Showing papers by "Swaroop Darbha published in 2021"

Lateral Control of an Autonomous and Connected Following Vehicle With Limited Preview Information

Abstract: Lateral control of an autonomous and connected vehicle (ACV), especially in emergency situations, is important from the safety viewpoint. In these situations, the trajectory to be followed by an ACV must either be planned in real-time (e.g., for a possible evasion maneuver if the obstacle to be avoided is detected) or be communicated from its preceding vehicle. Typically, the trajectory information is available to the following ACV in the form of GPS time samples. From the viewpoint of lateral control, the lateral velocity information is not readily available and the feedback structure must reflect this reality. In this paper, we develop a methodology to synthesize a lateral control algorithm for a following ACV in a two-vehicle platoon in two steps: 1) From the limited preview information of the trajectory to be tracked via samples of GPS way points, we estimate the radius of curvature of the trajectory using “least-square” estimation and 2) develop a fixed-structure feedback control scheme for following the predecessor by synthesizing the set of stabilizing gains corresponding to lateral position error, heading error and heading rate error. Numerical simulation and experimental results corroborate the effectiveness of the proposed schemes.

Optimal UAV Route Planning for Persistent Monitoring Missions

Abstract: This article addresses a persistent monitoring problem (PMP) that requires an unmanned aerial vehicle (UAV) to repeatedly visit $n$ targets of equal priority. The UAV has limited onboard fuel/charge and must be regularly serviced at a depot. Given a fixed number of visits, $k$ , for the UAV to the targets between successive services, the objective of the PMP is to determine an optimal sequence of visits such that the maximum time elapsed between successive visits to any target is minimized. This planning problem is a generalization of the traveling salesman problem and is NP-hard. We characterize the optimal solutions to this problem for different values of $k$ and develop algorithms that can compute the optimal solutions relatively fast. Numerical results are also presented to corroborate the performance of the proposed approach.

String Stability of Connected Vehicle Platoons Under Lossy V2V Communication

Abstract: Recent advances in vehicle connectivity have allowed formation of autonomous vehicle platoons for improved mobility and traffic throughput. In order to avoid a pile-up in such platoons, it is important to ensure platoon (string) stability, which is the focus of this work. As per conventional definition of string stability, the power (2-norm) of the spacing error signals should not amplify downstream in a platoon. But in practice, it is the infinity-norm of the spacing error signal that dictates whether a collision occurs. We address this discrepancy in the first part of our work, where we reconsider string stability from a safety perspective and develop an upper limit on the maximum spacing error in a homogeneous platoon as a function of the acceleration maneuver of the lead vehicle. In the second part of this paper, we extend our previous results by providing the minimum achievable time headway for platoons with two-predecessor lookup schemes experiencing burst-noise packet losses. Finally, we utilize throttle and brake maps to develop a longitudinal vehicle model and validate it against a Lincoln MKZ which is then used for numerical corroboration of the proposed time headway selection algorithms.

The next-best-view for workpiece localization in robot workspace

Abstract: Workpiece localization is the process of obtaining the location of a workpiece in a reference frame of a robotic workspace. The location (position and orientation) is represented by the transformation between a local frame associated with the workpiece and the specified reference frame in the workspace. In this work, we study the workpiece localization problem without the two commonly adopted restrictive assumptions: the data used to calculate the transformation is readily available and the correspondence between the data sets used for calculation is known. The goal is to automate the localization process starting from efficient data collection to determining the workpiece location in the workspace. We describe a strategy that includes the following aspects: predicting the correspondence between the measured data and the workpiece CAD model data; generating representative vectors that would aid in determining the next-best-view for collecting new information of the workpiece location; evaluating a search region to find the next sensor location that satisfies both the robot kinematics as well as sensor field-of-view constraints while giving the maximum view gain; and calculating the rigid body transformation from the local frame to the world frame to localize the workpiece. Numerical simulation and experimental results are presented and discussed for the proposed strategy.

An Approximation Algorithm for an Assisted Shortest Path Problem

Abstract: In this article, we introduce a cooperative path planning algorithm for a cardinal and a support robot where the cardinal robot is unable to traverse a subset of edges in a network until the support robot has first traversed them. This subset of edges represent paths in an environment that are initially unavailable to the cardinal robot and require the assistance of the support robot. A (2 + α)-approximation algorithm (where α is the supremum of the ratio of the travel time of the support robot versus the travel time of the cardinal robot) is presented for this problem and is applied to various types of networks in order to examine the quality of the solutions it produces. We then conclude by discussing some potential future work concerning variations of this problem.

Bounds on Optimal Revisit Times in Persistent Monitoring Missions with a Distinct \& Remote Service Station.

Abstract: Persistent monitoring missions require an up-to-date knowledge of the changing state of the underlying environment. UAVs can be gainfully employed to continually visit a set of targets representing tasks (and locations) in the environment and collect data therein for long time periods. The enduring nature of these missions requires the UAV to be regularly recharged at a service station. In this paper, we consider the case in which the service station is not co-located with any of the targets. An efficient monitoring requires the revisit time, defined as the maximum of the time elapsed between successive revisits to targets, to be minimized. Here, we consider the problem of determining UAV routes that lead to the minimum revisit time. The problem is NP-hard, and its computational difficulty increases with the fuel capacity of the UAV. We develop an algorithm to construct near-optimal solutions to the problem quickly, when the fuel capacity exceeds a threshold. We also develop lower bounds to the optimal revisit time and use these bounds to demonstrate (through numerical simulations) that the constructed solutions are, on an average, at most 0.01% away from the optimum.