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• W2019968616 abstract "This article proposes a probabilistic approach to account for robot stability uncertainty when planing motions over uneven terrains. A novel probabilistic stability criterion derived from the cumulative distribution of a tip-over metric is introduced that allows a safety constraint to be dynamically updated by available sensor data as it becomes available. The proposed safety constraint authorizes the planner to generates more conservative motion plans for areas with higher levels of uncertainty, while avoids unnecessary caution in well-known areas.The proposed systematic approach is particularly applicable to reconfigurable robots that can assume safer postures when required, although is equally valid for fixed-configuration platforms to choose safer paths to follow. The advantages of planning with the proposed probabilistic stability metric are demonstrated with data collected from an indoor rescue arena, as well as an outdoor rover testing facility. I. MOTIVATION AND RELATED WORK One of the most difficult problems of navigation over unstructured and unforgiving environments is how to address the uncertainties emanating from imperfect actuators and poor environmental sensor information. Several approaches have been developed in the literature to deal with uncertainties in the input data and system model parameters. For instance, path following with uncertainty has been studied by the control community. A Kalman-based active observer controller for the path following of wheeled mobile robots subject to non-holonomic constraints is presented in [1]. The effect of external disturbances, general model errors, and uncertainties present in the system are reduced by adding an extra state (the “active state”) to the controller design. The effectiveness of the proposed path-following controller was evaluated via simulation results for a wheelchair robot following a straight line and a circular path. Path following controllers based on a Lyapunov feedback linearisation have also been proposed to make the controller robust to modelling uncertainty, e.g. for articulated manipulators where experimental results of a 4 DoF manipulator with revolute and linear actuated links were presented moving the endeffector along a circular path [2]. Other authors have looked at the problem of incorporating uncertainty at the planning stage, e.g. by considering variations in the 2.5D terrain elevation data and localisation errors, as described in [3] for an articulated wheeled mobile robot. A conservative path planning approach is adopted that considers terrain measurement uncertainty, where a set All authors are with the Faculty of Engineering and IT, University of Technology Sydney (UTS), Sydney NSW 2007, Australia. {mohammad.norouzi, jaime.vallsmiro, gamini.dissanayake, teresa.vidalcalleja}@uts.edu.au Fig. 1: The iRobot PackBot Explorer robot with a 1 DoF arm, pan-tilt sensor unit and two small front sub-tracks (flippers). of potential worst-case robot configurations at boundary locations in the terrain are examined to make sure that the vehicle would remain stable for a given variance in the elevation map. If any posture in this set is proven unstable, the corresponding location in the map will be regarded as untraversable. To address the localisation uncertainty for a given path, all points along the path within a distance proportional to the assumed robot localisation uncertainty are examined given all possible configurations. A point in the terrain would be considered as a feasible point for path finding purposes only if all configurations in the overall search have been proven to be stable. The output of this brute-force approach is a simple failure or success, with no concern for the probability of a tip-over instability. This article looks at the challenging problem of global path planning over ruggedised terrains by formally accounting for stability uncertainty in the process. A novel probabilistic stability metric based on the uncertainty analysis technique described in [4] is introduced. The proposed criterion is employed to progress the deterministic stable path planning strategy described in [5], proven to be particularly suitable for search and rescue missions, with the goal of improving robot navigation safety in scenarios where the model of the system and the sensory data available to the robot may be imperfect. In [5], the force angle (FA) stability measure [6] was employed to evaluate the stability of the rover along the path. The FA margin is deterministically defined by the position of the robot’s centre of mass (CM) and the contactpoint (CP) interaction with the terrain, which form a convex area called “support polygon” (SP). The main drawback of employing deterministic constant stability margins to path planning is that while producing safer paths with larger, more conservative stability margins, they may also easily end up being overly restrictive, filtering out many probable pathways, while on the other hand planning on the boundary of tip-over could easily jeopardise stability if uncertainties Fig. 2: The 3D FA stability measure for n = 4 and i = 3. (CM’s position has been shifted up and vectors scaled for easier visualization). The FA measure can be intuitively described as the effect of the forces over CM projected on the supporting convex area defined by the contact-points between the vehicle and terrain e.g. β3 = θ3 ‖d3‖ ‖f3‖. are present. The proposed probabilistic approach allows to search paths with a minimum “safety confidence” instead, so that model uncertainties can be taken into consideration when finding paths, instead of resorting to restrictive fixed minimum safe margins. The advantages of planning with probabilistic stability will be demonstrated with a model of the Packbot robot shown in Fig. 1 through comprehensive simulations in a mock-up Urban Search and Rescue (USAR) arena an data from a quasi-outdoor rover testing facility at the University of Toronto Institute for Aerospace Studies (UTIAS) [7]. II. OVERVIEW OF STABILITY ANALYSIS To anticipate the stability measure, works like [8] have considered an ideal support polygon (ISP) for the vehicle, i.e. contact points are assumed to be fixed under the sprockets of the robot. It has been illustrated in the literature [5] how this is a strong assumption for the case of highly unstructured terrains, where contact points can lay anywhere along the robot’s track and in general describe a variable support polygon (VSP). Hence, no ISP is assumed in the following sections. A. Force Angle Stability Metric The example in Fig. 2 describes the FA derivations for the third tip-over axis with a vehicle exhibiting four CPs in a given terrain configuration. The line joining two consecutive CPs (pi and pi+1) forms the tip-over axes ai. The shortest vector between the CM and each ai is referred to as li. The FA stability margin [6] takes into account di, the distance between the projected CM to the ith tip-over axis ai, and fi the component of the effective net force fr which acts about the axis ai. It also considers the angle θi between fi and the tip-over axis normal li. Assuming the SP is composed of n CPs, the FA stability measure about each ai can be formulated as βi = θi ‖di‖ ‖fi‖, i = {1, ...,n} (1) The final FA margin would be the minimum βi i.e. β = min(βi), i = {1, ...,n}." @default.
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• W2019968616 title "Path planning with stability uncertainty for articulated mobile vehicles in challenging environments" @default.
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