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 Motivations and Goals   Become familiar with grid-based path planning    Understand the basic steps of mobile robot navigation   Be able to implement simple path planning algorithms and deploy them in robot navigation</description>
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 Motivations and Goals   Learn the principle of the occupancy grid map construction   Task t1d-map - Implement function for updating the occupancy grid map using sensory data  
Occupancy Grid Maps

Occupancy grid maps represent an example of environment representation in probabilistic robotics, which addresses the problem of generating maps from noisy and uncertain sensor measurement data, assuming that the robot pose is known. $p( m \vert z_{1:t}, x_{1:t} )$$m$$z_{1:t}$$1$$t$$x_{1:…</description>
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 Motivations and Goals   Become familiar with direct sensory-motor control of the robot   Become familiar with collision avoidance and be aware of robot sensors   Be able to implement reactive-based control of mobile robots $m$$m$$m$</description>
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 Motivations and Goals   Become familiar with the CoppeliaSim/V-REP robotic simulator   Be able to control hexapod walking robot  Familiarize with a simple “nature-inspired”$(x,y)$$\phi$$(x,y,\phi)$$(x_{goal}, y_{goal})$</description>
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Introduction to CoppeliaSim/V-REP and Open-Loop Robot Locomotion ControlExteroceptive sensing and Reactive-based Obstacle AvoidanceGrid and Graph-based Path PlanningMap Building</description>
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