LiAS: a reflexive navigation architecture for an intelligent mobile robot system

A platform of the Internet-based teleoperation system with an omni-directional mobile robot which has a five DOFs robot arm is constructed. Remote control of the robot through the Internet is implemented. The system is featured as low-cost and user interface friendly: remote users can control the robot through the Internet just by a client program in a general computer. The client computer can receive the live video and environment information measured by sensors. With the help of the remote video and the local simulation, users can easily communicate with the robot. Different modules are proposed and the implementation method of the system is presented. Related experiments are conducted to test the validity of the proposed system.

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“…So far, other intelligence modules, like path planning and localization [7][8][9][10] have been realized offline.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Teleoperation System of the Internet-based Omnidirectional Mobile Robot with A Mounted Manipulator

Wang

Zhao

et al. 2007

2007 International Conference on Mechatronics and Automation

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“…Fig. 1 shows a schematic diagram of Leuven Intelligent Autonomous System (LIAS), a mobile robot that uses several sensory modules [53], [54].…”

Section: Figmentioning

confidence: 99%

“…Several subsumption architectures use potential fields for obstacleavoidance at level 0 [33], [49]. Others use rule-based expert systems and fuzzy logic [41], [53], [54]. Neural networks and genetic algorithms have also been tried [46].…”

Section: A Behavior-based Architecturesmentioning

confidence: 99%

Sensor Fusion for Mobile Robot Navigation

1997

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We review techniques for sensor fusion in robot navigation, emphasizing algorithms for self-location. These find use when the sensor suite of a mobile robot comprises several different sensors, some complementary and some redundant. Integrating the sensor readings, the robot seeks to accomplish tasks such as constructing a map of its environment, locating itself in that map, and recognizing objects that should be avoided or sought. Our review describes integration techniques in two categories: low-level fusion is used for direct integration of sensory data, resulting in parameter and state estimates; high-level fusion is used for indirect integration of sensory data in hierarchical architectures, through command arbitration and integration of control signals suggested by different modules. The review provides an arsenal of tools for addressing this (rather ill-posed) problem in machine intelligence, including Kalman filtering, rule-based techniques, behavior based algorithms, and approaches that borrow from information theory, Dempster-Shafer reasoning, fuzzy logic and neural networks. It points to several further-research needs, including: robustness of decision rules; simultaneous consideration of self-location, motion planning, motion control and vehicle dynamics; the effect of sensor placement and attention focusing on sensor fusion; and adaptation of techniques from biological sensor fusion.

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“…Another kind of motion planning algorithm deals with navigation of a robot in a completely unknown environment (Baumagartner and Skaar, 1994;Figueroa and Mahajan, 1994;Tsugawa, 1994;Wu, 1995;Borenstein and Feng, 1996;Vandorpe et al, 1996;Li et al, 1997). Since the environment is unknown to the mobile robot, different sensors such as computer vision, ultrasonics, and odometers have been used in these algorithms, and each of these algorithms has shown its feasibility in different application areas.…”

Section: Introductionmentioning

confidence: 99%

A potential field method for robot motion planning in unknown environments

Lai

Shiue³

2007

Journal of the Chinese Institute of Engineers

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Based on a potential field function, a method is proposed to navigate a mobile robot from a given initial configuration to a desired final configuration in an unknown environment filled with obstacles. To determine its configuration accurately, the robot is equipped with an electronic compass and two optical encoders for deadreckoning, an ultrasonic module for self-localization, and a time-of-flight (TOF) laser range finder for environment recognition. From the readings of sensors at every sampling instant, the proposed method will determine the heading direction of the robot. Then the robot is driven to an intermediate configuration along the heading direction. The navigation procedure will be iterated until a collision-free path between the initial and the final configurations is found. To show the feasibility and validity of the proposed method, simulation and experimental results are included for illustration.

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LiAS: a reflexive navigation architecture for an intelligent mobile robot system

Cited by 23 publications

References 11 publications

Teleoperation System of the Internet-based Omnidirectional Mobile Robot with A Mounted Manipulator

Teleoperation System of the Internet-based Omnidirectional Mobile Robot with A Mounted Manipulator

Sensor Fusion for Mobile Robot Navigation

A potential field method for robot motion planning in unknown environments

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