Kinodynamic Trajectory Following with STELA: Simultaneous Trajectory Estimation & Local Adaptation

摘要

STELA execution on a real MuSHR robot.The middle image is a composite from 2 top-down cameras used for localization, covering a 7.6mx2.3mworkspace.The robot follows a trajectory computed by a planner with knowledge of the obstacles (rocks and boxes) but no knowledge of the ramp, affecting execution.Top and Bottom: i) STELA estimation and plan when the robot is on the unknown ramp; ii) the robot recovers from the ramp and avoids an obstacle; iii) STELA adapts the plan to follow the planned trajectory while avoiding another obstacle; iv) the robot reaches the end of the plan without collisions.Rviz visualization includes obstacles, planned trajectory (green), forward horizon (white), and history (cyan).Stars indicate corresponding states between the visualization and the real robot.Abstract-State estimation and control are often addressed separately, leading to unsafe execution due to sensing noise, execution errors, and discrepancies between the planning model and reality.Simultaneous control and trajectory estimation using probabilistic graphical models has been proposed as a unified solution to these challenges.Previous work, however, relies heavily on appropriate Gaussian priors and is limited to holonomic robots with linear time-varying models.The current research extends graphical optimization methods to vehicles with arbitrary dynamical models via Simultaneous Trajectory Estimation and Local Adaptation (STELA).The overall approach initializes feasible trajectories using a kinodynamic, samplingbased motion planner.Then, it simultaneously: (i) estimates the past trajectory based on noisy observations, and (ii) adapts the controls to be executed to minimize deviations from the planned, feasible trajectory, while avoiding collisions.The proposed factor graph representation of trajectories in STELA can be applied for any dynamical system given access to first or secondorder state update equations, and introduces the duration of execution between two states in the trajectory discretization as an optimization variable.These features provide both generalization and flexibility in trajectory following.In addition to targeting computational efficiency, the proposed strategy performs incremental updates of the factor graph using the iSAM algorithm and introduces a time-window mechanism.This mechanism allows the factor graph to be dynamically updated to operate over a limited history and forward horizon of the planned trajectory.This enables online updates of controls at a minimum of 10Hz.Experiments demonstrate that STELA achieves at least comparable performance to previous frameworks on idealized vehicles with linear dynamics.STELA also directly applies to and successfully solves trajectory following problems for more complex dynamical models.Beyond generalization, simulations assess STELA's robustness under varying levels of sensing and execution noise, while ablation studies highlight the importance of different components of STELA.Real-world experiments validate STELA's practical applicability on a low-cost MuSHR robot, which exhibits high noise and non-trivial dynamics.