This work deals with the problem of lateral control for Emergency Lane Change (ELC) maneuvers for a convoy of Autonomous and Connected Vehicles (ACVs). Typically, an ELC maneuver is triggered by emergency cues from the front or the end of convoy. From a safety viewpoint, connectivity of vehicles is essential for obtaining preview information of preceding vehicles; every following vehicle could additionally get the position information of its preceding vehicle or vehicles for controller synthesis. In this work, we propose a method to synthesize a lateral controller by using preview GPS data from the lead and immediately preceding vehicles to construct a target trajectory for the ego vehicle to track. We then compute the feedforward control and feedback error signals based on the target trajectory. Numerical and experimental results corroborate the effectiveness of this scheme in terms of suppressing lateral string instabilities, thereby preventing the amplification of crosstrack errors as the vehicles in a convoy execute an ELC maneuver.
Routing problems for unmanned vehicles are frequently encountered in civilian and military applications and have been studied extensively as a result. A routing problem of interest consists of constructing a tour that maximizes the total information gained over the course of the tour. Herein, we consider a version where information gain is represented by classification confidence at points of interest visited in the tour. The information gained at each point of interest is modeled using the Kullback-Leibler divergence (also referred to as mutual information) where the probability of correctly classifying the point of interest is taken to be time-dependent. A mixed-integer program (MIP) is formulated to model this problem and two standard heuristics (a modified two-step greedy algorithm and a standard 2-OPT algorithm) are combined in an attempt to produce high quality solutions. We run simulations with various conditions for the nature of the information gain and position of the points of interest. We show that combining these two heuristics produce near-optimal solutions in nearly all of the trials for up to 10 points of interest.
This study concerns a multi-UAV path planning problem in concert with a known ground vehicle path. The environment is given as a grid map, where each cell is either a target, an obstacle or contains nothing (neutral). Respectively, each cell has a reward that is either positive, negative, or zero. The team of UAVs has to visit as many targets as possible under a given time span or distance constraint, to maximize the collected reward. More formally, this problem is a generalization of the Orienteering Problem (OP) and is NP-Hard. In addition, the inclusion of obstacle avoidance and area coverage introduces additional complications that the current literature has not readily addressed. We propose using a greedy heuristic based on the A* algorithm, which involves three stages (selection, insertion, and post-processing) to solve this problem. A large scale problem instance is generated and the results are presented for different variations of our proposed algorithm. For large problems with thousand of nodes, our algorithm was able to provide a feasible solution to the proposed problem within few minutes of computation time on a standard laptop.
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