Local　path　planning　and　obstacle　avoidance　in　complex　environments　are　two　challenging　problems　in　the　research　of　intelligent　robots.　In　this　study,　we　develop　a　novel　approach　grounded　in　deep　distributional　reinforcement　learning　to　address　these　challenges.　Within　this　methodology,　agents　instantiated　by　deep　neural　networks　perceive　real-time　local　environmental　information　through　sensor　data,　addressing　inherent　stochasticity　and　local　path　planning　tasks　in　complex　environments.　End-to-end　training　is　facilitated　via　distributional　reinforcement　learning　algorithms　and　reward　functions　informed　by　heuristic　knowledge.　Optimal　actions　for　path　planning　are　determined　through　return　value　distributions.　Finally,　the　simulation　results　show　that　the　success　rate　of　the　proposed　distributed　algorithm　is　98%　in　a　random　environment　and　94%　in　a　dynamic　environment.　This　proves　that　the　algorithm　has　better　generalization　and　flexibility　than　the　non-distributed　algorithm.　©　2024　Elsevier　B.V.