Planning kinodynamically feasible trajectories for autonomous vehicles is computationally expensive, especially when planning over long distances in unstructured environments. This paper presents a hierarchical planner, called the Maverick planner, which can find such trajectories efficiently. It comprises two parts: a waypoint planner that uses a simplified vehicle model and an RRT* planner that respects full kinodynamic constraints. The waypoint planner quickly finds a directed graph of waypoints from start to goal, which is then used to bias sampling and speed up computation in RRT*. The Maverick planner is capable of anytime planning and continuous replanning. It has been tested extensively in simulation and on real vehicles. When planning on a sensor-generated map of the SwRI test track it can find a feasible path over 0.5 km in under 16 ms, and refine that path to within 1% of the local optimum in 0.5 seconds.

Advances in hardware design have made wheeled mobile robots (WMRs) exceptionally mobile. To fully exploit this mobility, we present a novel dynamic model formulation for use in WMR planning, control, and estimation systems. The formulation is high fidelity, general, modular, and fast. It builds on our prior work on recursive methods for 3-D kinematics derivation and constrained motion prediction using differential algebraic equations. It is stable, even for large integration steps, and can enforce realistic nonlinear models of wheel-terrain interaction. Simulation tests show our dynamic models to be more functional, stable, and efficient than common alternatives. Simulations can run over 1K × faster than real time on an ordinary PC. Experimental results on multiple platforms and terrain types show that, once calibrated, our models predict motion accurately. To facilitate their use, we have released open-source MATLAB and C++libraries implementing our modeling/simulation methods.

We formulate the problem of wheeled mobile robot (WMR) modeling under wheel slip, while moving over rough terrain, as the integral of a differential algebraic equation (DAE) of either first or second order. That is, the elements of the model include both differential equations and kinematic constraints. While such a formulation is typical of Lagrangian dynamics in augmented form, we show how identical principles apply to kinematic models of motion. In effect, we show in this paper that the very high fidelity models traditionally used in offline simulation can be readily adapted for use in real time for both control and estimation of the motion of arbitrary WMRs over arbitrary rolling terrain, while slipping. While such models are more correct and general than the naive methods that are typically used, we also show that they are orders of magnitude more efficient than such naive methods for equivalent accuracy.

This paper presents an online approach to calibrating vehicle model parameters that uses the integrated dynamics of the system. Specifically, we describe the identification of the time constant and delay in a first-order model of the vehicle powertrain, as well as parameters required for pose estimation (including position offsets for the inertial measurement unit, steer angle sensor parameters, and wheel radius). Our approach does not require differentiation of state measurements like classical techniques; making it ideal when only low-frequency measurements are available. Experimental results on the LandTamer and Zoë rover platforms show online calibration using integrated dynamics to be fast and more accurate than both manual and classical calibration methods.

When GPS, or other absolute positioning, is un-available, terrain-relative velocity is crucial for dead reckoning and the vehicle's pose estimate. Unfortunately, the position-denied accuracy of the inertial navigation system (INS) is governed by the performance of the linear velocity aiding sources, such as wheel odometry, which are typically corrupted by large systematic errors due to wheel slip. As a result, affordable terrestrial inertial navigation is ineffective in estimating position when denied position fixes for an extended period of time. For mobile robots, the mapping between inputs and resultant behavior depends critically on terrain conditions which vary significantly over time and space which cannot be pre-programmed. Past work has used Integrated Perturbative Dynamics (IPD) to identify successively systematic and stochastic models of wheel slip, but treated the pose filter only as input without improving the odometry measurements used for vehicle navigation. We present a unique approach of a predictive vehicle slip model in a delayed state extended Kalman filter. The relative pose difference between the current state and delayed state is used as a measurement update to the vehicle slip model. These results create an opportunity to compensate for wheel slip effects in terrestrial inertial navigation. This paper presents the design, calibration, and verification of such a system and concludes that the position-denied performance of the compensated system is far superior.

This paper describes and evaluates a 3-D kinematic controller for passively-steered rovers. Passively-steered rovers have no steering motors, but rely on differential wheel velocities to change the axle steer angles. This passive steering design is reliable and efficient but more challenging to control than powered steering designs, especially when driving on rough terrain. A controller based on 2-D kinematics fails to accurately maintain the desired trajectory when traversing obstacles. The presented 3-D kinematic controller uses inertial and proprioceptive sensing to modify commanded steer angles and wheel velocities, greatly improving steering accuracy. Validation in simulation and physical experiments is presented. These results are significant because they establish the viability of the passive-steering configuration for precise navigation.

An optical flow odometry method for mobile robots using a single downward-looking camera is presented. The method is robust to the robot's own moving shadow and other sources of error. Robustness derives from two techniques: prevention of feature selection on or near shadow edges and elimination of outliers based on inconsistent motion. In tests where the robot's shadow dominated the image, prevention of feature selection near shadow edges allowed accurate velocity estimation when outlier rejection alone failed. Performance was evaluated on two robot platforms and on multiple terrain types at speeds up to 2 m/s.