From Teleoperation to Autonomy: Reviewing the Pathway for Mobile Robot Navigation in Cluttered Environments

Unraveling the solutions to the numerous challenges poised on mobile robot navigation in cluttered, dynamic environments remain an ambiguous concern in robotics. This is in spite of resounding advances so far recorded in key areas like sensing, perception, and control, as most commercial platforms obviously still rely heavily on human teleoperation when operating outside structured warehouses. Hence the, review synthesizes 2015–2026 research on the transition from teleoperation to full autonomy for mobile robots in cluttered indoor and outdoor settings. Using a PRISMA-guided literature search across IEEE Xplore, ACM, and Scopus, we analyze 127 peer-reviewed studies through the lens of Technology Readiness Levels [TRL] and the “Sense-Plan-Act” autonomy stack. Findings reveal three dominant pathways among others to wit: 1. learning-based end-to-end navigation, 2. modular classical pipelines with learned components, and 3. hybrid human-in-the-loop autonomy. However, key bottlenecks still persist in sim-to-real transfer, long-horizon planning under occlusion, and certifiable safety. We therefore propose a four-stage pathway: Teleoperated Baseline; Shared Autonomy; Conditional Autonomy and Full Autonomy while specifically recommending it for datasets, benchmarks, and regulatory testbeds. The review concludes that modular-hybrid architectures currently offer the most feasible and flexible route to deployment, while end-to-end learning remains vital for long-term robustness.