{"id":54022,"date":"2025-08-12T20:49:47","date_gmt":"2025-08-12T12:49:47","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/54022.html"},"modified":"2025-08-12T20:49:47","modified_gmt":"2025-08-12T12:49:47","slug":"robotics_py-docs-%e6%9c%ba%e5%99%a8%e4%ba%ba%e7%8a%b6%e6%80%81-%e4%bd%8d%e5%a7%bf-%e7%8e%af%e5%a2%83%e8%a1%a8%e7%a4%ba_%e6%a0%85%e6%a0%bc%e5%9c%b0%e5%9b%be","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/54022.html","title":{"rendered":"[Robotics_py] docs | \u673a\u5668\u4eba\u72b6\u6001\/\u4f4d\u59ff | \u73af\u5883\u8868\u793a_\u6805\u683c\u5730\u56fe"},"content":{"rendered":"

\u94fe\u63a5：https:\/\/atsushisakai.github.io\/PythonRobotics\/modules\/0_getting_started\/0_getting_started.html# \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

docs：PythonRobotics<\/h2>\n

PythonRobotics \u662f\u4e00\u4e2a\u7efc\u5408\u6027\u7684\u673a\u5668\u4eba\u7b97\u6cd5\u5b9e\u73b0\u9879\u76ee，\u6f14\u793a\u4e86\u673a\u5668\u4eba\u5982\u4f55\u901a\u8fc7\u6805\u683c\u5730\u56fe\u7b49\u8868\u5f81\u65b9\u5f0f\u611f\u77e5\u73af\u5883，\u501f\u52a9\u5b9a\u4f4d\u6ee4\u6ce2\u5668\u7cbe\u786e\u4f30\u8ba1\u81ea\u8eab\u72b6\u6001\/\u4f4d\u59ff，\u5229\u7528\u8fd0\u52a8\u6a21\u578b\u9884\u6d4b\u79fb\u52a8\u8f68\u8ff9\u3002<\/p>\n

\u5176\u6838\u5fc3\u5c55\u793a\u4e86\u673a\u5668\u4eba\u5982\u4f55\u901a\u8fc7\u57fa\u4e8e\u641c\u7d22\u8282\u70b9\u7684\u591a\u6837\u5316\u8def\u5f84\u89c4\u5212\u7b97\u6cd5\u5236\u5b9a\u76ee\u6807\u8def\u7ebf，\u5e76\u901a\u8fc7\u8def\u5f84\u8ddf\u8e2a\u63a7\u5236\u5668\u751f\u6210\u63a7\u5236\u6307\u4ee4\u7cbe\u51c6\u6267\u884c\u89c4\u5212\u65b9\u6848\u3002<\/p>\n

\u67b6\u6784\u603b\u89c8<\/h3>\n

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u7ae0\u8282\u5bfc\u822a<\/h3>\n
  • \u673a\u5668\u4eba\u72b6\u6001\/\u4f4d\u59ff<\/li>\n
  • \u73af\u5883\u8868\u793a（\u6805\u683c\u5730\u56fe）<\/li>\n
  • \u673a\u5668\u4eba\u8fd0\u52a8\u6a21\u578b<\/li>\n
  • \u5b9a\u4f4d\u6ee4\u6ce2\u5668<\/li>\n
  • \u8def\u5f84\u89c4\u5212\u7b97\u6cd5<\/li>\n
  • \u641c\u7d22\u8282\u70b9（\u8def\u5f84\u89c4\u5212\u7528）<\/li>\n
  • \u8def\u5f84\u8ddf\u8e2a\u63a7\u5236\u5668<\/li>\n
    \n

    \u7b2c\u4e00\u7ae0：\u673a\u5668\u4eba\u72b6\u6001\/\u4f4d\u59ff<\/h2>\n

    \u6b22\u8fce\u8fdb\u5165PythonRobotics\u7cfb\u5217！<\/p>\n

    \u672c\u7ae0\u6211\u4eec\u5c06\u6df1\u5165\u63a2\u8ba8\u673a\u5668\u4eba\u5b66\u4e2d\u6700\u57fa\u7840\u7684\u6982\u5ff5\u2014\u2014\u673a\u5668\u4eba\u72b6\u6001（State）\u6216**\u4f4d\u59ff**（Pose）\u3002<\/p>\n

    \u8fd9\u76f8\u5f53\u4e8e\u673a\u5668\u4eba\u7684"\u8eab\u4efd\u8bc1"\u548c"\u5b9e\u65f6\u72b6\u6001\u7b80\u62a5"，\u7cbe\u786e\u8bb0\u5f55\u7740\u673a\u5668\u4eba\u7684\u7a7a\u95f4\u4f4d\u7f6e\u4e0e\u8fd0\u52a8\u7279\u6027\u3002<\/p>\n

    \u673a\u5668\u4eba\u72b6\u6001\u4e3a\u4f55\u5982\u6b64\u91cd\u8981？<\/h4>\n

    \u60f3\u8c61\u5bb6\u7528\u626b\u5730\u673a\u5668\u4eba\u6267\u884c\u6e05\u6d01\u4efb\u52a1\u65f6，\u9700\u8981\u5b9e\u65f6\u638c\u63e1\u4ee5\u4e0b\u4fe1\u606f：<\/p>\n

      \n
    • \u5df2\u6e05\u6d01\u533a\u57df\u5b9a\u4f4d（X\/Y\u5750\u6807）<\/li>\n
    • \u884c\u8fdb\u65b9\u5411\u5224\u65ad（\u504f\u822a\u89d2）<\/li>\n
    • \u79fb\u52a8\u901f\u5ea6\u8c03\u8282（\u901f\u5ea6\u53c2\u6570）<\/li>\n<\/ul>\n

      \u8fd9\u4e9b\u53c2\u6570\u6784\u6210\u7684\u5b8c\u6574\u72b6\u6001\u63cf\u8ff0，\u662f\u673a\u5668\u4eba\u6267\u884c\u8def\u5f84\u89c4\u5212\u3001\u907f\u969c\u7b49\u9ad8\u7ea7\u529f\u80fd\u7684\u57fa\u77f3\u3002\u82e5\u7f3a\u4e4f\u72b6\u6001\u611f\u77e5，\u673a\u5668\u4eba\u5c06\u5982\u540c\u76f2\u4eba\u5931\u53bb\u7a7a\u95f4\u8ba4\u77e5\u80fd\u529b<\/p>\n

      \u72b6\u6001\u53c2\u6570\u7ec4\u6210\u89e3\u6790<\/h4>\n

      \u9488\u5bf9\u5730\u9762\u79fb\u52a8\u673a\u5668\u4eba（\u5982\u8f66\u8f86\u3001\u65e0\u4eba\u673a），\u5178\u578b\u72b6\u6001\u5305\u542b\u56db\u4e2a\u6838\u5fc3\u7ef4\u5ea6：<\/p>\n\n\u53c2\u6570\u7269\u7406\u610f\u4e49\u751f\u6d3b\u7c7b\u6bd4<\/tr>\n\n\n\n\n\n
      X\u5750\u6807<\/td>\n\u4e1c\u897f\u8f74\u5411\u4f4d\u7f6e（\u7c73）<\/td>\n\u8857\u9053\u95e8\u724c\u53f7<\/td>\n<\/tr>\n
      Y\u5750\u6807<\/td>\n\u5357\u5317\u8f74\u5411\u4f4d\u7f6e（\u7c73）<\/td>\n\u697c\u5c42\u7f16\u53f7<\/td>\n<\/tr>\n
      \u504f\u822a\u89d2<\/td>\n\u671d\u5411\u65b9\u4f4d（\u5f27\u5ea6\u5236，0\u901a\u5e38\u6307\u4e1c\u65b9）<\/td>\n\u4eba\u4f53\u9762\u90e8\u671d\u5411<\/td>\n<\/tr>\n
      \u901f\u5ea6<\/td>\n\u79fb\u52a8\u901f\u7387（\u7c73\/\u79d2）<\/td>\n\u6b65\u884c\/\u9a7e\u9a76\u901f\u5ea6\u8868\u663e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u4ee3\u7801\u5b9e\u73b0：\u72b6\u6001\u7c7b\u7ed3\u6784<\/h4>\n

      \u5728PythonRobotics\u9879\u76ee\u4e2d，\u673a\u5668\u4eba\u72b6\u6001\u901a\u8fc7\u9762\u5411\u5bf9\u8c61\u7f16\u7a0b\u5c01\u88c5\u3002\u4ee5\u4e0b\u4ee3\u7801\u8282\u9009\u81eaPathPlanning\/ClosedLoopRRTStar\/unicycle_model.py，\u5c55\u793a\u5178\u578b\u5b9e\u73b0：<\/p>\n

      import<\/span> math # \u6570\u5b66\u8fd0\u7b97\u5e93<\/span><\/p>\n

      class<\/span> State<\/span>:<\/span>
      \n def<\/span> __init__<\/span>(<\/span>self,<\/span> x=<\/span>0.0<\/span>,<\/span> y=<\/span>0.0<\/span>,<\/span> yaw=<\/span>0.0<\/span>,<\/span> v=<\/span>0.0<\/span>)<\/span>:<\/span>
      \n self.<\/span>x =<\/span> x # \u4e1c\u5411\u5750\u6807（\u7c73）<\/span>
      \n self.<\/span>y =<\/span> y # \u5317\u5411\u5750\u6807（\u7c73）<\/span>
      \n self.<\/span>yaw =<\/span> yaw # \u504f\u822a\u89d2（\u5f27\u5ea6）<\/span>
      \n self.<\/span>v =<\/span> v # \u7ebf\u901f\u5ea6（\u7c73\/\u79d2）<\/span><\/p>\n

      \u5173\u952e\u89e3\u6790：<\/p>\n

        \n
      • \u7c7b\u6784\u9020\u51fd\u6570\u521d\u59cb\u5316\u9ed8\u8ba4\u5750\u6807\u539f\u70b9<\/li>\n
      • \u91c7\u7528\u56fd\u9645\u5355\u4f4d\u5236\u4fdd\u8bc1\u8ba1\u7b97\u4e00\u81f4\u6027<\/li>\n
      • \u504f\u822a\u89d2\u91c7\u7528\u5f27\u5ea6\u5236\u4fbf\u4e8e\u4e09\u89d2\u51fd\u6570\u8ba1\u7b97<\/li>\n<\/ul>\n

        \u5b9e\u4f8b\u5316\u6f14\u793a：<\/p>\n

        # \u521b\u5efa\u521d\u59cb\u72b6\u6001：\u5750\u6807(10,5)，45\u5ea6\u671d\u5411，\u901f\u5ea62m\/s<\/span>
        \nmy_robot =<\/span> State(<\/span>x=<\/span>10.0<\/span>,<\/span> y=<\/span>5.0<\/span>,<\/span> yaw=<\/span>math.<\/span>radians(<\/span>45<\/span>)<\/span>,<\/span> v=<\/span>2.0<\/span>)<\/span><\/p>\n

        # \u8f93\u51fa\u683c\u5f0f\u5316\u72b6\u6001\u4fe1\u606f<\/span>
        \nprint<\/span>(<\/span>f"X\u5750\u6807: <\/span>{<\/span>my_robot.<\/span>x:<\/span>.2f<\/span>}<\/span><\/span>m"<\/span><\/span>)<\/span>
        \nprint<\/span>(<\/span>f"Y\u5750\u6807: <\/span>{<\/span>my_robot.<\/span>y:<\/span>.2f<\/span>}<\/span><\/span>m"<\/span><\/span>)<\/span>
        \nprint<\/span>(<\/span>f"\u822a\u5411\u89d2: <\/span>{<\/span>math.<\/span>degrees(<\/span>my_robot.<\/span>yaw)<\/span>:<\/span>.2f<\/span>}<\/span><\/span>\u00b0"<\/span><\/span>)<\/span>
        \nprint<\/span>(<\/span>f"\u77ac\u65f6\u901f\u5ea6: <\/span>{<\/span>my_robot.<\/span>v:<\/span>.2f<\/span>}<\/span><\/span>m\/s"<\/span><\/span>)<\/span><\/p>\n

        \u72b6\u6001\u66f4\u65b0\u673a\u5236<\/h4>\n

        \u673a\u5668\u4eba\u8fd0\u52a8\u901a\u8fc7\u8fd0\u52a8\u6a21\u578b\u5b9e\u73b0\u72b6\u6001\u8fed\u4ee3\u66f4\u65b0\u3002\u4ee5\u4e0b\u4ee3\u7801\u6bb5\u6765\u81eaunicycle_model.py，\u6f14\u793a\u5355\u6b65\u72b6\u6001\u9884\u6d4b：<\/p>\n

        dt =<\/span> 0.05<\/span> # \u65f6\u95f4\u6b65\u957f（\u79d2）<\/span>
        \nL =<\/span> 0.9<\/span> # \u8f74\u8ddd\u53c2\u6570（\u7c73）<\/span><\/p>\n

        def<\/span> update<\/span>(<\/span>state,<\/span> accel,<\/span> delta)<\/span>:<\/span>
        \n # \u4f4d\u7f6e\u66f4\u65b0<\/span>
        \n state.<\/span>x +=<\/span> state.<\/span>v *<\/span> math.<\/span>cos(<\/span>state.<\/span>yaw)<\/span> *<\/span> dt
        \n state.<\/span>y +=<\/span> state.<\/span>v *<\/span> math.<\/span>sin(<\/span>state.<\/span>yaw)<\/span> *<\/span> dt<\/p>\n

        # \u65b9\u5411\u66f4\u65b0<\/span>
        \n state.<\/span>yaw +=<\/span> state.<\/span>v\/<\/span>L *<\/span> math.<\/span>tan(<\/span>delta)<\/span> *<\/span> dt
        \n state.<\/span>yaw =<\/span> pi_2_pi(<\/span>state.<\/span>yaw)<\/span> # \u89d2\u5ea6\u5f52\u4e00\u5316<\/span><\/p>\n

        # \u901f\u5ea6\u66f4\u65b0<\/span>
        \n state.<\/span>v +=<\/span> accel *<\/span> dt<\/p>\n

        return<\/span> state<\/p>\n

        \u53c2\u6570\u8bf4\u660e：<\/p>\n

          \n
        • accel: \u52a0\u901f\u5ea6（m\/s\u00b2）<\/li>\n
        • delta: \u524d\u8f6e\u8f6c\u5411\u89d2（\u5f27\u5ea6）<\/li>\n
        • pi_2_pi(): \u5c06\u89d2\u5ea6\u7ea6\u675f\u5728[-\u03c0, \u03c0]\u7684\u8f85\u52a9\u51fd\u6570<\/li>\n<\/ul>\n

          \u8fd0\u52a8\u6a21\u62df\u793a\u4f8b：<\/p>\n

          # \u89d2\u5ea6\u89c4\u7ea6\u51fd\u6570（utils\/angle.py）<\/span>
          \ndef<\/span> pi_2_pi<\/span>(<\/span>angle)<\/span>:<\/span>
          \n return<\/span> (<\/span>angle +<\/span> math.<\/span>pi)<\/span> %<\/span> (<\/span>2<\/span>*<\/span>math.<\/span>pi)<\/span> –<\/span> math.<\/span>pi<\/p>\n

          # \u521d\u59cb\u72b6\u6001：\u5750\u6807(10,5)，45\u5ea6\u822a\u5411，2m\/s<\/span>
          \ncurrent =<\/span> State(<\/span>10.0<\/span>,<\/span> 5.0<\/span>,<\/span> math.<\/span>radians(<\/span>45<\/span>)<\/span>,<\/span> 2.0<\/span>)<\/span><\/p>\n

          # \u65bd\u52a00.5m\/s\u00b2\u52a0\u901f，5\u5ea6\u53f3\u8f6c<\/span>
          \nupdated =<\/span> update(<\/span>current,<\/span> 0.5<\/span>,<\/span> math.<\/span>radians(<\/span>5<\/span>)<\/span>)<\/span><\/p>\n

          # \u8f93\u51fa\u72b6\u6001\u53d8\u5316<\/span>
          \nprint<\/span>(<\/span>f"X\u4f4d\u79fb\u589e\u91cf: <\/span>{<\/span>updated.<\/span>x –<\/span> current.<\/span>x:<\/span>.3f<\/span>}<\/span><\/span>m"<\/span><\/span>)<\/span>
          \nprint<\/span>(<\/span>f"\u822a\u5411\u4fee\u6b63\u91cf: <\/span>{<\/span>math.<\/span>degrees(<\/span>updated.<\/span>yaw –<\/span> current.<\/span>yaw)<\/span>:<\/span>.3f<\/span>}<\/span><\/span>\u00b0"<\/span><\/span>)<\/span><\/p>\n

          \u72b6\u6001\u8868\u5f81\u591a\u6837\u6027<\/h4>\n

          \u4e0d\u540c\u5e94\u7528\u573a\u666f\u9700\u6269\u5c55\u72b6\u6001\u53c2\u6570：<\/p>\n

        • \n

          \u65f6\u5e8f\u8def\u5f84\u89c4\u5212（TimeBasedPathPlanning\/Node.py）：<\/p>\n

          @dataclass<\/span>
          \nclass<\/span> Node<\/span>:<\/span>
          \n pos:<\/span> tuple<\/span> # (x,y)\u5750\u6807<\/span>
          \n time:<\/span> int<\/span> # \u5230\u8fbe\u65f6\u523b<\/span>
          \n heuristic:<\/span> float<\/span> # \u542f\u53d1\u5f0f\u4ee3\u4ef7<\/span><\/p>\n

          \u5f3a\u8c03\u65f6\u95f4\u7ef4\u5ea6\u800c\u975e\u77ac\u65f6\u8fd0\u52a8\u72b6\u6001<\/p>\n<\/li>\n

        • \n

          \u7eaf\u8ffd\u8e2a\u63a7\u5236（pure_pursuit.py）：<\/p>\n

          class<\/span> EnhancedState<\/span>(<\/span>State)<\/span>:<\/span>
          \n def<\/span> __init__<\/span>(<\/span>self,<\/span> is_reverse=<\/span>False<\/span>,<\/span> **<\/span>kwargs)<\/span>:<\/span>
          \n super<\/span>(<\/span>)<\/span>.<\/span>__init__(<\/span>**<\/span>kwargs)<\/span>
          \n self.<\/span>direction =<\/span> –<\/span>1<\/span> if<\/span> is_reverse else<\/span> 1<\/span>
          \n # \u8ba1\u7b97\u540e\u8f74\u4e2d\u5fc3（\u63a7\u5236\u7b97\u6cd5\u9700\u8981）<\/span>
          \n self.<\/span>rear_x =<\/span> self.<\/span>x –<\/span> self.<\/span>direction*<\/span>(<\/span>L\/<\/span>2<\/span>)<\/span>*<\/span>math.<\/span>cos(<\/span>self.<\/span>yaw)<\/span>
          \n self.<\/span>rear_y =<\/span> self.<\/span>y –<\/span> self.<\/span>direction*<\/span>(<\/span>L\/<\/span>2<\/span>)<\/span>*<\/span>math.<\/span>sin(<\/span>self.<\/span>yaw)<\/span><\/p>\n

          \u589e\u52a0\u9006\u5411\u884c\u9a76\u6807\u5fd7\u4f4d\u548c\u540e\u8f74\u5750\u6807\u8ba1\u7b97<\/p>\n<\/li>\n

          \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

          \u5c0f\u7ed3<\/h4>\n

          \u673a\u5668\u4eba\u72b6\u6001\/\u4f4d\u59ff\u662f\u6784\u5efa\u667a\u80fd\u79fb\u52a8\u7cfb\u7edf\u7684\u57fa\u77f3，\u5305\u542b\u7a7a\u95f4\u5750\u6807\u3001\u8fd0\u52a8\u65b9\u5411\u4e0e\u901f\u5ea6\u7b49\u6838\u5fc3\u53c2\u6570\u3002<\/p>\n

          \u901a\u8fc7\u72b6\u6001\u7c7b\u5c01\u88c5\u548c\u8fd0\u52a8\u6a21\u578b\u8fed\u4ee3，\u5b9e\u73b0\u4e86\u673a\u5668\u4eba\u5bf9\u81ea\u8eab\u8fd0\u52a8\u72b6\u6001\u7684\u6301\u7eed\u8ba4\u77e5\u3002\u8fd9\u79cd\u57fa\u7840\u611f\u77e5\u80fd\u529b\u4e3a\u540e\u7eed\u73af\u5883\u5efa\u6a21\u3001\u8def\u5f84\u89c4\u5212\u7b49\u9ad8\u7ea7\u529f\u80fd\u5960\u5b9a\u57fa\u7840\u3002<\/p>\n

          \u4e0b\u4e00\u7ae0：\u73af\u5883\u8868\u793a（\u6805\u683c\u5730\u56fe）<\/p>\n

          \n

          \u7b2c\u4e8c\u7ae0：\u73af\u5883\u8868\u793a（\u6805\u683c\u5730\u56fe）<\/h2>\n

          \u5728\u7b2c\u4e00\u7ae0：\u673a\u5668\u4eba\u72b6\u6001\/\u4f4d\u59ff\u4e2d，\u6211\u4eec\u5b66\u4e60\u4e86\u673a\u5668\u4eba\u5982\u4f55\u8ddf\u8e2a\u81ea\u8eab\u4f4d\u7f6e\u548c\u8fd0\u52a8\u72b6\u6001\u3002<\/p>\n

          \u8fd9\u76f8\u5f53\u4e8e\u673a\u5668\u4eba\u7684"\u8eab\u4efd\u8bc1"\u3002\u4f46\u673a\u5668\u4eba\u5982\u4f55\u611f\u77e5\u5468\u56f4\u73af\u5883？\u5982\u4f55\u77e5\u9053\u5899\u9762\u4f4d\u7f6e\u6216\u969c\u788d\u7269\u5206\u5e03？<\/p>\n

          \u8fd9\u6b63\u662f\u73af\u5883\u8868\u793a\u7684\u6838\u5fc3\u4ef7\u503c！\u672c\u7ae0\u5c06\u805a\u7126\u673a\u5668\u4eba\u7406\u89e3\u73af\u5883\u7684\u5e38\u7528\u65b9\u6cd5\u2014\u2014\u6805\u683c\u5730\u56fe\u3002<\/p>\n

          \u4e3a\u4f55\u9700\u8981\u73af\u5883\u5730\u56fe？<\/h3>\n

          \u4ee5\u626b\u5730\u673a\u5668\u4eba\u4e3a\u4f8b，\u4ec5\u77e5\u9053\u81ea\u8eab\u5750\u6807\u4e0d\u8db3\u4ee5\u6709\u6548\u5de5\u4f5c\u3002\u5b83\u9700\u8981：<\/p>\n

            \n
          • \u907f\u969c\u529f\u80fd：\u907f\u514d\u78b0\u649e\u5899\u58c1\u3001\u5bb6\u5177\u6216\u5ba0\u7269<\/li>\n
          • \u5bfc\u822a\u80fd\u529b：\u89c4\u5212\u623f\u95f4\u7a7f\u8d8a\u8def\u5f84<\/li>\n
          • \u6e05\u6d01\u8def\u5f84：\u8bbe\u8ba1\u5168\u8986\u76d6\u65e0\u6b7b\u89d2\u7684\u79fb\u52a8\u8f68\u8ff9<\/li>\n<\/ul>\n

            \u6805\u683c\u5730\u56fe\u5982\u540c\u6570\u5b57\u5316\u7684\u73af\u5883\u84dd\u56fe，\u5c06\u7a7a\u95f4\u5212\u5206\u4e3a\u82e5\u5e72\u5355\u5143\u683c，\u6bcf\u4e2a\u5355\u5143\u5b58\u50a8\u7279\u5b9a\u4f4d\u7f6e\u7684\u73af\u5883\u4fe1\u606f\u3002<\/p>\n

            \n

            \u6805\u683c\u5730\u56fe\u6838\u5fc3\u8981\u7d20<\/h3>\n

            \u6805\u683c\u5730\u56fe\u53ef\u7c7b\u6bd4\u4e3a\u7535\u5b50\u8868\u683c\u6216\u50cf\u7d20\u56fe\u50cf，\u5176\u6784\u6210\u8981\u7d20\u5305\u62ec：<\/p>\n\n\u8981\u7d20\u63cf\u8ff0\u7c7b\u6bd4<\/tr>\n\n\n\n\n\n\n\n\n
            \u5355\u5143\u683c<\/td>\n\u6784\u6210\u5730\u56fe\u7684\u57fa\u672c\u5355\u5143，\u4ee3\u8868\u771f\u5b9e\u4e16\u754c\u7684\u5fae\u5c0f\u533a\u57df<\/td>\n\u68cb\u76d8\u65b9\u683c\u6216\u4f4e\u5206\u8fa8\u7387\u56fe\u50cf\u7684\u50cf\u7d20<\/td>\n<\/tr>\n
            \u5206\u8fa8\u7387<\/td>\n\u5355\u5143\u683c\u5bf9\u5e94\u7684\u771f\u5b9e\u5c3a\u5bf8（\u59820.1m\u00d70.1m），\u6570\u503c\u8d8a\u5c0f\u7ec6\u8282\u8d8a\u4e30\u5bcc<\/td>\n\u56fe\u50cf\u7f29\u653e\u7ea7\u522b<\/td>\n<\/tr>\n
            \u6570\u503c\u7f16\u7801<\/td>\n\u5355\u5143\u683c\u5b58\u50a8\u7684\u73af\u5883\u4fe1\u606f：<\/td>\n\u7ed8\u753b\u4e2d\u7684\u8272\u5f69<\/td>\n<\/tr>\n
            <\/td>\n– \u5360\u7528(1.0)：\u5b58\u5728\u969c\u788d\u7269<\/td>\n\u9ed1\u8272\u50cf\u7d20\u4ee3\u8868\u5899\u9762<\/td>\n<\/tr>\n
            <\/td>\n– \u81ea\u7531(0.0)：\u53ef\u901a\u884c\u533a\u57df<\/td>\n\u767d\u8272\u50cf\u7d20\u4ee3\u8868\u5f00\u653e\u533a\u57df<\/td>\n<\/tr>\n
            <\/td>\n– \u672a\u77e5(0.5)：\u672a\u63a2\u7d22\u533a\u57df<\/td>\n\u7070\u8272\u50cf\u7d20\u8868\u793a\u672a\u77e5\u533a\u57df<\/td>\n<\/tr>\n
            \u5750\u6807\u539f\u70b9<\/td>\n\u5730\u56fe\u5750\u6807\u7cfb\u4e0e\u771f\u5b9e\u4e16\u754c\u5750\u6807\u7cfb\u7684\u5bf9\u5e94\u5173\u7cfb<\/td>\n\u5927\u5730\u56fe\u4e0a\u7684\u8d77\u59cb\u5b9a\u4f4d\u70b9<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
            \n

            PythonRobotics\u6805\u683c\u5730\u56fe\u5b9e\u8df5<\/h3>\n

            PythonRobotics\u7684GridMap\u7c7b（\u4f4d\u4e8eMapping\/grid_map_lib\/grid_map_lib.py）\u662f\u6784\u5efa\u6805\u683c\u5730\u56fe\u7684\u6838\u5fc3\u5de5\u5177\u3002\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u5982\u4f55\u521b\u5efa\u57fa\u7840\u5730\u56fe [7]：<\/p>\n

            import<\/span> numpy as<\/span> np
            \nimport<\/span> matplotlib.<\/span>pyplot as<\/span> plt
            \nfrom<\/span> Mapping.<\/span>grid_map_lib.<\/span>grid_map_lib import<\/span> GridMap,<\/span> FloatGrid<\/p>\n

            # 1. \u5b9a\u4e49\u5730\u56fe\u53c2\u6570<\/span>
            \nwidth_cells =<\/span> 100<\/span> # \u6a2a\u5411\u5355\u5143\u683c\u6570<\/span>
            \nheight_cells =<\/span> 120<\/span> # \u7eb5\u5411\u5355\u5143\u683c\u6570<\/span>
            \nresolution =<\/span> 0.5<\/span> # \u5355\u5143\u683c\u5206\u8fa8\u7387（\u7c73）<\/span>
            \ncenter_x =<\/span> 10.0<\/span> # \u5730\u56fe\u4e2d\u5fc3\u771f\u5b9e\u5750\u6807X<\/span>
            \ncenter_y =<\/span> –<\/span>0.5<\/span> # \u5730\u56fe\u4e2d\u5fc3\u771f\u5b9e\u5750\u6807Y<\/span><\/p>\n

            # 2. \u521b\u5efaGridMap\u5bf9\u8c61<\/span>
            \nmy_room_map =<\/span> GridMap(<\/span>width_cells,<\/span> height_cells,<\/span> resolution,<\/span>
            \n center_x,<\/span> center_y,<\/span> init_val=<\/span>FloatGrid(<\/span>0.0<\/span>)<\/span>)<\/span><\/p>\n

            print<\/span>(<\/span>f"\u521b\u5efa\u5730\u56fe\u5c3a\u5bf8：<\/span>{<\/span>width_cells}<\/span><\/span>x<\/span>{<\/span>height_cells}<\/span><\/span>\u5355\u5143\u683c"<\/span><\/span>)<\/span>
            \nprint<\/span>(<\/span>f"\u5730\u56fe\u5206\u8fa8\u7387：<\/span>{<\/span>resolution}<\/span><\/span> \u7c73\/\u5355\u5143\u683c"<\/span><\/span>)<\/span><\/p>\n

            \u6280\u672f\u89e3\u6790：<\/p>\n

              \n
            • FloatGrid\u5c01\u88c5\u6d6e\u70b9\u6570\u503c，\u652f\u6301\u540e\u7eed\u6269\u5c55\u590d\u6742\u6570\u636e\u7c7b\u578b<\/li>\n
            • init_val=FloatGrid(0.0)\u521d\u59cb\u5316\u5168\u56fe\u5355\u5143\u683c\u4e3a\u81ea\u7531\u72b6\u6001<\/li>\n
            • \u5750\u6807\u7cfb\u8f6c\u6362\u6a21\u5757\u5b9e\u73b0\u771f\u5b9e\u5750\u6807\u4e0e\u6805\u683c\u7d22\u5f15\u7684\u53cc\u5411\u6620\u5c04 [4]<\/li>\n<\/ul>\n
              \n

              \u969c\u788d\u7269\u6807\u6ce8\u65b9\u6cd5<\/h3>\n

              \u901a\u8fc7\u771f\u5b9e\u5750\u6807\u6807\u6ce8\u969c\u788d\u7269\u793a\u4f8b：<\/p>\n

              # \u6807\u6ce8\u969c\u788d\u7269\u5750\u6807（1.0\u8868\u793a\u5360\u7528\u72b6\u6001）<\/span>
              \nmy_room_map.<\/span>set_value_from_xy_pos(<\/span>10.1<\/span>,<\/span> –<\/span>1.1<\/span>,<\/span> FloatGrid(<\/span>1.0<\/span>)<\/span>)<\/span>
              \nmy_room_map.<\/span>set_value_from_xy_pos(<\/span>10.1<\/span>,<\/span> –<\/span>0.1<\/span>,<\/span> FloatGrid(<\/span>1.0<\/span>)<\/span>)<\/span>
              \nmy_room_map.<\/span>set_value_from_xy_pos(<\/span>11.1<\/span>,<\/span> 0.1<\/span>,<\/span> FloatGrid(<\/span>1.0<\/span>)<\/span>)<\/span><\/p>\n

              # \u67e5\u8be2\u7279\u5b9a\u5750\u6807\u503c<\/span>
              \nvalue =<\/span> my_room_map.<\/span>get_value_from_xy_pos(<\/span>10.1<\/span>,<\/span> 0.1<\/span>)<\/span>
              \nprint<\/span>(<\/span>f"\u5750\u6807(10.1,0.1)\u503c：<\/span>{<\/span>value.<\/span>get_float_data(<\/span>)<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n

              \u6ce8\u610f\u4e8b\u9879：<\/p>\n

                \n
              • \u8d8a\u754c\u5750\u6807\u64cd\u4f5c\u8fd4\u56deFalse，\u5730\u56fe\u4e0d\u4f1a\u81ea\u52a8\u6269\u5c55\u8fb9\u754c<\/li>\n
              • \u6279\u91cf\u6807\u6ce8\u5efa\u8bae\u4f7f\u7528set_value_from_xy_index\u63d0\u5347\u6548\u7387<\/li>\n<\/ul>\n
                \n

                \u5730\u56fe\u53ef\u89c6\u5316\u5b9e\u73b0<\/h3>\n

                matplotlib\u53ef\u89c6\u5316\u5de5\u5177\u5c55\u793a\u6805\u683c\u5730\u56fe：<\/p>\n

                my_room_map.<\/span>plot_grid_map(<\/span>)<\/span>
                \nplt.<\/span>title(<\/span>"\u623f\u95f4\u6805\u683c\u5730\u56fe"<\/span>)<\/span>
                \nplt.<\/span>xlabel(<\/span>"X\u5750\u6807(\u7c73)"<\/span>)<\/span>
                \nplt.<\/span>ylabel(<\/span>"Y\u5750\u6807(\u7c73)"<\/span>)<\/span>
                \nplt.<\/span>axis(<\/span>"equal"<\/span>)<\/span> # \u4fdd\u6301\u5355\u5143\u683c\u65b9\u5f62\u6bd4\u4f8b<\/span>
                \nplt.<\/span>grid(<\/span>True<\/span>)<\/span>
                \nplt.<\/span>show(<\/span>)<\/span><\/p>\n

                \u8f93\u51fa\u7279\u5f81：<\/p>\n

                  \n
                • \u84dd\u8272\u5355\u5143\u683c\u8868\u793a\u969c\u788d\u7269（1.0）<\/li>\n
                • \u6d45\u8272\u533a\u57df\u4e3a\u81ea\u7531\u7a7a\u95f4（0.0）<\/li>\n
                • \u5750\u6807\u7cfb\u81ea\u52a8\u5bf9\u9f50\u771f\u5b9e\u4e16\u754c\u5c3a\u5ea6 [13]<\/li>\n<\/ul>\n
                  \n

                  \u5e95\u5c42\u6280\u672f\u89e3\u6790<\/h3>\n

                  \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

                  \u5750\u6807\u8f6c\u6362\u7b97\u6cd5<\/h4>\n

                  \u6838\u5fc3\u8f6c\u6362\u51fd\u6570\u5b9e\u73b0\u771f\u5b9e\u5750\u6807\u5230\u6805\u683c\u7d22\u5f15\u7684\u7cbe\u786e\u6620\u5c04：<\/p>\n

                  class<\/span> GridMap<\/span>:<\/span>
                  \n def<\/span> calc_xy_index_from_position<\/span>(<\/span>self,<\/span> pos,<\/span> lower_pos,<\/span> max_index)<\/span>:<\/span>
                  \n """\u8ba1\u7b97\u5750\u6807\u5bf9\u5e94\u7684\u6805\u683c\u7d22\u5f15"""<\/span>
                  \n ind =<\/span> int<\/span>(<\/span>np.<\/span>floor(<\/span>(<\/span>pos –<\/span> lower_pos)<\/span> \/<\/span> self.<\/span>resolution)<\/span>)<\/span>
                  \n return<\/span> ind if<\/span> 0<\/span> <=<\/span> ind <=<\/span> max_index else<\/span> None<\/span><\/p>\n

                  \u8ba1\u7b97\u8fc7\u7a0b：<\/p>\n

                • \u8ba1\u7b97\u5750\u6807\u76f8\u5bf9\u4e8e\u5730\u56fe\u5de6\u4e0b\u89d2\u7684\u504f\u79fb\u91cf<\/li>\n
                • \u9664\u4ee5\u5206\u8fa8\u7387\u83b7\u5f97\u5355\u5143\u683c\u6570\u91cf<\/li>\n
                • \u53d6\u6574\u786e\u4fdd\u7d22\u5f15\u4e3a\u6574\u6570<\/li>\n
                • \u8fb9\u754c\u68c0\u67e5\u9632\u6b62\u8d8a\u754c\u8bbf\u95ee<\/li>\n

                  \u6570\u636e\u5b58\u50a8\u4f18\u5316<\/h4>\n

                  \u91c7\u7528\u4e00\u7ef4\u6570\u7ec4\u5b58\u50a8\u4e8c\u7ef4\u6805\u683c\u6570\u636e，\u901a\u8fc7\u884c\u5217\u8ba1\u7b97\u5feb\u901f\u5b9a\u4f4d：<\/p>\n

                  grid_ind =<\/span> y_ind *<\/span> self.<\/span>width +<\/span> x_ind # \u4e8c\u7ef4\u8f6c\u4e00\u7ef4\u7d22\u5f15<\/span>
                  \nself.<\/span>data[<\/span>grid_ind]<\/span> =<\/span> value # \u66f4\u65b0\u5b58\u50a8<\/span><\/p>\n

                  \n

                  \u9ad8\u7ea7\u6805\u683c\u5730\u56fe\u53d8\u4f53<\/h3>\n

                  \u52a8\u6001\u969c\u788d\u7269\u5730\u56fe<\/h4>\n

                  TimeBasedPathPlanning\/GridWithDynamicObstacles.py\u5b9e\u73b0\u65f6\u5e8f\u6805\u683c\u5806\u6808，\u652f\u6301：<\/p>\n

                    \n
                  • \u9884\u6d4b\u672a\u6765\u65f6\u6bb5\u7684\u969c\u788d\u7269\u5206\u5e03<\/li>\n
                  • \u56db\u7ef4\u6570\u636e\u5b58\u50a8（x,y,time,value）<\/li>\n
                  • \u52a8\u6001\u8def\u5f84\u89c4\u5212\u907f\u969c<\/li>\n<\/ul>\n

                    \u4f20\u611f\u5668\u5efa\u56fe\u6280\u672f<\/h4>\n

                    lidar_to_grid_map.py\u5c55\u793a\u6fc0\u5149\u96f7\u8fbe\u5efa\u56fe\u6d41\u7a0b：<\/p>\n

                  • \u6fc0\u5149\u5c04\u7ebf\u6295\u5c04（ray casting）\u6807\u8bb0\u969c\u788d<\/li>\n
                  • \u533a\u57df\u751f\u957f\u7b97\u6cd5\u586b\u5145\u672a\u77e5\u533a\u57df<\/li>\n
                  • \u6982\u7387\u66f4\u65b0\u673a\u5236\u5904\u7406\u4f20\u611f\u5668\u566a\u58f0<\/li>\n
                    \n

                    \u5c0f\u7ed3<\/h3>\n

                    \u672c\u7ae0\u6df1\u5165\u63a2\u8ba8\u4e86\u6805\u683c\u5730\u56fe\u4f5c\u4e3a\u73af\u5883\u8868\u793a\u7684\u6838\u5fc3\u65b9\u6cd5，\u6db5\u76d6\u5730\u56fe\u521b\u5efa\u3001\u969c\u788d\u6807\u6ce8\u3001\u53ef\u89c6\u5316\u53ca\u5e95\u5c42\u5b9e\u73b0\u539f\u7406\u3002<\/p>\n

                    \u7ed3\u5408\u7b2c\u4e00\u7ae0\u7684\u673a\u5668\u4eba\u72b6\u6001\u8ddf\u8e2a，\u73b0\u5df2\u5177\u5907\u73af\u5883\u8ba4\u77e5\u57fa\u7840\u80fd\u529b\u3002\u4e0b\u4e00\u7ae0\u5c06\u89e3\u9501\u673a\u5668\u4eba\u7684\u8fd0\u52a8\u5965\u79d8！<\/p>\n

                    \u4e0b\u4e00\u7ae0：\u673a\u5668\u4eba\u8fd0\u52a8\u6a21\u578b<\/p>\n","protected":false},"excerpt":{"rendered":"

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