Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method

<table class="table-group" id="tab2"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left">Items</td><td class="align_center">Setting points I</td><td class="align_center">Setting points II</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">Time-consuming (ms)</td><td class="align_center">2063</td><td class="align_center">1969</td></tr><tr><td class="align_left">Numbers of path nodes</td><td class="align_center">66</td><td class="align_center">68</td></tr><tr><td class="align_left">Path length (unit grid)</td><td class="align_center">69</td><td class="align_center">84</td></tr><tr><td class="align_left">Turning angle (radian)</td><td class="align_center"><span style="width: 14.0748ptpx;"><svg height="8.55521pt" id="M151" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -8.34882 14.0748 8.55521" width="14.0748pt" 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="M412 140C382 77 369 73 315 73H129L270 222C362 320 402 379 402 466C402 571 322 635 234 635C177 635 130 609 99 576L42 495L64 475C90 514 133 568 201 568C274 568 318 519 318 435C318 349 255 267 193 193C144 135 87 78 32 23V0H405C417 45 427 89 440 131L412 140Z"></path></g><g transform="matrix(.013,0,0,-0.013,6.24,0)"><path d="M574 449L545 460C526 432 516 430 487 430C404 430 311 435 226 435C104 435 56 379 25 341L43 318C81 354 121 372 181 372C161 246 87 53 23 3L30 -12C48 -12 88 -4 113 11C157 75 207 248 232 371L386 367L326 109C321 86 318 66 318 50C318 4 339 -12 366 -12C410 -12 461 21 505 69L492 96C467 79 434 60 418 60C406 60 400 78 411 147C422 217 439 300 457 366C487 366 524 367 536 370C547 385 558 408 574 449Z"></path></g></svg></span></td><td class="align_center"><span style="width: 23.3133ptpx;"><svg height="8.55521pt" id="M152" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -8.34882 23.3133 8.55521" width="23.3133pt" 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="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"></path></g><g transform="matrix(.013,0,0,-0.013,6.24,0)"><path d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z"></path></g><g transform="matrix(.013,0,0,-0.013,9.204,0)"><path d="M153 550H386L412 615L406 623H120L82 318C104 327 142 338 184 338C294 338 347 275 347 187C347 112 305 39 221 39C160 39 119 71 97 89C88 97 80 96 71 90C59 80 50 67 49 57C48 45 52 36 66 23C80 9 123 -12 169 -12C221 -11 288 15 342 59C403 109 431 165 431 225C431 308 366 395 238 395C212 395 165 379 127 364L153 550Z"></path></g><g transform="matrix(.013,0,0,-0.013,15.444,0)"><path d="M574 449L545 460C526 432 516 430 487 430C404 430 311 435 226 435C104 435 56 379 25 341L43 318C81 354 121 372 181 372C161 246 87 53 23 3L30 -12C48 -12 88 -4 113 11C157 75 207 248 232 371L386 367L326 109C321 86 318 66 318 50C318 4 339 -12 366 -12C410 -12 461 21 505 69L492 96C467 79 434 60 418 60C406 60 400 78 411 147C422 217 439 300 457 366C487 366 524 367 536 370C547 385 558 408 574 449Z"></path></g></svg></span></td></tr><tr><td class="align_left">Comprehensive indicator (ST)</td><td class="align_center">9.61</td><td class="align_center">10.2</td></tr><tr><td class="align_left">Robotic exercise time (s)</td><td class="align_center">147</td><td class="align_center">155</td></tr><tr><td class="align_left">Robot moving distance (mm)</td><td class="align_center">13788</td><td class="align_center">16661</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

Computational Intelligence and Neuroscience

tab2

Table 2

Table 2: Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method 