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%

In situ skid estimation for mobile robots in outdoor environments

Skidding is a surface hazard for mobile robots' navigation and traction control systems when operating in outdoor environments and uneven terrains due to the wheel–terrain interaction. It could lead to large trajectory tracking errors, losing the robot's controllability, and mission failure occurring. Despite research in this field, the development of a real-world feasible in situ skid estimation system with the capability of operating in harsh and unforeseen environments using low-cost/power and ease of integrating sensors is still an open problem in terramechanics. This paper presents a novel velocity-based definition for skidding that enables a real-world feasible estimation for the mobile robot's undesired skidding at the vehicle-level in outdoor environments. The proposed technique estimates the undesired skidding using a combination of two proprioceptive sensors (e.g., inertial measurement unit and wheel encoder) and deep learning. The practicality of a velocity-based definition and the performance of the proposed undesired skid estimation technique are evaluated experimentally in various outdoor terrains. The results show that the proposed technique performs with less than 11.79 mm/s mean absolute error and estimates the direction of undesired skidding with approximately 98% accuracy.</p

Slippage Estimation for Skid-Steering Robots in Outdoor Environments using Deep Learning

Estimating the slippage ratio is a crucial and challenging task to safely navigate a wheeled mobile robot in outdoor environments. High wheel-slippage could affect the controllability of the robot, which makes it a safety risk. This paper proposes a slippage estimation technique for skid-steering mobile robots in outdoor environments using deep-learning and proprioceptive sensors. The proposed technique consists of two stages: data collection then training a slippage estimation model respectively. For the first stage, a Pioneer 3-AT robot is driven in a real-world environment on grass and gravel with two different tyres (e.g. solid and pneumatic) and proprioceptive sensors (e.g. IMU and wheel encoder) are utilized to measure the behaviour of the robot. Thereafter, a CNN-LSTM model is proposed and trained on the experimental data to evaluate the model performance for both tyres. The main advantages of the proposed technique are the capability of estimating the slippage ratio for the entire robot rather than one wheel, utilizing low-cost proprioceptive sensors and being unaffected by weather conditions. The results confirm the capacity of the proposed method to be utilized in real-world environments and uneven terrains

In situ slip estimation for mobile robots in outdoor environments

Accounting for wheel–terrain interaction is crucial for navigation and traction control of mobile robots in outdoor environments and rough terrains. Wheel slip is one of the surface hazards that needs to be detected to mitigate against the risk of losing the robot's controllability or mission failure occurring. The open problems in the Terramechanics field addressed are (1) the need for in situ wheel-slippage estimation in harsh environments using low-cost/power and easy to integrate sensors, and (2) removing the need for prior information of the soil, which is not always available. This paper presents a novel slip estimation method that utilizes only two proprioceptive sensors (IMU and wheel encoder) to estimate the wheel slip using deep learning methods. It is experimentally shown to be real-world feasible in outdoor, uneven terrains without prior soil information assumptions. Comparison with previously used machine learning algorithms for continuous and discrete slip estimation problems show more than 9% and 14% improvement in estimation performance, respectively.</p