–264. OptimumBipedTrajectoryPlanningforHumanoidRobotNavigationinUnseenEnvironment 175 OptimumBipedTrajectoryPlanningforHumanoidRobotNavigationin UnseenEnvironment HanaahYussofandMasahiroOhka X Optimum Biped Trajectory Planning for Humanoid Robot Navigation in Unseen Environment Hanafiah Yussof 1,2 and Masahiro Ohka 1 1 Graduate School of Information Science, Nagoya University Japan 2 Faculty of Mechanical Engineering, Universiti Teknologi MARA Malaysia 1. Introduction The study on biped locomotion in humanoid robots has gained great interest since the last decades (Hirai et. al. 1998, Hirukawa et. al., 2004, Ishiguro, 2007). This interest are motivated from the high level of mobility, and the high number of degrees of freedom allow this kind of mobile robot adapt and move upon very unstructured sloped terrain. Eventually, it is more desirable to have robots of human build instead of modifying environment for robots (Khatib et. al, 1999). Therefore, a suitable navigation system is necessary to guide the robot’s locomotion during real-time operation. In fundamental robot navigation studies, robot system is normally provided with a map or a specific geometrical guidance to complete its tasks (Okada et al., 2003, Liu et al., 2002). However during operation in uncertain environment such as in emergency sites like an earthquake site, or even in a room that the robots never been there before, which is eventually become the first experience for them, robots needs some intelligence to recognize and estimate the position and structure of objects around them. The most important is robot must localize its position within this environment and decide suitable action based on the environment conditions. To archives its target tasks, the robot required a highly reliable sensory devices for vision, scanning, and touching to recognize surrounding. These problems have become the main concern in our research that deals with humanoid robot for application in built-for-human environment. Operation in unseen environment or areas where visual information is very limited is a new challenge in robot navigation. So far there was no much achievement to solve robot navigation in such environments. In previous research, we have proposed a contact interaction-based navigation strategy in a biped humanoid robot to operate in unseen environment (Hanafiah et al., 2008). In this chapter, we present analysis results of optimum biped trajectory planning for humanoid robot navigation to minimize possibility of collision during operation in unseen environment. In this analysis, we utilized 21-dof biped humanoid robot Bonten-Maru II. Our aim is to develop reliable walking locomotion in order 9 RobotLocalizationandMapBuilding176 to support the main tasks in the humanoid robot navigation system. Fig. 1 shows diagram of humanoid robot Bonten-Maru II and its configurations of dofs. Fig. 1. Humanoid Robot Bonten-Maru II and its configuration of dofs. It is inevitable that stable walking gait strategy is required to provide efficient and reliable locomotion for biped robots. In the biped locomotion towards application in unseen environment, we identified three basic motions: walk forward and backward directions, side-step to left and right, and yawing movement to change robot’s orientation. In this chapter, at first we analyzed the joint trajectory generation in humanoid robot legs to define efficient gait pattern. We present kinematical solutions and optimum gait trajectory patterns for humanoid robot legs. Next, we performed analysis to define efficient walking gait locomotion by improvement of walking speed and travel distance without reducing reduction-ratio at joint-motor system. This is because sufficient reduction-ratio is required by the motor systems to supply high torque to the robot’s manipulator during performing tasks such as object manipulation and obstacle avoidance. We also present optimum yawing motion strategy for humanoid robot to change its orientation within confined space. The analysis results were verified with simulation and real-time experiment with humanoid robot Bonten-Maru II. Eventually, to safely and effectively navigate robots in unseen environment, the navigation system must feature reliable collision checking method to avoid collisions. In this chapter, we present analyses of collision checking using the robot arms to perform searching, touching and grasping motions in order to recognize its surrounding condition. The collision checking is performed in searching motion of the robot’s arms that created a radius of detection area within the arm’s reach. Based on the searching area coverage of the robot arms, we geometrically analyze the robot biped motions using Rapid-2D CAD software to identify the ideal collision free area. The collision free area is used to calculate maximum biped step-length when no object is detected. Consequently the robot control system created an absolute collision free area for the robot to generate optimum biped trajectories. In case of object is detected during searching motion, the robot arm will touch and grasp the object surface to define self-localization, and consequently optimum step-length is refined. Z Y X Yaw R oll P itch Verification experiments were conducted using humanoid robot Bonten-Maru II to operate in a room with walls and obstacles was conducted. In this experiment, the robot visual sensors are not connected to the system. Therefore the robot locomotion can only rely on contact interaction of the arms that are equipped with force sensors. 2. Short Survey on Humanoid Robot Navigation Operation in unseen environment or areas where visual information is very limited is a new challenge in robot navigation. So far there was no much achievement to solve robot navigation in such environments. In normal conditions, it is obvious that a navigation system that applies non-contact sensors such as vision sensors provides intensive information about the environment (Sagues & Guerrero, 1999). However, robots cannot just rely on this type of sensing information to effectively work and cooperate with humans. For instance, in real applications the robots are likely to be required to operate in areas where vision information is very limited, such as in a dark room or during a rescue mission at an earthquake site (Diaz et. al., 2001). Moreover vision sensors have significant measurement accuracy problems resulting from technical problems such as low camera resolution and the dependence of stereo algorithms on specific image characteristics. Furthermore, the cameras are normally located at considerable distance from objects in the environment where operation takes place, resulting in approximate information of the environment. In addition to the above, a laser range finder has also been applied in a robot navigation system (Thompson et. al., 2006). This sensor is capable of producing precise distance information and provides more accurate measurements compared with the vision sensor. However, it is impractical to embed this type of sensor with its vision analysis system in a walking robot system because of its size and weight (Okada et. al., 2003). A navigation system that applies contact-based sensors is capable of solving the above problems, particularly for a biped walking robot system (Hanafiah et. al., 2007). This type of sensor can accurately gauge the structure of the environment, thus making it suitable to support current navigation systems that utilize non-contact sensors. Furthermore, the system architecture is simpler and can easily be mounted on the walking robot body. Eventually, to safely and effectively navigate robots in unseen environment, the navigation system must feature reliable collision checking method to avoid collisions. To date, in collision checking and prediction research, several methods such as vision based local floor map (Okada et al., 2003, Liu et al., 2002) and cylinder model (Guttmann et al., 2005) have been proposed for efficient collision checking and obstacle recognition in biped walking robot. In addition, Kuffner (Kuffner et al., 2002) have used fast distance determination method for self-collision detection and prevention for humanoid robots. This method is for convex polyhedra in order to conservatively guarantee that the given trajectory is free of self-collision. However, to effectively detect objects based on contact-based sensors, such methods are not suitable because they are mostly based on assumption of environment conditions acquired by non-contact sensors such as vision and laser range sensors. Several achievements have been reported related with navigation in humanoid robots. Ogata have proposed human-robot collaboration based on quasi-symbolic expressions applying humanoid on static platform named Robovie (Ogata et al., 2005). This work combined non-contact and contact sensing approach in collaboration of human and robot during navigation tasks. This is the closest work with the approach used in this research. OptimumBipedTrajectoryPlanningforHumanoidRobotNavigationinUnseenEnvironment 177 to support the main tasks in the humanoid robot navigation system. Fig. 1 shows diagram of humanoid robot Bonten-Maru II and its configurations of dofs. Fig. 1. Humanoid Robot Bonten-Maru II and its configuration of dofs. It is inevitable that stable walking gait strategy is required to provide efficient and reliable locomotion for biped robots. In the biped locomotion towards application in unseen environment, we identified three basic motions: walk forward and backward directions, side-step to left and right, and yawing movement to change robot’s orientation. In this chapter, at first we analyzed the joint trajectory generation in humanoid robot legs to define efficient gait pattern. We present kinematical solutions and optimum gait trajectory patterns for humanoid robot legs. Next, we performed analysis to define efficient walking gait locomotion by improvement of walking speed and travel distance without reducing reduction-ratio at joint-motor system. This is because sufficient reduction-ratio is required by the motor systems to supply high torque to the robot’s manipulator during performing tasks such as object manipulation and obstacle avoidance. We also present optimum yawing motion strategy for humanoid robot to change its orientation within confined space. The analysis results were verified with simulation and real-time experiment with humanoid robot Bonten-Maru II. Eventually, to safely and effectively navigate robots in unseen environment, the navigation system must feature reliable collision checking method to avoid collisions. In this chapter, we present analyses of collision checking using the robot arms to perform searching, touching and grasping motions in order to recognize its surrounding condition. The collision checking is performed in searching motion of the robot’s arms that created a radius of detection area within the arm’s reach. Based on the searching area coverage of the robot arms, we geometrically analyze the robot biped motions using Rapid-2D CAD software to identify the ideal collision free area. The collision free area is used to calculate maximum biped step-length when no object is detected. Consequently the robot control system created an absolute collision free area for the robot to generate optimum biped trajectories. In case of object is detected during searching motion, the robot arm will touch and grasp the object surface to define self-localization, and consequently optimum step-length is refined. Z Y X Yaw R oll P itch Verification experiments were conducted using humanoid robot Bonten-Maru II to operate in a room with walls and obstacles was conducted. In this experiment, the robot visual sensors are not connected to the system. Therefore the robot locomotion can only rely on contact interaction of the arms that are equipped with force sensors. 2. Short Survey on Humanoid Robot Navigation Operation in unseen environment or areas where visual information is very limited is a new challenge in robot navigation. So far there was no much achievement to solve robot navigation in such environments. In normal conditions, it is obvious that a navigation system that applies non-contact sensors such as vision sensors provides intensive information about the environment (Sagues & Guerrero, 1999). However, robots cannot just rely on this type of sensing information to effectively work and cooperate with humans. For instance, in real applications the robots are likely to be required to operate in areas where vision information is very limited, such as in a dark room or during a rescue mission at an earthquake site (Diaz et. al., 2001). Moreover vision sensors have significant measurement accuracy problems resulting from technical problems such as low camera resolution and the dependence of stereo algorithms on specific image characteristics. Furthermore, the cameras are normally located at considerable distance from objects in the environment where operation takes place, resulting in approximate information of the environment. In addition to the above, a laser range finder has also been applied in a robot navigation system (Thompson et. al., 2006). This sensor is capable of producing precise distance information and provides more accurate measurements compared with the vision sensor. However, it is impractical to embed this type of sensor with its vision analysis system in a walking robot system because of its size and weight (Okada et. al., 2003). A navigation system that applies contact-based sensors is capable of solving the above problems, particularly for a biped walking robot system (Hanafiah et. al., 2007). This type of sensor can accurately gauge the structure of the environment, thus making it suitable to support current navigation systems that utilize non-contact sensors. Furthermore, the system architecture is simpler and can easily be mounted on the walking robot body. Eventually, to safely and effectively navigate robots in unseen environment, the navigation system must feature reliable collision checking method to avoid collisions. To date, in collision checking and prediction research, several methods such as vision based local floor map (Okada et al., 2003, Liu et al., 2002) and cylinder model (Guttmann et al., 2005) have been proposed for efficient collision checking and obstacle recognition in biped walking robot. In addition, Kuffner (Kuffner et al., 2002) have used fast distance determination method for self-collision detection and prevention for humanoid robots. This method is for convex polyhedra in order to conservatively guarantee that the given trajectory is free of self-collision. However, to effectively detect objects based on contact-based sensors, such methods are not suitable because they are mostly based on assumption of environment conditions acquired by non-contact sensors such as vision and laser range sensors. Several achievements have been reported related with navigation in humanoid robots. Ogata have proposed human-robot collaboration based on quasi-symbolic expressions applying humanoid on static platform named Robovie (Ogata et al., 2005). This work combined non-contact and contact sensing approach in collaboration of human and robot during navigation tasks. This is the closest work with the approach used in this research. [...]...178 Robot Localization and Map Building However Ogata use humanoid robot without leg On the other hand, related with biped humanoid robot navigation, the most interesting work was presented by Stasse where visual 3D Simultaneous Localization and Mapping (SLAM) was used to navigate HRP-2 humanoid robot performing visual loop-closing motion (Stasse et al., 20 06) In other achievements,... matrices for respectively 1 4 T and 4 h (3) T can be defined as (4) and (5), 1 3 T= 2 T 2 T 4 T 3  s2c 34   s34   c 2c34   0  4 h 1 4 s2s34 c 2  c34 c 2s34 l1s2c3    l1s3   l1c 2c3   1   (4) l2  l3c5c6   l3s5c6   l3s6   1   (5) 0  s2 0 0 5 T= 4 T 6 T 6 T 5 h c5c6  s c  5 6   s6   0  The coordinate transformation matrix c5s6  s5s6  c6 0 for 0 h s5 c5 0 0 T, which... cycle in human Swing 1 86 Robot Localization and Map Building r Side View Front View : GCoM : CoM Fig 5 Static walking model for biped robot in one cycle In contrast, for a biped robot two different situations arise in sequence during the walking motion: the statically stable double-support phase in which the whole structure of the robot is supported on both feet simultaneously, and the statically unstable... biped robot locomotion: 1) Step length; s 2) hip-joint height from the ground; h 3) Duty-ratio; d Figure 6 shows initial orientation of Bonten-Maru II during motion mode which also indicate the step length and hip-joint height of the robot The step-length is the distance between ankle-joints of a support leg and a swing leg when both of them are settled on the 188 Robot Localization and Map Building. .. Finally, the distance of the robot to object L and grasping angle  are shown in (37) and (38), respectively y  ax  b (34) 1 x a (35) y 200 Robot Localization and Map Building C x , C y      L ab b    a 1 a 1 b 2 , 2 a2  1  1  a   tan 1    ( 36) (37) (38) For collision checking, the size of collision free area is refined automatically in the robot control system based on... avoid the obstacle The robot will repeat the whole process again until it reaches to the desired target point In this research, the robot control system is intelligently classified each process so that the robot could effectively perform suitable locomotion according to the surrounding conditions 202 Robot Localization and Map Building 6. 2 Motion Algorithm In the proposed humanoid robot navigation strategy,... 21 Robot orientation after grasping process 5.3 Condition II: When Object Detected In situation where object is detected during searching tasks, the robot arm will grasp the object surface and define self -localization The control system will measure the object orientation and then consequently calculate the robot distance and angle Figure 21 shows the geometrical analysis of the robot position and. .. )u4  6 ( y f  y o )u5 (28) 184 Where, Robot Localization and Map Building u current time t  tf motion time (29) These formulations provide smooth and controlled motion trajectory to the robot s manipulators during performing tasks in the proposed navigation system Consequently, to perform a smooth and reliable gait, it is necessary to define step-length and foot-height during transferring one leg... the pattern 1 and pattern 2 Motion planning for pattern 1 and pattern 2 are shown in Fig 16 For example, in the case of rotating to left side, at first the left leg’s hipjoint yaw will rotate counterclockwise direction to θ1 (see Fig 15) At the same time, the left leg follow along an ellipse trajectory in regard to z-axis direction to move the leg one step 194 Robot Localization and Map Building This... to avoid collision with external objects so that the robot can perform walking motions safely in the navigation tasks In conjunction with this collision free area, a suitable step-size of the robot s foot during walking motion is defined We assumed that no objects were detected in the searching process, so that we can 1 96 Robot Localization and Map Building calculate maximum size of the collision free .                   1000 0 3212342342 313434 3212342342 cclssccc slcs cslcsscs (4) 4 h T= 4 5 T 5 6 T 6 h T                   1000 0 63 66 653 565 65 65 32 565 65 slcs cslcsscs ccllssccc (5) The coordinate transformation.                   1000 0 3212342342 313434 3212342342 cclssccc slcs cslcsscs (4) 4 h T= 4 5 T 5 6 T 6 h T                   1000 0 63 66 653 565 65 65 32 565 65 slcs cslcsscs ccllssccc (5) The coordinate transformation. Algorithmic Fundation of Robotics (WAFR), pages 155– 168 . Amato, N. M. & Wu, Y. (19 96) . A randomized roadmap method for path and manipulation planning. In In IEEE Int. Conf. Robot. and Autom., pages