Online terrain estimation for autonomous vehicles on deformable terrains

Abstract

In this work, a terrain estimation framework is developed for autonomous vehicles operating on deformable terrains. Previous work in this area usually relies on steady state tire operation, linearized classical terramechanics models, or on computationally expensive algorithms that are not suitable for real-time estimation. To address these shortcomings, this work develops a reduced-order nonlinear terramechanics model as a surrogate of the Soil Contact Model (SCM) through extending a state-of-the-art Bekker model to account for additional dynamic effects. It is shown that this reduced-order surrogate model is able to accurately replicate the forces predicted by the SCM while reducing the computation cost by an order of magnitude. This surrogate model is then utilized in a unscented Kalman filter to estimate the sinkage exponent. Simulations suggest this parameter can be estimated within 4% of its true value for clay and sandy loam terrains. It is also shown that utilizing this estimated parameter can reduce the prediction errors of the future vehicle states by orders of magnitude, which could assist with achieving more robust model-predictive autonomous navigation strategies.

Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains

“…Particularly, most of the existing contact models, having a suitable computational workload for realtime planning, are based on the Bekker-Wong theory, a semi-empirical method that combines physics considerations with the evaluation of experimental soil-dependent parameters [3] [4]. Several methods have been proposed to estimate online the Bekker's parameters exploiting proprioceptive data [23] [24]. Then, once the parameters have been identified, the Bekker's equations can be used to infer contact forces, torques, drawbar pull, as well as energy consumption.…”

“…Specifically, SCM extends the Bekker-Wong theory to 3D-bodies dynamic simulators, enabling more realistic modeling of arbitrary shaped wheel-terrain interactions in deformable terrains, compared with traditional 2D Bekker models, and retaining a more efficient computing workload than alternative Finite Element or Discrete Element methods [4] [46]. Therefore, the SCM represents a valid asset for testing of integrated UGV prediction and planning algorithms on a variety of realistic off-road scenarios [24] [27]. Practically, terrains with different behaviors can be modeled in SCM by the setting of 6 terraindependent parameters: exponent of sinkage n, cohesive modulus K c , frictional modulus K φ , cohesion limit c, angle of internal friction φ, Janosi-Hanamoto coefficient J [3] [47].…”

Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

“…The development in study of terrain classification discussed in the previous section can extend to the research of mobile robot motion planning and control with the help of a suitable robot model [45,46] and tire terrain interaction model [47][48][49][50][51]. The section investigates the role of terrain parameters in controlling torques to the wheels.…”

“…A linear regression model is formed. James Dallas et al [51] investigated the formulation of terrain parameters based on soil cohesion and internal friction angle. The model is analyzed using the least square method, Neuton Raphton method, and Simpson's rule, with inputs from the measured internal forces [63].…”

Modular mobile manipulators coalition formation through distributed transportation tasks allocation

“…Usually, they are connected and have several perception sensors used for localisation, safety and obstacle avoidance. 11 They are used in several fields such as manufacturing systems, 12,13 military operations, 14 medical care systems, space exploration, 15 etc. In the industrial field, these robots are called AGVs (Automatic Guided Vehicles).…”