Key Elements for Motion Planning Algorithms

Benitez, Antonio; Huitzil, Ignacio; Vallejo, Daniel; Calleja, Jorge de la; Medina, Ma. Auxilio

doi:10.5772/9264

Cited by 2 publications

(9 citation statements)

References 15 publications

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“…The problem of motion planning can be identified as: Given a start pose of the robot, a desired goal pose, a geometric description of the robot and a geometric description of the world, the objective is to find a path that moves the robot gradually from start to goal while never touching any obstacle [1,2].…”

Section: Motion Planningmentioning

confidence: 99%

A Simulated Motion Planning Algorithm in 2d And 3d Environments Using Hill Climbing

El-Aawar¹,

Bakri²

2014

IJAIA

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This paper describes a computer simulated artificial intelligence (AI) agent moving in 2D and 3D environments. In the presented algorithm, the agent can take two operating modes: Manual Mode and Map or Autopilot mode. The user can control the agent fully in a manual mode by moving it in all possible directions depending on the environment. Obstacles are sensed by the agent from a certain distance and are avoided accordingly. Another important mode is the Map mode. In this mode the user create a custom map where initial position and a goal position are set. The user is able also to assign sudden and predefined obstacles. By finding the shortest path, the agent moves to the goal position avoiding any obstacles on its path. The paper documents a set of algorithms that can help the agent to find the shortest path to a predefined target location in a complex 3D environment, such as cities and mountains, avoiding all predefined and sudden obstacles. These obstacles are avoided also in manual mode and the agent moves automatically to a safe location. The implementation is based on the Hill Climbing algorithm (descent version), where the agent finds its path to the global minimum (target goal). The Map generation algorithm, which is used to assign costs to every location in the map, avoids a lot of the limitations of Hill Climbing.

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Section: Motion Planningmentioning

confidence: 99%

A Simulated Motion Planning Algorithm in 2d And 3d Environments Using Hill Climbing

El-Aawar¹,

Bakri²

2014

IJAIA

View full text Add to dashboard Cite

show abstract

“…As an abstracted version that aims to solve the problem, a robot can be represented as a point in space taking translational coordinates (x, y, z). Normally in 3D workspace, it is a recurring theme to use six parameters: (x, y, z) for locating the position of the robot and (α, β, γ) for its rotation at every point [2].…”

Section: Robot's (Agent's) Workpace (Environment)mentioning

confidence: 99%

“…The basic motion planning problem is stated as: Given a start pose of the robot, a desired goal pose, a geometric description of the robot and a geometric description of the world, the objective is to find a path that moves the robot gradually from start to goal while never touching any obstacle [1,2].…”

Section: Motion Planningmentioning

confidence: 99%

“…Planning as a sub discipline in the artificial intelligence field has been an area of research for over three decades. Throughout the history of research in this domain, the distinction between planning and problem solving has been indefinable [1,2].…”

Section: Ai Planningmentioning

confidence: 99%

“…These types of discrete models can still be modeled using continuous spaces but it seems more convenient to define them as finite set of actions applied to a discrete set of states. Many decision-theoretic ideas have recently been incorporated into the AI planning problem, to model uncertainties, adversarial scenarios, and optimization [1,2,11,12].…”

Section: Ai Planningmentioning

confidence: 99%

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