Trajectory Tracking Control of Skid-Steering Mobile Robots with Slip and Skid Compensation using Sliding-Mode Control and Deep Learning

Slip and skid compensation is crucial for mobile robots' navigation in outdoor environments and uneven terrains. In addition to the general slipping and skidding hazards for mobile robots in outdoor environments, slip and skid cause uncertainty for the trajectory tracking system and put the validity of stability analysis at risk. Despite research in this field, having a real-world feasible online slip and skid compensation is still challenging due to the complexity of wheel-terrain interaction in outdoor environments. This paper presents a novel trajectory tracking technique with real-world feasible online slip and skid compensation at the vehicle-level for skid-steering mobile robots in outdoor environments. The sliding mode control technique is utilized to design a robust trajectory tracking system to be able to consider the parameter uncertainty of this type of robot. Two previously developed deep learning models [1], [2] are integrated into the control feedback loop to estimate the robot's slipping and undesired skidding and feed the compensator in a real-time manner. The main advantages of the proposed technique are (1) considering two slip-related parameters rather than the conventional three slip parameters at the wheel-level, and (2) having an online real-world feasible slip and skid compensator to be able to reduce the tracking errors in unforeseen environments. The experimental results show that the proposed controller with the slip and skid compensator improves the performance of the trajectory tracking system by more than 27%

Interactive multiple model filtering for robotic navigation and tracking applications

The work contained in this thesis focuses on two main objectives. The first objective is to evaluate the Interactive Multiple Model (IMM) filter method for robotic applications including inertial navigation systems (INS) and computer vision tracking. The second objective is to design an experimental testbed for multi-model mobile robot state estimation research in the Intelligent Systems Laboratory (ISLAB) at Memorial University. An IMM estimator uses multiple filters that run simultaneously to produce a combined weighted estimation of an observed system’s states. The weights are functions of the likelihood of how well each individual filter matches the current behaviour exhibited by the system. The performance of IMM filtering is evaluated using two different strategies for augmenting the system’s filter banks. The first method uses multiple kinematic models (constant velocity and constant acceleration models) in a mean-shift-based computer vision tracking application. The results of this experiment indicate that the IMM improves tracking performance due to its ability to adapt to the continuously changing motion characteristics of 2D blobs in videos. The second approach uses the same kinematics for each filter; however, the process and sensor noise parameters are tuned differently for each model. This method is tested in INS applications for both an automobile and a skid-steer mobile robot (Seekur Jr). Results show that the method improves INS tracking over single model Extended Kalman Filter (EKF) designs. Furthermore, an augmented state-space model containing skid-steer instantaneous center of rotation (ICR) kinematics is presented for future testing on the Seekur Jr INS. The experimental testbed designed in this thesis work is an operational data acquisition system developed for use with the Seekur Jr robot. The Seekur Jr platform has been Robot Operating System (ROS) enabled with access to data streams from 2D Lidar, 3D nodding Lidar, inertial measurement unit, digital compass, wheel encoder, onboard Global Positioning System (GPS), real-time kinematic (RTK) differential global positioning system (DGPS) ground truth, and vision sensors. The physical setup and data networking aspects of the testbed have been used for validation of an IMM filter presented in this thesis and is fully configured for future multi-model localization experiments of the ISLAB

Effects of Turning Radius on Skid-Steered Wheeled Robot Power Consumption on Loose Soil

This research highlights the need for a new power model for skid-steered wheeled robots driving on loose soil and lays the groundwork to develop such a model. State-of-the-art power modeling assumes hard ground; under typical assumptions this predicts constant power consumption over a range of small turning radii where the inner wheels are rotating backwards. However, experimental results performed both in the field and in a controlled laboratory sandbox show that, on sand, power is not in fact constant with respect to turning radius. Power peaks by 20% in a newly identified range of turns where the inner wheels rotate backwards but are being dragged forward. This range of turning radii spans from half the rover width to R', the radius at which the inner wheel is not commanded to turn. Data shows higher motor torque and wheel sinkage in this range. To progress toward predicting the required power for a skid-steered wheeled robot to maneuver on loose soil, a preliminary version of a two-dimensional slip-sinkage model is proposed, along with a model of the force required to bulldoze the pile of sand that accumulates next to the wheels as it they are skidding. However, this is shown to be a less important factor contributing to the increased power in small-radius turns than the added inner wheel torque induced by dragging these wheels through the piles of sand they excavate by counter-rotation (in the identified range of turns). Finally, since a direct application of a power model is to design energy-efficient paths, time dependency of power consumption is also examined. Experiments show reduced rover angular velocity in sand around turning radii where the inner wheels are not rotated and this leads to the introduction to a new parameter to consider in path planning: angular slip