An　autonomous　path-planning　strategy　based　on　Skinner　operant　conditioning　principle　and　reinforcement　learning　principle　is　developed　in　this　paper.　The　core　strategies　are　the　use　of　tendency　cell　and　cognitive　learning　cell,　which　simulate　bionic　orientation　and　asymptotic　learning　ability.　Cognitive　learning　cell　is　designed　on　the　base　of　Boltzmann　machine　and　improved　Q-Learning　algorithm,　which　executes　operant　action　learning　function　to　approximate　the　operative　part　of　robot　system.　The　tendency　cell　adjusts　network　weights　by　the　use　of　information　entropy　to　evaluate　the　function　of　operate　action.　The　results　of　the　simulation　experiment　in　mobile　robot　showed　that　the　designed　autonomous　path-planning　strategy　lets　the　robot　realize　autonomous　navigation　path　planning.　The　robot　learns　to　select　autonomously　according　to　the　bionic　orientate　action　and　have　fast　convergence　rate　and　higher　adaptability.