Sivakumar Rathinam

Vision-based road-following using a small autonomous aircraft

This paper describes the vision-based control of a small autonomous aircraft following a road. The computer vision system detects natural features of the scene and tracks the roadway in order to determine relative yaw and lateral displacement between the aircraft and the road. Using only the vision measurements and onboard inertial sensors, a control strategy stabilizes the aircraft and follows the road. The road detection and aircraft control strategies have been verified by hardware in the loop (HIL) simulations over long stretches (several kilometers) of straight roads and in conditions of up to 5 m/s of prevailing wind. Hardware experiments have also been conducted using a modified radio-controlled aircraft. Successful road following was demonstrated over an airfield runway under variable lighting and wind conditions. The development of vision-based control strategies for unmanned aerial vehicles (UAVs), such as the ones presented here, enables complex autonomous missions in environments where typical navigation sensor like GPS are unavailable.

A Resource Allocation Algorithm for Multivehicle Systems With Nonholonomic Constraints

This paper is about the allocation of tours of m targets to n vehicles. The motion of the vehicles satisfies a nonholonomic constraint (i.e., the yaw rate of the vehicle is bounded). Each target is to be visited by one and only one vehicle. Given a set of targets and the yaw rate constraints on the vehicles, the problem addressed in this paper is 1) to assign each vehicle a sequence of targets to visit, and 2) to find a feasible path for each vehicle that passes through the assigned targets with a requirement that the vehicle returns to its initial position. The heading angle at each target location may not be specified. The objective function is to minimize the sum of the distances traveled by all vehicles. A constant factor approximation algorithm is presented for the above resource allocation problem for both the single and the multiple vehicle case. Note to Practitioners-The motivation for this paper stems from the need to develop resource allocation algorithms for unmanned aerial vehicles (UAVs). Small autonomous UAVs are seen as ideal platforms for many applications, such as searching for targets, mapping a given area, traffic surveillance, fire monitoring, etc. The main advantage of using these small autonomous vehicles is that they can be used in situations where a manned mission is dangerous or not possible. Resource allocation problems naturally arise in these applications where one would want to optimally assign a given set of vehicles to the tasks at hand. The feature that differentiates these resource allocation problems from similar problems previously studied in the literature is that there are constraints on the motion of the vehicle. This paper addresses the constraint that captures the inability of a fixed wing aircraft to turn at any arbitrary yaw rate. The basic problem addressed in this paper is as follows: Given n vehicles and m targets, find a path for each vehicle satisfying yaw rate contraints such that each target is visited exactly once by a vehicle and the total distance traveled by all vehicles is minimized. We assume that the targets are at least 2r apart, where r is the minimum turning radius of the vehicle. This is a reasonable assumption because the sensors on these vehicles can map or see an area whose width is at least 2r. We give an algorithm to solve this problem by combining ideas from the traveling salesman problem and the path planning literature. We also show how these algorithms perform in the worst-case scenario

Autonomous Searching and Tracking of a River using an UAV

Surveillance operations include inspecting and monitoring river boundaries, bridges and coastlines. An autonomous unmanned aerial vehicle (UAV) can decrease the operational costs, expedite the monitoring process and be used in situations where a manned inspection is not possible. This paper addresses the problem of searching and mapping such littoral boundaries using an autonomous UAV based on visual feedback. Specifically, this paper describes an exploration system that equips a fixed wing UAV to autonomously search a given area for a specified structure (could be a river, a coastal line etc.), identify the structure if present and map the coordinates of the structure based on the images from the onboard sensor(could be vision or near infra-red). Experimental results with a fixed wing UAV searching and mapping the coordinates of a 2 mile stretch of a river with a cross track error of around 9 meters are presented.

Vision Based Following of Locally Linear Structures using an Unmanned Aerial Vehicle

Inspecting and monitoring oil-gas pipelines, roads, bridges, power generation grids is very important in ensuring the reliability and life expectancy of these civil systems. An autonomous UAV can decrease the operational costs, expedite the monitoring process and be used in situations where manned inspection is not possible. This paper addresses the problem of monitoring these systems using an autonomous unmanned aerial vehicle (UAV) which follows the locally linear structures using visual feedback.

An approximation algorithm for a symmetric Generalized Multiple Depot, Multiple Travelling Salesman Problem

In this paper, we present an algorithm with an approximation factor of 2 for a Generalized, Multiple Depot, Multiple Travelling Salesman Problem (GMTSP) when the costs are symmetric and satisfy the triangle inequality. The algorithm requires finding a degree constrained minimum spanning tree which we compute using a Lagrangian relaxation.

A Modular Software Infrastructure for Distributed Control of Collaborating UAVs

Collaborating unmanned aerial vehicles can e‐ciently perform surveillance, mapping, and other tasks without human risk. Currently deployed unmanned aerial vehicles demonstrate a need for increased autonomy and cooperation. We present a UAV software architecture and hardware platform that have demonstrated single-user control of a ∞eet of aircraft, distributed task assignment, and vision-based navigation. A modular software infrastructure has been developed to coordinate distributed control, communications, and vision-based control. Along with the onboard control architecture, a set of user interfaces has been developed to allow a single user to e‐ciently control the ∞eet of aircraft. Distributed and vision-based control are enabled by powerful onboard computing capability and an aircraft-to-aircraft ad-hoc wireless network. Custom modiflcations to the Sig Rascal airframe are required to support this capability, including reinforcement and vibration isolation. We describe original elements of the system that provide unique capabilities for collaboration, followed by results of a ∞ight demonstration.

An architecture for UAV team control

Abstract Recent years has seen a widespread interest in the use of Unmanned aircraft vehicles for military applications. These UAVs can be used in many applications such as surveillance, information gathering, suppression of enemy defenses, air to air combat, mapping buildings and facilities etc. In this paper, we present an architecture with the necessary algorithms that we have implemented to control a team of UAVs to search for targets such as SAMs, ground troops, artillery, tanks etc in a given region.

Lower and Upper Bounds for a Multiple Depot UAV Routing Problem

This paper extends the well known Held-Karps lower bound available for a single travelling salesman problem to the following multiple depot UAV routing problem (MDURP): Given a collection of UAVs that start at different depots, a set of terminals and destinations, the problem is to choose paths for each of the UAVs so that (1) each UAV starts at its respective depot, visits atleast one destination and reaches any one of the terminals not visited by other UAVs; (2) each destination is visited by exactly one UAV; and (3) the cost of the paths is a minimum among all possible paths for the UAVs. The criteria for the cost of paths considered is the total cost of the edges travelled by the entire collection. This MDURP is formulated as a minimum cost constrained forest problem subject to side constraints. By dualizing the side constraints, one can obtain an infinite family of lower bounds for MDURP. Each lower bound can be computed in a tractable way using a matroid intersection algorithm. Also, when the costs of traveling between any two locations satisfy triangle inequality, it is shown that there exists a 2-approximation algorithm for solving the MDURP

A Mixed Integer Linear Program for Solving a Multiple Route Taxi Scheduling Problem

Aircraft movements on taxiways at busy airports often create bottlenecks. This paper introduces a mixed integer linear program to solve a Multiple Route Aircraft Taxi Scheduling Problem. The outputs of the model are in the form of optimal taxi schedules, which include routing decisions for taxiing aircraft. The model extends an existing single route formulation to include routing decisions. An efficient comparison framework compares the multi-route formulation and the single route formulation. The multi-route model is exercised for east side airport surface traffic at Dallas/Fort Worth International Airport to determine if any arrival taxi time savings can be achieved by allowing arrivals to have two taxi routes: a route that crosses an active departure runway and a perimeter route that avoids the crossing. Results indicate that the multi-route formulation yields reduced arrival taxi times over the single route formulation only when a perimeter taxiway is used. In conditions where the departure aircraft are given an optimal and fixed takeoff sequence, accumulative arrival taxi time savings in the multi-route formulation can be as high as 3.6 hours more than the single route formulation. If the departure sequence is not optimal, the multi-route formulation results in less taxi time savings made over the single route formulation, but the average arrival taxi time is significantly decreased.

Heuristics for Routing Heterogeneous Unmanned Vehicles with Fuel Constraints

This paper addresses a multiple depot, multiple unmanned vehicle routing problem with fuel constraints. The objective of the problem is to find a tour for each vehicle such that all the specified targets are visited at least once by some vehicle, the tours satisfy the fuel constraints, and the total travel cost of the vehicles is a minimum. We consider a scenario where the vehicles are allowed to refuel by visiting any of the depots or fuel stations. This is a difficult optimization problem that involves partitioning the targets among the vehicles and finding a feasible tour for each vehicle. The focus of this paper is on developing fast variable neighborhood descent (VND) and variable neighborhood search (VNS) heuristics for finding good feasible solutions for large instances of the vehicle routing problem. Simulation results are presented to corroborate the performance of the proposed heuristics on a set of 23 large instances obtained from a standard library. These results show that the proposed VND heuristic, on an average, performed better than the proposed VNS heuristic for the tested instances.