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Related Concept Videos

Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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Problem-Solving01:29

Problem-Solving

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Effective problem-solving consists of two steps: 1. identifying the problem and 2. selecting the appropriate problem-solving strategy (i.e., a plan of action used to find a solution). Humans use four problem-solving strategies:
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The Availability Heuristic01:08

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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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Trial and Error and Algorithm01:12

Trial and Error and Algorithm

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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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A Safe Heuristic Path-Planning Method Based on a Search Strategy.

Xiaozhen Yan1, Xinyue Zhou1, Qinghua Luo1,2

  • 1School of Information Science and Engineering, Harbin Institute of Technology at WeiHai, No. 2 Wenhua West Road, Weihai 264209, China.

Sensors (Basel, Switzerland)
|January 11, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel heuristic path-planning method for robots, enhancing safety and efficiency. The new approach significantly improves path smoothness and reduces execution time and length, conserving robot battery power.

Keywords:
collision-free safetyheuristicsmobile robotoptimal boundarypath planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Robot path planning in industrial settings is challenged by the need for safe, collision-free, and smooth trajectories.
  • Existing algorithms often struggle to optimize for multiple criteria simultaneously, leading to inefficient or risky paths.

Purpose of the Study:

  • To develop a safe heuristic path-planning method that generates collision-free, smooth robot paths with minimal inflection points.
  • To improve upon traditional path-planning algorithms like A-star in terms of path length, execution time, and smoothness.

Main Methods:

  • A novel search strategy that expands search nodes and calculates node states (normal or dangerous).
  • Incorporation of a 'danger coefficient' to select lower-risk paths.
  • Integration of environmental facilities to determine optimal boundaries for path generation.

Main Results:

  • Reduced path length by 2.89% compared to the A-star algorithm.
  • Decreased execution time by 13.98%.
  • Enhanced path smoothness by 93.17%, leading to more reliable robot navigation.

Conclusions:

  • The proposed heuristic path-planning method offers significant improvements in safety, efficiency, and smoothness over traditional algorithms.
  • The optimized paths reduce power consumption, extending robot operational time and battery life.
  • This method enables more secure and reliable task completion for robots in industrial production.