GA-based path planning for mobile robot systems employing an active search algorithm

Gemeinder, Marcus; Gerke, Michael

doi:10.1016/s1568-4946(03)00010-3

Cited by 81 publications

(27 citation statements)

References 11 publications

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“…The board then processes the signals received from the potentiometers and finally, converts them into requisite digital pulses that are then sent to the servomotors. This servo will respond with regards to the pulses which results in the moment of the arm [5]. Figure 1 shows the image of a servo motor.…”

Section: Design Of Robotic Armmentioning

confidence: 99%

Design and Operation of Synchronized Robotic Arm

.¹

2013

IJRET

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Section: Design Of Robotic Armmentioning

confidence: 99%

Design and Operation of Synchronized Robotic Arm

.¹

2013

IJRET

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“…Following the standard formulae given by Piazzi splines is we can easily calculate the Cartesian coordinates of the point on the curve via 5 …”

Section: The η 3 -Splinesmentioning

confidence: 99%

“…Approaches based on introducing randomness into the path planner, e.g. PRM [18], RRT [19], adaptive random walk [13], and simple genetic algorithm [1,3,5,9] or parallel genetic algorithm [14], [7], [10], show the effectiveness in searching a feasible path in difficult environments.…”

Section: Introductionmentioning

confidence: 99%

Mobile robot path planning with η<sup>3</sup>-splines using spatial-fitness-sharing variable-length genetic algorithm

Wei

Liu

2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This paper presents a novel evolutionary G 3 -continuous (continuous-differentiable curvature) path planner for nonholonomic wheeled mobile robots. The evolutionary path planner generates intermediate configurations connected via η 3 -splines that yields a collision-free G 3 -continuous path, which each point on the path has a closed-form expression. The path planner is implemented as architecture of island parallel genetic algorithm (IPGA) running a variable-length genetic algorithm in each island. The techniques of spatial fitness-sharing in search space and a crowded measure of generated paths are integrated in the planner to implement a high-diversity evolutionary optimizer of paths that could self-adjust the spacing and number of intermediate configurations. The experimental result demonstrates the robustness and self-adjusting capability of evolutionary path planner in discovering shorter and smoother composite η 3 -splines paths in complex environments.

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“…Not so complicated sensing system is installed. There are various studies on genetic algorithm applications on orienting path planning [11,[15][16][17]. There are multiple types of genetic algorithm, of which different type suits different type of applications.…”

Section: Controlmentioning

confidence: 99%

Autonomous Mobile Robot: Path Planning Using Backward Chaining

Nawi

Rahim

2008

Advances in Electrical and Electronics Engineering - IAENG Special Edition of the World Congress on Engineering and Computer Sc

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This project concerns the design and fabrication of the Autonomous Mobile Robot (AMR) prototype, utilizing backward chaining as a mainframe in helping the robot to generate a self-understanding of the area of work and mobilization to a destination desired by the user. Further explanation of backward chaining is included. The main objective of this project is to create and develop a Path Planning Mobile Robot able to avoid obstacles in its path and reach a target designated position from its starting point utilizing 4 wheel-based rover body, sensors, linear motors and microcontrollers. The paper describes the design process of the AMR development, from the initiation of idea to development of algorithms, to virtual prototype development of AMR mechanisms and controller board, to fabrication, to mobility, obstacle avoidance and path planning test. Two methods of path planning are presented, using compass bearing in undetermined space and predetermined path. Obstacle avoidance and path planning algorithms has been tested using the completed design of AMR body.

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GA-based path planning for mobile robot systems employing an active search algorithm

Cited by 81 publications

References 11 publications

Design and Operation of Synchronized Robotic Arm

Design and Operation of Synchronized Robotic Arm

Mobile robot path planning with η<sup>3</sup>-splines using spatial-fitness-sharing variable-length genetic algorithm

Autonomous Mobile Robot: Path Planning Using Backward Chaining

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GA-based path planning for mobile robot systems employing an active search algorithm

Cited by 81 publications

References 11 publications

Design and Operation of Synchronized Robotic Arm

Design and Operation of Synchronized Robotic Arm

Mobile robot path planning with &#x03B7;<sup>3</sup>-splines using spatial-fitness-sharing variable-length genetic algorithm

Autonomous Mobile Robot: Path Planning Using Backward Chaining

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Mobile robot path planning with η<sup>3</sup>-splines using spatial-fitness-sharing variable-length genetic algorithm