Apurva Patil

Savitribai Phule Pune University, Walker (United States)

Papers

Total Citations

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About

Apurva Patil is a robotics and control systems researcher whose work spans robot kinematics, stochastic optimal control, and risk-aware autonomous navigation. Patil's most widely recognized contribution is a 2017 study on inverse kinematics for 5-DOF robot arms, which garnered 26 citations by proposing a practical algorithm grounded in Denavit-Hartenberg parameters and a brute-force iterative approach to solving nonlinear kinematic equations — a foundational resource for robotics engineers designing manipulator control systems. Building on this foundation, Patil's research has evolved toward the theoretical frontiers of autonomous systems under uncertainty. A 2022 paper tackles chance-constrained stochastic optimal control by elegantly bridging path integral methods with Hamilton-Jacobi-Bellman equations through Lagrangian relaxation, earning 7 citations for its rigorous treatment of risk-minimization in continuous-time systems. Complementing this, a 2021 work introduces novel probabilistic bounds for estimating collision risk in autonomous robot navigation under Gaussian uncertainty, addressing a critical gap between theoretical motion planning and real-world execution. Together, Patil's body of work reflects a clear trajectory from classical robotics modeling toward principled, safety-conscious decision-making in uncertain environments — increasingly vital as autonomous systems move into complex, real-world deployments.