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Vector Functions and Motion: Problem Solving01:30

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...
Indirect Motor Pathways01:22

Indirect Motor Pathways

The indirect motor or extrapyramidal pathways originate in the brainstem, the lower portion of the brain that connects it to the spinal cord. They consist of several distinct tracts, each with specialized functions. The four main tracts of the indirect motor pathways are the vestibulospinal tract, the reticulospinal tract, the tectospinal tract, and the rubrospinal tract.
The vestibulospinal tract originates in the vestibular nuclei of the brainstem. The vestibular system detects changes in...
Direct Motor Pathways01:11

Direct Motor Pathways

The direct motor pathways, also known as the pyramidal tracts, are a group of neural pathways that originate in the brain and descend through the spinal cord. They control the voluntary movement of the body. There are two major direct motor pathways: the corticospinal and the corticobulbar tracts.
The corticospinal tract is responsible for the voluntary movement of the limbs and trunk. It originates in the cerebral cortex of the brain and descends through the cerebrum's internal capsule and the...
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...

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Related Experiment Video

Updated: Jul 7, 2026

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents
04:41

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents

Published on: December 2, 2022

A counterexample to the Alexopoulos-Griffin path planning algorithm.

R A Conn1, J Elenes, M Kam

  • 1Data Fusion Lab., Drexel Univ., Philadelphia, PA.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 1, 1997
PubMed
Summary

The V*GRAPH algorithm for path planning with polygonal obstacles is neither global nor optimal, contrary to its claims. This study demonstrates its limitations through counter-examples, impacting robotic navigation research.

More Related Videos

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Related Experiment Videos

Last Updated: Jul 7, 2026

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents
04:41

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents

Published on: December 2, 2022

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

  • Robotics
  • Computational Geometry
  • Artificial Intelligence

Background:

  • The VGRAPH algorithm provides a complete and optimal solution for planar stationary-obstacle path planning.
  • Existing VGRAPH variants achieve O(mu(2)) computation time for path planning.
  • The V*GRAPH algorithm was recently proposed, claiming optimal path planning solutions.

Purpose of the Study:

  • To evaluate the V*GRAPH algorithm's claims of global optimality.
  • To identify and demonstrate the limitations of the V*GRAPH algorithm.

Main Methods:

  • Analysis of the V*GRAPH algorithm's path planning methodology.
  • Development of counter-examples to disprove V*GRAPH's optimality and global properties.

Main Results:

  • Demonstration that V*GRAPH fails to provide a global path.
  • Proof that V*GRAPH does not guarantee an optimal path.
  • Identification of specific scenarios where V*GRAPH produces suboptimal or no paths.

Conclusions:

  • The V*GRAPH algorithm is not a globally optimal solution for the stationary-obstacle path-planning problem.
  • The findings necessitate a re-evaluation of V*GRAPH for practical applications in robotics and autonomous systems.
  • Further research is needed to develop or refine algorithms that guarantee global optimality in path planning.