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Journal Article

Revision on fuzzy artificial potential field for humanoid robot path planning in unknown environment

27 Oct 2015-International Journal of Advanced Mechatronic Systems (Inderscience Publishers (IEL))-Vol. 6, Iss: 5, pp 174-183
TL;DR: Two different approaches for path planning of a humanoid robot in an unknown environment using fuzzy artificial potential (FAP) method are investigated; in the first approach, the direction of the moving robot is derived from fuzzified artificial potential field whereas in the second one, thedirection of the robot is extracted from some linguistic rules that are inspired from Artificial potential field.
Abstract: Path planning in a completely known environment has been experienced various ways. However, in real world, most humanoid robots work in unknown environments. Robots' path planning by artificial potential field and fuzzy artificial potential field methods are very popular in the field of robotics navigation. However, by default humanoid robots lack range sensors; thus, traditional artificial potential field approaches needs to adopt themselves to these limitations. This paper investigates two different approaches for path planning of a humanoid robot in an unknown environment using fuzzy artificial potential (FAP) method. In the first approach, the direction of the moving robot is derived from fuzzified artificial potential field whereas in the second one, the direction of the robot is extracted from some linguistic rules that are inspired from artificial potential field. These two introduced trajectory design approaches are validated though some software and hardware in the loop simulations and the experimental results demonstrate the superiority of the proposed approaches in humanoid robot real-time trajectory planning problems.
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Abstract: Using mobile robots in disaster areas can reduce risks and the search time in urban search and rescue operations. Optimal path-planning for mobile robotics can play a key role in the reduction of the search time for rescuing victims. In order to minimize the search time, the shortest path to the target should be determined. In this paper, a new integrated Reinforcement Learning—based method is proposed to search and find a hidden target in an unknown environment in the minimum time. The proposed algorithm is developed in two main phases. Depending on whether or not the mobile robot receives the signal from the hidden target, phases I or II of the proposed algorithm can be carried out. Then, the proposed algorithm is implemented on an e-puck robot in an urban environment which is simulated within Webots software. Finally, to demonstrate the efficiency of the proposed method and to verify it, the computational results from the proposed method are compared with three conventional methods from the literature.

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Journal ArticleDOI
TL;DR: This work presents and applies energy optimal and artificial potential field to develop a path planning method for six degree of freedom (DOF) serial harvesting robot under dynamic uncertain environment and shows that the proposed path planning algorithm can be used to the harvesting robot.
Abstract: Collision-free autonomous path planning under a dynamic and uncertainty vineyard environment is the most important issue which needs to be resolved firstly in the process of improving robotic harvesting manipulator intelligence. We present and apply energy optimal and artificial potential field to develop a path planning method for six degree of freedom (DOF) serial harvesting robot under dynamic uncertain environment. Firstly, the kinematical model of Six-DOF serial manipulator was constructed by using the Denavit-Hartenberg (D-H) method. The model of obstacles was defined by axis-aligned bounding box, and then the configuration space of harvesting robot was described by combining the obstacles and arm space of robot. Secondly, the harvesting sequence in path planning was computed by energy optimal method, and the anticollision path points were automatically generated based on the artificial potential field and sampling searching method. Finally, to verify and test the proposed path planning algorithm, a virtual test system based on virtual reality was developed. After obtaining the space coordinates of grape picking point and anticollision bounding volume, the path points were drew out by the proposed method. 10 times picking tests for grape anticollision path planning were implemented on the developed simulation system, and the success rate was up to 90%. The results showed that the proposed path planning method can be used to the harvesting robot.

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References
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Proceedings ArticleDOI
12 May 2009
TL;DR: The mechatronic design of the autonomous humanoid robot called NAO that is built by the French company Aldebaran-Robotics is presented, which has been designed to be affordable without sacrificing quality and performance.
Abstract: This article presents the mechatronic design of the autonomous humanoid robot called NAO that is built by the French company Aldebaran-Robotics. With its height of 0.57 m and its weight about 4.5 kg, this innovative robot is lightweight and compact. It distinguishes itself from existing humanoids thanks to its pelvis kinematics design, its proprietary actuation system based on brush DC motors, its electronic, computer and distributed software architectures. This robot has been designed to be affordable without sacrificing quality and performance. It is an open and easy-to-handle platform. The comprehensive and functional design is one of the reasons that helped select NAO to replace the AIBO quadrupeds in the 2008 RoboCup standard league.

532 citations


"Revision on fuzzy artificial potent..." refers methods in this paper

  • ...In order to show the capability and effectiveness of our proposed methods, we applied them to a Nao H25 V4 robot which is produced by Aldebaran Robotics French Company (Gouaillier et al., 2009) (Figure5)....

    [...]

Journal ArticleDOI
01 Sep 1990
TL;DR: It turns out that extensive modifications of simpler tactile algorithms are needed to take full advantage of additional sensing capabilities, and two algorithms that guarantee convergence and exhibit different styles of behavior are described, and their performance is demonstrated in simulated examples.
Abstract: A model of mobile robot navigation is considered in which the robot is a point automaton operating in an environment with unknown obstacles of arbitrary shapes. The robot's input information includes its own and the target-points coordinates as well as local sensing information such as that from stereo vision or a range finder. These algorithmic issues are addressed: (1) Is it possible to combine sensing and planning functions to produce 'active sensing' guided by the needs of planning? (The answer is yes). (2) Can richer sensing (e.g., stereo vision versus tactile) guarantee better performance, that is, resulting in shorter paths? (The general answer is no). A paradigm for combining range data with motion planning is presented. It turns out that extensive modifications of simpler tactile algorithms are needed to take full advantage of additional sensing capabilities. Two algorithms that guarantee convergence and exhibit different styles of behavior are described, and their performance is demonstrated in simulated examples. >

272 citations


"Revision on fuzzy artificial potent..." refers background in this paper

  • ...New bug algorithms such as dist-bug (Kamon and Rivlin, 1997), vis-bug (Lumelsky and Skewis, 1990), tangent-bug (Kamon et al., 1998) and sens-bug (Kim et al., 2003), unlike old ones work with range sensors....

    [...]

  • ...New bug algorithms such as dist-bug (Kamon and Rivlin, 1997), vis-bug (Lumelsky and Skewis, 1990), tangent-bug (Kamon et al....

    [...]

Journal ArticleDOI
01 Dec 1997
TL;DR: DistBug is presented, a new navigation algorithm for mobile robots which exploits range data in a new "leaving condition" which allows the robot to abandon obstacle boundaries as soon as global convergence is guaranteed, based on the free range in the direction of the target.
Abstract: We present DistBug, a new navigation algorithm for mobile robots which exploits range data. The algorithm belongs to the Bug family, which combines local planning with global information that guarantees convergence. Most Bug-type algorithms use contact sensors and consist of two reactive modes of motion: moving toward the target between obstacles and following obstacle boundaries, DistBug uses range data in a new "leaving condition" which allows the robot to abandon obstacle boundaries as soon as global convergence is guaranteed, based on the free range in the direction of the target. The leaving condition is tested directly on the sensor readings, thus making the algorithm simple to implement. To further improve performance, local information is utilized for choosing the boundary following direction, and a search manager is introduced for bounding the search area. The simulation results indicate a significant advantage of DistBug relative to the classical Bug2 algorithm. The algorithm was implemented and tested on a real robot, demonstrating the usefulness and applicability of our approach.

239 citations


"Revision on fuzzy artificial potent..." refers background in this paper

  • ...New bug algorithms such as dist-bug (Kamon and Rivlin, 1997), vis-bug (Lumelsky and Skewis, 1990), tangent-bug (Kamon et al., 1998) and sens-bug (Kim et al., 2003), unlike old ones work with range sensors....

    [...]

  • ...New bug algorithms such as dist-bug (Kamon and Rivlin, 1997), vis-bug (Lumelsky and Skewis, 1990), tangent-bug (Kamon et al....

    [...]

Proceedings Article
01 Jan 2003
TL;DR: An algorithm for planning goal-directed footstep navigation strategies for biped robots through obstacle-filled environ- ments and uneven ground and a simplified, online version of the algorithm running on the H7 hu- manoid robot are presented.
Abstract: We present an algorithm for planning goal-directed footstep navigation strategies for biped robots through obstacle-filled environ- ments and uneven ground. Planning footsteps is more general than most existing navigation methods designed for wheeled robots, since the op- tions of stepping over or upon obstacles are available. Given a height map of the terrain and a discrete set of possible footstep motions, the planner uses an A* search to generate a sequence of footstep locations to reach a given goal state. The planner evaluates footstep locations for viability using a collection of heuristic metrics designed to encode the relative safety, effort required, and overall motion complexity. We show preliminary results of the planner over several simulated terrains, as well as a simplified, online version of the algorithm running on the H7 hu- manoid robot. In the latter case, a stereo vision system is used to sense obstacles in the immediate environment and identify a target goal loca- tion, which is used to update the current optimal footstep sequence to the goal from the robot's present location.

216 citations


"Revision on fuzzy artificial potent..." refers methods in this paper

  • ...Additionally, in another study, best-first search and A* algorithms has been used for foot step path planning on H7 humanoid robot by Chestnutt et al. (2003)....

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Proceedings ArticleDOI
06 Jul 2004
TL;DR: Methods for path planning and obstacle avoidance for the humanoid robot QRIO, allowing the robot to autonomously walk around in a home environment are presented, based on plane extraction from data captured by a stereo-vision system that has been developed specifically forQRIO.
Abstract: This work presents methods for path planning and obstacle avoidance for the humanoid robot QRIO, allowing the robot to autonomously walk around in a home environment. For an autonomous robot, obstacle detection and localization as well as representing them in a map are crucial tasks for the success of the robot. Our approach is based on plane extraction from data captured by a stereo-vision system that has been developed specifically for QRIO. We briefly overview the general software architecture composed of perception, short and long term memory, behavior control, and motion control, and emphasize on our methods for obstacle detection by plane extraction, occupancy grid mapping, and path planning. Experimental results complete the description of our system.

204 citations


"Revision on fuzzy artificial potent..." refers methods in this paper

  • ...Furthermore, Sabe et al. (2004) have presented a method for path planning and obstacle avoidance for the QRIO humanoid robot....

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