A Gradient-Based Recurrent Neural Network for Visual Servoing of Robot Manipulators with Acceleration Command

<div>Simulation results on the PUMA 560 manipulator driven by the quadratic programming scheme (<a href="https://static.hindawi.com/articles/complexity/volume-2020/2305459/figures/#EEq9">11</a>)–(<a href="https://static.hindawi.com/articles/complexity/volume-2020/2305459/figures/#EEq14">14</a>) aided with GRNN (<a href="https://static.hindawi.com/articles/complexity/volume-2020/2305459/figures/#EEq26">26</a>) for the visual servoing task with execution time <svg height="9.16851pt" id="M129" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -8.96212 29.5984 9.16851" width="29.5984pt" 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="M620 675H597C578 656 570 650 541 650H144C112 650 104 653 94 675H72C59 618 42 552 23 493L53 491C71 534 88 564 105 585C124 608 144 615 238 615H290L197 121C182 40 174 34 88 28L82 0H361L367 28C275 34 266 38 281 121L374 615H441C522 615 543 608 553 583C562 560 566 531 565 493L597 494C603 551 612 629 620 675Z"></path></g><g transform="matrix(.013,0,0,-0.013,11.917,0)"><path d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z"></path></g><g transform="matrix(.013,0,0,-0.013,23.18,0)"><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></svg>s. (a) Actual path and desired point in image space. (b) Time history of 2-norm of error <span class="nowrap"><svg height="6.1673pt" id="M130" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -5.96091 5.44961 6.1673" width="5.44961pt" 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="M387 375C387 402 357 448 257 448C172 448 82 404 82 326C82 289 108 255 156 241V239C85 223 23 181 23 116C23 39 89 -12 182 -12C265 -12 336 31 378 91L361 114C320 73 269 47 216 47C157 47 115 82 115 137C115 191 160 219 218 219C243 219 262 218 272 217L304 259L302 266C295 265 281 264 255 264C195 264 163 294 163 335C163 377 200 416 249 416C293 416 321 389 329 342C331 332 335 329 341 329C355 329 387 352 387 375Z"></path></g></svg>.</span> (c) Time history of 2-norm of error <span class="nowrap"><svg height="8.95973pt" id="M131" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -8.75334 6.159 8.95973 " width="6.159pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,6.159,-.222)"><path d="M-161 571C-161 600 -183 623 -212 623C-238 623 -262 600 -262 571C-262 544 -238 520 -212 520C-183 520 -161 544 -161 571Z"></path></g><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M387 375C387 402 357 448 257 448C172 448 82 404 82 326C82 289 108 255 156 241V239C85 223 23 181 23 116C23 39 89 -12 182 -12C265 -12 336 31 378 91L361 114C320 73 269 47 216 47C157 47 115 82 115 137C115 191 160 219 218 219C243 219 262 218 272 217L304 259L302 266C295 265 281 264 255 264C195 264 163 294 163 335C163 377 200 416 249 416C293 416 321 389 329 342C331 332 335 329 341 329C355 329 387 352 387 375Z"></path></g></svg>.</span> (d) Time history of joint acceleration. (e) Time history of joint velocity. (f) Time history of joint angle.</div>

Complexity

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Figure 3

Figure 3: A Gradient-Based Recurrent Neural Network for Visual Servoing of Robot Manipulators with Acceleration Command 