Cubic Spline Interpolation-Based Robot Path Planning Using a Chaotic Adaptive Particle Swarm Optimization Algorithm

<div>(a) The variation diagram of <svg height="6.1673pt" id="M57" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -5.96091 8.42472 6.1673" width="8.42472pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M615 290C615 392 563 448 529 448C511 448 491 441 484 434L483 425C518 400 546 349 546 273C546 158 504 46 417 46C381 46 356 77 356 142C356 184 380 293 392 334L387 340L307 323C305 288 296 228 287 188C265 94 221 52 180 52C144 52 112 89 112 171C112 278 164 372 241 430L226 454C106 394 23 288 23 155C23 40 79 -12 139 -12C184 -12 245 32 285 85C292 31 322 -12 376 -12C423 -12 470 18 501 45C570 104 615 184 615 290Z"></path></g></svg> in algorithm operation. (b) The variation diagram of <i>C</i><sub>1</sub> and <i>C</i><sub>2</sub> in algorithm operation.</div>

Mathematical Problems in Engineering

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Figure 3

Figure 3: Cubic Spline Interpolation-Based Robot Path Planning Using a Chaotic Adaptive Particle Swarm Optimization Algorithm 