Morgan Quigley

Autonomous Vehicle Technologies for Small Fixed-Wing UAVs

Autonomous unmanned air vehicle ∞ight control systems require robust path generation to account for terrain obstructions, weather, and moving threats such as radar, jammers, and unfriendly aircraft. In this paper, we outline a feasible, hierarchal approach for real-time motion planning of small autonomous flxed-wing UAVs. The approach divides the trajectory generation into four tasks: waypoint path planning, dynamic trajectory smoothing, trajectory tracking, and low-level autopilot compensation. The waypoint path planner determines the vehicle’s route without regard for the dynamic constraints of the vehicle. This results in a signiflcant reduction in the path search space, enabling the generation of complicated paths that account for pop-up and dynamically moving threats. Kinematic constraints are satisfled using a trajectory smoother which has the same kinematic structure as the physical vehicle. The third step of the approach uses a novel tracking algorithm to generate a feasible state trajectory that can be followed by a standard autopilot. Monte-Carlo simulations were done to analyze the performance and feasibility of the approach and determine real-time computation requirements. A planar version of the algorithm has also been implemented and tested in a low-cost micro-controller. The paper describes a custom UAV built to test the algorithms.

Supporting Wilderness Search and Rescue using a Camera-Equipped Mini UAV

Wilderness Search and Rescue (WiSAR) entails searching over large regions in often rugged remote areas. Because of the large regions and potentially limited mobility of ground searchers, WiSAR is an ideal application for using small (human-packable) unmanned aerial vehicles (UAVs) to provide aerial imagery of the search region. This paper presents a brief analysis of the WiSAR problem with emphasis on practical aspects of visual-based aerial search. As part of this analysis, we present and analyze a generalized contour search algorithm, and relate this search to existing coverage searches. Extending beyond laboratory analysis, lessons from field trials with search and rescue personnel indicated the immediate need to improve two aspects of UAV-enabled search: How video information is presented to searchers and how UAV technology is integrated into existing WiSAR teams. In response to the first need, three computer vision algorithms for improving video display presentation are compared; results indicate that constructing temporally localized image mosaics is more useful than stabilizing video imagery. In response to the second need, a goal-directed task analysis of the WiSAR domain was conducted and combined with field observations to identify operational paradigms and field tactics for coordinating the UAV operator, the payload operator, the mission manager, and ground searchers.

Target Acquisition, Localization, and Surveillance Using a Fixed-Wing Mini-UAV and Gimbaled Camera

Target acquisition and continuous surveillance using fixed-wing UAVs is a difficult task due to the many degrees of freedom inherent in aircraft and gimbaled cameras. Mini-UAVs further complicate the problem by introducing severe restrictions on the size and weight of electro-optical sensor assemblies. We present a field-tested mini-UAV gimbal mechanism and flightpath generation algorithm as well as a human-UAV interaction scheme in which the operator manually flies the UAV to produce an estimate of the target position, then allows the aircraft to fly itself and control the gimbal while the operator re fines or moves the target position as required.

Efficient, generalized indoor WiFi GraphSLAM

The widespread deployment of wireless networks presents an opportunity for localization and mapping using only signal-strength measurements. The current state of the art is to use Gaussian process latent variable models (GP-LVM). This method works well, but relies on a signature uniqueness assumption which limits its applicability to only signal-rich environments. Moreover, it does not scale computationally to large sets of data, requiring O (N3) operations per iteration. We present a GraphSLAM-like algorithm for signal strength SLAM. Our algorithm shares many of the benefits of Gaussian processes, yet is viable for a broader range of environments since it makes no signature uniqueness assumptions. It is also more tractable to larger map sizes, requiring O (N2) operations per iteration. We compare our algorithm to a laser-SLAM ground truth, showing it produces excellent results in practice.

High-accuracy 3D sensing for mobile manipulation: Improving object detection and door opening

High-resolution 3D scanning can improve the performance of object detection and door opening, two tasks critical to the operation of mobile manipulators in cluttered homes and workplaces. We discuss how high-resolution depth information can be combined with visual imagery to improve the performance of object detection beyond what is (currently) achievable with 2D images alone, and we present door-opening and inventory-taking experiments.

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

We present several human-UAV 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-based 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.

Grasping novel objects with depth segmentation

We consider the task of grasping novel objects and cleaning fairly cluttered tables with many novel objects. Recent successful approaches employ machine learning algorithms to identify points on the scene that the robot should grasp. In this paper, we show that the task can be significantly simplified by using segmentation, especially with depth information. A supervised localization method is employed to select graspable segments. We also propose a shape completion and grasp planner method which takes partial 3D information and plans the most stable grasping strategy. Extensive experiments on our robot demonstrate the effectiveness of our approach.

Task Switching and Multi-Robot Teams

Determining whether it is possible for a single human to manage a team of multiple robots is an important question given current trends in robotics. Restricting attention to managing a team of multiple robots where a single human must be able to analyze video from each robot, we review how neglect time and interaction time of the interface-robot system provide a test for feasibility of a team. We then present a feasibility test that is applicable if the cost of switching attention between multiple robots or multiple tasks can become prohibitive. We then establish that switch costs can be high, and show that different tasks impose different switch costs.

Sub-meter indoor localization in unmodified environments with inexpensive sensors

The interpretation of uncertain sensor streams for localization is usually considered in the context of a robot. Increasingly, however, portable consumer electronic devices, such as smartphones, are equipped with sensors including WiFi radios, cameras, and inertial measurement units (IMUs). Many tasks typically associated with robots, such as localization, would be valuable to perform on such devices. In this paper, we present an approach for indoor localization exclusively using the low-cost sensors typically found on smartphones. Environment modification is not needed. We rigorously evaluate our method using ground truth acquired using a laser range scanner. Our evaluation includes overall accuracy and a comparison of the contribution of individual sensors. We find experimentally that fusion of multiple sensor modalities is necessary for optimal performance and demonstrate sub-meter localization accuracy.

A low-cost compliant 7-DOF robotic manipulator

We present the design of a new low-cost series-elastic robotic arm. The arm is unique in that it achieves reasonable performance for the envisioned tasks (backlash-free, sub-3mm repeatability, moves at 1.5m/s, 2kg payload) but with a significantly lower parts cost than comparable manipulators. The paper explores the design decisions and tradeoffs made in achieving this combination of price and performance. A new, human-safe design is also described: the arm uses stepper motors with a series-elastic transmission for the proximal four degrees of freedom (DOF), and non-series-elastic robotics servos for the distal three DOF. Tradeoffs of the design are discussed, especially in the areas of human safety and control bandwidth. The arm is used to demonstrate pancake cooking (pouring batter, flipping pancakes), using the intrinsic compliance of the arm to aid in interaction with objects.