Artificial Potential Field-Based Motion Planning/Navigation, Dynamic Constrained Optimization And Simple Genetic Hill Climbing  

Oleh:

Dozier, Gerry ; Homaifar, Abdollah ; Bryson, Sidney ; Bikdash, Marwan

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Simulation vol. 71 no. 3 (Mar. 1995), page 168-183.
Topik: Motion planning; navigation; genetic algorithms; artificial potential field; constrained optimization; robot; simple genetic hill climbing; steepest descent hill climbing; NASA; AGIE-3
Fulltext: 168.pdf (1.16MB)

Isi artikel

In this paper we show a relationship between artificial potential field (APF) based motion planning/navigation, and constrained optimization. We then present a simple genetic hill climbing algorithm (SGHC), which is used to navigate a point robot through an environment using the APF approach. We compare SGHC with steepest descent hill climbing (SDHC). In SDHC, candidate moves are evaluated within a 360-degree radius and the best candidate is selected by the robot. One would think that SGHC would be at a disadvantage; however, the performance of SGHC is comparable with SDHC. SGHC has an advantage in that it is capable of evolving (learning) the appropriate step size as well as the appropriate angle of movement.

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