Gaurav Kewlani

Stochastic mobility-based path planning in uncertain environments

The ability of mobile robots to generate feasible trajectories online is an important requirement for their autonomous operation in unstructured environments. Many path generation techniques focus on generation of time- or distance-optimal paths while obeying dynamic constraints, and often assume precise knowledge of robot and/or environmental (i.e. terrain) properties. In uneven terrain, it is essential that the robot mobility over the terrain be explicitly considered in the planning process. Further, since significant uncertainty is often associated with robot and/or terrain parameter knowledge, this should also be accounted for in a path generation algorithm. Here, extensions to the rapidly exploring random tree (RRT) algorithm are presented that explicitly consider robot mobility and robot parameter uncertainty based on the stochastic response surface method (SRSM). Simulation results suggest that the proposed approach can be used for generating safe paths on uncertain, uneven terrain.

A polynomial chaos approach to the analysis of vehicle dynamics under uncertainty

The ability of ground vehicles to quickly and accurately analyse their dynamic response to a given input is critical to their safety and efficient autonomous operation. In field conditions, significant uncertainty is associated with terrain and/or vehicle parameter estimates, and this uncertainty must be considered in the analysis of vehicle motion dynamics. Here, polynomial chaos approaches that explicitly consider parametric uncertainty during modelling of vehicle dynamics are presented. They are shown to be computationally more efficient than the standard Monte Carlo scheme, and experimental results compared with the simulation results performed on ANVEL (a vehicle simulator) indicate that the method can be utilised for efficient and accurate prediction of vehicle motion in realistic scenarios.

Predictable Mobility : A Statistical Approach for Planetary Surface Exploration Rovers in Uncertain Terrain

In this article, a statistical mobility prediction for planetary surface exploration rovers has been described. This method explicitly considers uncertainty of the terrain physical parameters via SRSM and employs models of both vehicle dynamics and wheel-terrain interaction mechanics. The simulation results of mobility prediction using three different techniques, SMC, LHSMC, and SRSM, confirms that SRSM significantly improves the computational efficiency compared with those conventional methods. The usefulness and validity of the proposed method has been confirmed through experimental studies of the slope traversal scenario in two different terrains. The results show that the predicted motion path with confidence ellipses can be used as a probabilistic reachability metric of the rover position. Also, for the slope-traversal case, terrain parameter uncertainty has a larger influence on the lateral motion of the rover than on longitudinal motion. Future directions of this study will apply the proposed technique to the path-planning problem. Here, confidence ellipses will be used to define collision-free areas, which will provide useful criteria for generating safe trajectories.

A stochastic response surface approach to statistical prediction of mobile robot mobility

The ability of autonomous or semi-autonomous mobile robots to rapidly and accurately predict their mobility characteristics is an important requirement for their use in unstructured environments. Most methods for mobility prediction, however, assume precise knowledge of environmental (i.e. terrain) properties. In practical conditions, significant uncertainty is associated with terrain parameter estimation from robotic sensors, and this uncertainty must be considered in a mobility prediction algorithm. Here a method for efficient mobility prediction based on the stochastic response surface approach is presented that explicitly considers terrain parameter uncertainty. The method is compared to a Monte Carlo-based method and simulations show that the stochastic response surface approach can be used for efficient, accurate prediction of mobile robot mobility.

Statistical mobility prediction for planetary surface exploration rovers in uncertain terrain

Planetary surface exploration rovers must accurately and efficiently predict their mobility on natural, rough terrain. Most approaches to mobility prediction assume precise a priori knowledge of terrain physical parameters, however in practical scenarios knowledge of terrain parameters contains significant uncertainty. In this paper, a statistical method for mobility prediction that incorporates terrain uncertainty is presented. The proposed method consists of two techniques: a wheeled vehicle model for calculating vehicle dynamic motion and wheel-terrain interaction forces, and a stochastic response surface method (SRSM) for modeling of uncertainty. The proposed method generates a predicted motion path of the rover with confidence ellipses indicating the probable rover position due to uncertainty in terrain physical parameters. Rover orientations and wheel slippage are also predicted. The computational efficiency of SRSM as compared to conventional Monte Carlo methods is shown via numerical simulations. Experimental results of rover travel over sloped terrain in two different uncertain terrains are presented that confirms the utility of the proposed mobility prediction method.

A Multi-Element generalized Polynomial Chaos approach to analysis of mobile robot dynamics under uncertainty

The ability of mobile robots to quickly and accurately analyze their dynamics is critical to their safety and efficient operation. In field conditions, significant uncertainty is associated with terrain and/or vehicle parameter estimates, and this must be considered in an analysis of robot motion. Here a Multi-Element generalized Polynomial Chaos (MEgPC) approach is presented that explicitly considers vehicle parameter uncertainty for long term estimation of robot dynamics. It is shown to be an improvement over the generalized Askey polynomial chaos framework as well as the standard Monte Carlo scheme, and can be used for efficient, accurate prediction of robot dynamics.

AN INTERACTIVE, PHYSICS-BASED UNMANNED GROUND VEHICLE SIMULATOR LEVERAGING OPEN SOURCE GAMING TECHNOLOGY: PROGRESS IN THE DEVELOPMENT AND APPLICATION OF THE VIRTUAL AUTONOMOUS NAVIGATION ENVIRONMENT (VANE) DESKTOP

It is widely recognized that simulation is pivotal to vehicle development, whether manned or unmanned. There are few dedicated choices, however, for those wishing to perform realistic, end-to-end simulations of unmanned ground vehicles (UGVs). The Virtual Autonomous Navigation Environment (VANE), under development by US Army Engineer Research and Development Center (ERDC), provides such capabilities but utilizes a High Performance Computing (HPC) Computational Testbed (CTB) and is not intended for on-line, real-time performance. A product of the VANE HPC research is a real-time desktop simulation application under development by the authors that provides a portal into the HPC environment as well as interaction with wider-scope semi-automated force simulations (e.g. OneSAF). This VANE desktop application, dubbed the Autonomous Navigation Virtual Environment Laboratory (ANVEL), enables analysis and testing of autonomous vehicle dynamics and terrain/obstacle interaction in real-time with the capability to interact within the HPC constructive geo-environmental CTB for high fidelity sensor evaluations. ANVEL leverages rigorous physics-based vehicle and vehicle-terrain interaction models in conjunction with high-quality, multimedia visualization techniques to form an intuitive, accurate engineering tool. The system provides an adaptable and customizable simulation platform that allows developers a controlled, repeatable testbed for advanced simulations. ANVEL leverages several key technologies not common to traditional engineering simulators, including techniques from the commercial video-game industry. These enable ANVEL to run on inexpensive commercial, off-the-shelf (COTS) hardware. In this paper, the authors describe key aspects of ANVEL and its development, as well as several initial applications of the system.

Mobility prediction for unmanned ground vehicles in uncertain environments

The ability of autonomous unmanned ground vehicles (UGVs) to rapidly and effectively predict terrain negotiability is a critical requirement for their use on challenging terrain. Most methods for assessing traversability, however, assume precise knowledge of vehicle and terrain properties. In practical applications, uncertainties are associated with the estimation of the vehicle/terrain parameters, and these uncertainties must be considered while determining vehicular mobility. Here a computationally inexpensive method for efficient mobility prediction based on the stochastic response surface (SRSM) approach is presented that considers imprecise knowledge of terrain and vehicle parameters while analyzing various metrics associated with UGV mobility. A conventional Monte Carlo method and the proposed response surface methodology have been applied to two simulated cases of mobility analysis, and it has been shown that the SRSM method is an efficient tool as compared to conventional Monte Carlo methods for the analysis of vehicular mobility in uncertain environments.

Statistical Approach to Mobility Prediction for Planetary Surface Exploration Rovers in Uncertain Terrain

Planetary surface exploration rovers must accurately and efficiently predict their mobility on natural, rough terrain. Most approaches to mobility prediction assume precise a priori knowledge of terrain physical parameters, however in practical scenarios knowledge of terrain parameters contains significant uncertainty. In this paper, a statistical method for mobility prediction that incorporates terrain uncertainty is presented. The proposed method consists of two techniques: a wheeled vehicle model for calculating vehicle dynamic motion and wheel-terrain interaction forces, and a stochastic response surface method (SRSM) for modeling of uncertainty. The proposed method generates a predicted motion path of the rover with confidence ellipses indicating the probable rover position due to uncertainty in terrain physical parameters. Rover orientations and wheel slippage are also predicted. The computational efficiency of SRSM as compared to conventional Monte Carlo methods is shown via numerical simulations. Experimental results of rover travel over sloped terrain in two different uncertain terrains are presented that confirms the utility of the proposed mobility prediction method.

Mode Transition and Intermittency in an Acoustically Uncoupled Lean Premixed Swirl-Stabilized Combustor

The prediction of dynamic instability remains an open and important issue in the development of gas turbine systems, particularly those constrained by emissions limitations. The existence and characteristics of dynamic instability are known to be functions of combustor geometry, flow conditions, and combustion parameters, but the form of dependence is not well understood. By modifying the acoustic boundary conditions, changes in flame and flow structure due to inlet parameters can be studied independent of the acoustic modes with which they couple. This paper examines the effect of equivalence ratio on the flame macrostructure — the relationship between the turbulent flame brush and the dominant flow structures — in an acoustically uncoupled environment. The flame brush is measured using CH* chemiluminescence, and the flow is interrogated using two-dimensional particle image velocimetry. We examine a range of equivalence ratios spanning three distinct macrostructures. The first macrostructure (ϕ = 0.550) is characterized by a diffuse flame brush confined to the interior of the inner recirculation zone. We observe a conical flame in the inner shear layer, continuing along the wall shear layer in the second macrostructure (ϕ = 0.600). The third macrostructure exhibits the same flame brush as the second, with an additional flame brush in the outer shear layer (ϕ = 0.650). Between the second and third macrostructures, we observe a regime in which the flame brush transitions intermittently between the two structures. We use dynamic mode decomposition on the PIV data to show that this transition event, which we call flickering, is linked to vorticity generated by the intermittent expansion of the outer recirculation zone as the flame jumps in and out of the outer shear layer. In a companion paper, we show how the macrostructures described in this paper are linked with dynamic instability [1].Copyright