Subject Training

Topic : SLAM-Centric Safe Control Design for mobile robots (ROS/Gazebo, CARLA) Abstract: Modern mobile robots must plan, perceive, and control robustly in dynamic environments in a safe manner. This research combines classical aspects from control and planning to Simultaneous Localization and Mapping (SLAM), then adds safety layer to push autonomy beyond hand tuned pipelines, all built and tested in ROS + Gazebo and high fidelity simulators (CARLA). Scholarship Duration: 6 months Period: March 2026 - August 2026 Supervisors (France): Mayank Shekhar JHA, Associate Professor, CRAN, University of Lorraine, France. Didier Theilliol, Full Professor, CRAN, University of Lorraine, France. Description: The project envisages, for mobile robots, a SLAM-first autonomy stack in which perception, simulation engineering, planning, and control are composed with safety as a primary requirement. The work centers on the development of a visual/visual-inertial SLAM module that provides pose, mapping, and basic uncertainty estimates and serves as the backbone for downstream decision and control. Simulation is a core activity: a ROS/ Gazebo pipeline will be engineered for rapid iteration and systematically bridged with CARLA to enable high-fidelity sensing/physics, domain randomization, and scenario stress-testing (dynamic obstacles, wind, sensor artifacts). On top of SLAM, motion planning will be implemented using graph-based (A*/D* Lite) and sampling-based (RRT/RRT*) methods with time-parameterized, kinodynamically feasible trajectories that account for safety margins and, where relevant, SLAM uncertainty. Control strategies will be investigated from PID to optimal control (LQR/MPC) for point-to-point motion and constrained trajectory following. Safe control learning is a principal objective: safety filters (e.g., Control Barrier Function-based QPs), runtime assurance, and robust constraint handling will be integrated into both the classical controllers and the safety layer. As a novelty component, safe control design will be developed to augment tracking and local avoidance, with risk-aware rewards and ablations against classical baselines. Deliverables include a reproducible codebase (ROS/Gazebo ?CARLA), quantitative benchmarks (classical vs. modern), a concise research report, a demo video, and preparation of a manuscript for submission to reputed venues. Objectives include:
⬢ Engineer a SLAM backbone: Implement and benchmark a visual/VIO SLAM pipeline (pose, mapping, basic uncertainty), establishing it as the primary state-estimation module.
⬢ Build the simulation stack: Configure a reproducible ROS/Gazebo environment (worlds, sensors, plugins) with scripted scenarios for rapid iteration.
⬢ Bridge high-fidelity simulation: Integrate CARLA ? ROS/Gazebo (time sync, topics, sensors) to enable physics- and sensor-realistic stress tests and domain randomization.
⬢ Path planning & trajectories: Implement A*/D* Lite and RRT/RRT*; generate time-parameterized, kinodynamically feasible trajectories (e.g., jerk- limited splines).
⬢ Baseline control: Develop basic PID based cntrol and preferebly extend to optimal control (LQR) for point-to-point motion and constrained trajectory tracking.
⬢ Safety layer for control: Design CBF-QP safety filters and/or runtime assurance (Simplex) to guarantee constraint satisfaction and minimize safety violations.