An Overview of Autonomous Mobile Robot Path Planning Algorithms

Sariff, Nohaidda Binti; Buniyamin, Norlida

doi:10.1109/scored.2006.4339335

Cited by 171 publications

(82 citation statements)

References 15 publications

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“…Sariff and Buniyamin [4] explained the various approaches and problems related to robot path planning and navigation. They compare the various algorithms for robot path planning, such as the A* algorithm, potential field based algorithms, and genetic algorithms.…”

Section: Literature Surveymentioning

confidence: 99%

Motion Planning for a Chain of Mobile Robots Using A* and Potential Field

2018

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Traditionally, motion planning involved navigating one robot from source to goal for accomplishing a task. Now, tasks mostly require movement of a team of robots to the goal site, requiring a chain of robots to reach the desired goal. While numerous efforts are made in the literature for solving the problems of motion planning of a single robot and collective robot navigation in isolation, this paper fuses the two paradigms to let a chain of robot navigate. Further, this paper uses SLAM to first make a static map using a high-end robot, over which the physical low-sensing robots run. Deliberative Planning uses A* algorithm to plan the path. Reactive planning uses the Potential Field Approach to avoid obstacles and stay as close to the initial path planned as possible. These two algorithms are then merged to provide an algorithm that allows the robot to reach its goal via the shortest path possible while avoiding obstacles. The algorithm is further extended to multiple robots so that one robot is followed by the next robot and so on, thus forming a chain. In order to maintain the robots in a chain form, the Elastic Strip model is used. The algorithm proposed successfully executes the above stated when tested on Amigobot robots in an office environment using a map made by the Pioneer LX robot. The proposed algorithm works well for moving a group of robots in a chain in a mapped environment.

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Section: Literature Surveymentioning

confidence: 99%

Motion Planning for a Chain of Mobile Robots Using A* and Potential Field

2018

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“…For all the applications, path planning plays an important role in navigating robot to execute missions 5,6 . Further, it is generally occurred that more than one accessible path can be found, thus a strategy is necessary for selecting the optimal or near-optimal path.…”

Section: Introductionmentioning

confidence: 99%

Optimal Robot Path Planning for Multiple Goals Visiting Based on Tailored Genetic Algorithm

Liu¹,

Liang²,

Xian³

2014

IJCIS

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In real applications, mobile robot may be commanded to go to multiple goals to execute special commissions. This study analyzes the particular properties of this multiple goals visiting task and proposes a novel tailored genetic algorithm for optimal path planning for this task. In proposed algorithm, objectives for evaluating the path are energy consumption and idle time that are proposed in our previous work. Under the constraint of energy consumption, it will generate an optimal path that comprises as more goals as possible and as less idle time as possible. In this algorithm, customized chromosome representing a path and genetic operators including Repair, Cut and Deletion are developed and implemented. Afterwards, simulations are carried out to verify the effectiveness and applicability. Finally, analysis of simulation results is conducted and future work is addressed.

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“…Navigation is understood as a process or activity to plan and direct a trajectory, a route or a path for a mobile robot. It is an activity that an autonomous robot performs with relative precision according to the task in order to move safely from one location to another location without getting lost or colliding with other objects [23]. Regarding the precision requirement in trajectory planning, [5] says that Cartesian space trajectory planning is commonly used because it is a straightforward and relatively simple concept.…”

Section: Introductionmentioning

confidence: 99%

Data Structures in Robot Navigation Optimized by Adaptive Straightness

ClaroBarrosNeto¹,

Hirakawa²

2013

IJCA

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Utilizing adaptive finite automaton (AFA) such as motion automaton, we propose an alternative for the available researches on data structures in robotics navigation, in which trajectories are made up of straight line segments. Software is modeled by a set of rules as systems of state machines to cover the complete space environment of the robot. The formalism of adaptive digitized straight line segments (ADSLS) is applied for data representation, aiming to exploit its ability to express tolerances, scalability, errors and deviations in angle or in length of segments. Consequently, ADSLS is shown by simulations to be effective to represent the complexities of real world scenarios of a robot; furthermore, it is able to adapt, reacting to circumstance stimuli in a single pass, also presenting learning capability.

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An Overview of Autonomous Mobile Robot Path Planning Algorithms

Cited by 171 publications

References 15 publications

Motion Planning for a Chain of Mobile Robots Using A* and Potential Field

Motion Planning for a Chain of Mobile Robots Using A* and Potential Field

Optimal Robot Path Planning for Multiple Goals Visiting Based on Tailored Genetic Algorithm

Data Structures in Robot Navigation Optimized by Adaptive Straightness

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