3D X-Y-T Space Path Planning for Autonomous Mobile Robots Considering Dynamic Constraints

2014 ◽  
Vol 490-491 ◽  
pp. 1163-1167 ◽  
Author(s):  
Ippei Nishitani ◽  
Tetsuya Matsumura ◽  
Mayumi Ozawa ◽  
Ayanori Yorozu ◽  
Masaki Takahashi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Personal Space ◽  
Free Motion ◽  
Autonomous Mobile Robot ◽  
Dynamic Constraints ◽  
Living Space ◽  
Look Ahead ◽  
Laser Range Finders

An autonomous mobile robot in a human living space should be able to not only realize collision-free motion but also give way to humans depending on the situation. Although various reactive obstacle avoidance methods have been proposed, it is difficult to achieve such motion. On the other hand, 3D X-Y-T space path planning, which takes into account the motion of both the robot and the human in a look-ahead time horizon, is effective. This paper proposes a real-time obstacle avoidance method for an autonomous mobile robot that considers the robots dynamic constraints, the personal space, and human directional area based on grid-based 3D X-Y-T space path planning. The proposed method generates collision-free motion in which the robot can yield to humans. To verify the effectiveness of the proposed method, various experiments in which the humans position and velocity were estimated using laser range finders were carried out.

Design and Fabrication of an Autonomous Mobile Robot for Obstacle Avoidance and Path Planning in Partially Known Environment

2011 ◽  
Vol 403-408 ◽  
pp. 3917-3924
Author(s):  
Deep Sharma ◽  
S. K. Dwivedy
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Low Cost ◽  
Dc Motor ◽  
Outdoor Environment ◽  
Autonomous Mobile Robot ◽  
Solar Panels ◽  
Dc Motors ◽  
Servo Controller

In this paper, an autonomous mobile robot has been designed and fabricated which can be used in both indoor and outdoor for industrial and household applications. Here using six servo motors and four DC motors with their controllers (servo controller and L293D DC Motor controller) the mobile robot can pick any object from its workspace and by avoiding collision it can place the object in the desired location. ASCII ultrasonic sensor and motion sensor are used along with ATmega 2560 microcontroller which is programmed to take the sensors output as its input and controls the dc motor and servo motors to pick and place objects and avoid obstacle during motion of the mobile robot. Here low-cost solar panels have been used to recharge the Li-ion batteries used for the motors and microcontroller in case of outdoor environment. The obstacle avoidance and path planning algorithms have been developed and a case study has been presented in this paper.

scholarly journals Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110264
Author(s):  
Jiqing Chen ◽  
Chenzhi Tan ◽  
Rongxian Mo ◽  
Hongdu Zhang ◽  
Ganwei Cai ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Path Length ◽  
Search Time ◽  
Experimental Results ◽  
Artificial Potential Field ◽  
A Algorithm ◽  
Neighbor Search

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

scholarly journals Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

2020 ◽  
Vol 89 ◽  
pp. 106076 ◽  
Author(s):  
Fatin H. Ajeil ◽  
Ibraheem Kasim Ibraheem ◽  
Mouayad A. Sahib ◽  
Amjad J. Humaidi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Autonomous Mobile Robot ◽  
Multi Objective

Research on path planning technology of mobile robot based on visual obstacle avoidance

2021 ◽  
Author(s):  
Zai Luo ◽  
Yiwen Chen ◽  
Wensong Jiang ◽  
Xiaofeng Hu ◽  
Li Yang ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance

scholarly journals Minimum control effort–based path planning and nonlinear guidance for autonomous mobile robots

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881263 ◽  
Author(s):  
Paul Quillen ◽  
Kamesh Subbarao
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Predictive Control ◽  
Autonomous Mobile Robots ◽  
Autonomous Mobile Robot ◽  
Control Effort ◽  
Control Approach ◽  
Navigation Function ◽  
Guidance Law ◽  
High Level

This article puts forth a framework using model-based techniques for path planning and guidance for an autonomous mobile robot in a constrained environment. The path plan is synthesized using a numerical navigation function algorithm that will form its potential contour levels based on the “minimum control effort.” Then, an improved nonlinear model predictive control approach is employed to generate high-level guidance commands for the mobile robot to track a trajectory fitted along the planned path leading to the goal. A backstepping-like nonlinear guidance law is also implemented for comparison with the NMPC formulation. Finally, the performance of the resulting framework using both nonlinear guidance techniques is verified in simulation where the environment is constrained by the presence of static obstacles.

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