Feedback Control For Rob ot Formation Maneuvers

This paper develops control strategies for moving multiple-agents in formation, using a virtual structure. The controls are specifically applied to robots. by introducing feedback from the followers to the coordinating mechanism, the robots are shown to better coordinate their motion. Hardware results are presented

A Testbed Architecture for MAGICC Applications

A testbed is developed for testing Multi-AGent Intelligent Coordination and Control (MAGICC). The robots act as agents which make decisions and act upon them via classical control theory. The testbed consists of sensors( camera and encoders), embedded controllers, modems and a computer network which are integrated into a hierarchical control which has decision makers, controllers, estimators and predictors. The testbed has successfully been applied to initializing the robots into geometric formation. Simulation and Hardware results are presented

Path Planning in the Local-Level Frame for Small Unmanned Aircraft Systems

In this chapter, we propose a 3D path planning algorithm for small unmanned aircraft systems (UASs). We develop the path planning logic using a body fixed relative coordinate system which is the unrolled, unpitched body frame. In this relative coordinate system, the ownship is fixed at the center of the coordinate system, and the detected intruder is located at a relative position and moves with a relative velocity with respect to the ownship. This technique eliminates the need to translate the sensor’s measurements from local coordinates to global coordinates, which saves computation cost and removes the error introduced by the transformation. We demonstrate and validate this approach using predesigned encounter scenarios in the Matlab/Simulink environment

Spacecraft Formation Flying Via Dynamic Compensation

In this paper we present a passivity based control for spacecraft formation flying. We derive both attitude and position control. The controls do not require velocity information. They only require information about the position and attitude of the given robot and two of its neighbors. Furthermore, our approach has the advantage that we provide convergence results and establish bounds on the formation error

Decentralized Scheme for Spacecraft Formation Flying via the Virtual Structure Approach

this paper. Following a decentralized coordination architecture via the virtual structure approach, decentralized formation control strategies are introduced, which are appropriate when a large number of spacecraft are involved and/or stringent inter-spacecraft communication limitations are exerted. The e#ectiveness of the proposed control strategies is demonstrated through simulation result

A Passivity-Based Approach to Spacecraft Formation Flying

In this paper we present a generalized coupled dynamics approach to formation flying. The approach is completely passivity-based. It requires no velocity information. It is decentralized in that very little communication among spacecraft is required

Semi-autonomous human-UAV interfaces for fixed-wing mini-UAVs

Abstract-We present several human-robot interfaces that support real-time control of a small semi-autonomous UAV. These interfaces are designed for searching tasks and other missions that typically do not have a precise predetermined flight plan. We present a detailed analysis of a PDA interface and describe how our other interfaces relate to this analysis. We then offer quantative and qualitative performance comparisons of the interfaces, as well as an analysis of their possible real-world applications

Vision Based Navigation and Tracking with Small UAVs

Presented on November 1, 2017 from 12:15 p.m.-1:15 p.m. in the Marcus Nanotechnology Building, Rooms 1116-1118, Georgia Tech.Randal W. Beard received a B.S. degree in electrical engineering from the University of Utah, Salt Lake City in 1991, an M.S. degree in electrical engineering in 1993, an M.S. degree in mathematics in 1994, and a Ph.D. degree in electrical engineering in 1995, all from Rensselaer Polytechnic Institute. Since 1996, Beard has worked in the Electrical and Computer Engineering Department at Brigham Young University in Provo, Utah, where he is currently a professor. In 1997 and 1998, he was a Summer Faculty Fellow at the Jet Propulsion Laboratory at the California Institute of Technology in Pasadena, Calif. In 2006 and 2007, he was a visiting research fellow at the Air Force Research Laboratory, Munitions Directorate at Eglin AFB in Florida. Beard's primary research focus is autonomous control of small air vehicles and multivehicle coordination and control. He is a past associate editor for the IEEE Transactions on Automatic Control, the IEEE Control Systems Magazine, and the Journal of Intelligent and Robotic Systems. He is a fellow of the IEEE and an associate fellow of AIAA.Runtime: 58:43 minutesThis talk will describe our current work on vision-based autonomous navigation and tracking using small UAVs and provide an overview of two ongoing projects. The first project is relative navigation in GPS degraded environments. There are many applications where GPS is either restricted or denied. We have developed an architecture that uses a relative front end to navigate relative to key frames, and then opportunistically uses GPS measurements and SLAM-style loop closures in a back-end process to provide global context. We will show some recent flight results that demonstrate robustness to GPS failure and degradation. The second project we will discuss is robust tracking of multiple ground-based targets from an airborne platform. We will present a new multiple target tracking algorithm that is based on the random sample consensus (RANSAC) algorithm widely used in computer vision. A recursive version of the RANSAC algorithm will be discussed, and its extension to tracking multiple dynamic objects will be explained. The performance of R-RANSAC will be compared to state of the art target tracking algorithms in the context of problems that are relevant to UAV applications

Multiple UAV cooperative search under collision avoidance and limited range communication constraints

This paper focuses on the problem of cooperatively searching, using a team of unmanned air vehicles (UAVs), an area of interest that contains regions of opportunity and regions of potential hazard. The objective of the UAV team is to visit as many opportunities as possible, while avoiding as many hazards as possible. To enable cooperation, the UAVs are constrained to stay within communication range of one another. Collision avoidance is also required. Algorithms for teamoptimal and individually-optimal/team-suboptimal solutions are developed and their computational complexity compared. Simulation results demonstrating the feasibility of the cooperative search algorithms are presented.