Robert Fitch

Online Localization of Radio-Tagged Wildlife with an Autonomous Aerial Robot System

The application of autonomous robots to efficiently locate small wildlife species has the potential to provide significant ecological insights not previously possible using traditional landbased survey techniques, and a basis for improved conservation policy and management. We present an approach for autonomously localizing radio-tagged wildlife using a small aerial robot. We present a novel two-point phased array antenna system that yields unambiguous bearing measurements and an associated uncertainty measure. Our estimation and informationbased planning algorithms incorporate this bearing uncertainty to choose observation points that improve confidence in the location estimate. These algorithms run online in real time and we report experimental results that show successful autonomous localization of stationary radio tags and live radio-tagged birds.

Distributed control for unit-compressible robots: goal-recognition, locomotion, and splitting

We present a distributed self-reconfiguring robot system with unit-compressible modules called the Crystal robot. A new design for the Crystal is presented that decouples the x axis and y axis actuation, has on-board sensing and has neighbor-to-neighbor communication. We also describe a suite of distributed control algorithms for this type of robot and associated experiments for each algorithm. Several of the algorithms presented are instantiations of generic distributed algorithms for self-reconfiguring robots. Specifically, we present an algorithm for distributed goal recognition, two new distributed locomotion algorithms designed for unit-compressible actuation and a new generic-division algorithm. We also present the integration of a locomotion algorithm with distributed goal recognition, allowing the robot to reconfigure and recognize the achievement of its goal, all without the use of a central controller. For all of these algorithms, we describe the implementation, sketch correctness analysis and present experimental data. Our experiments empirically verify the usefulness of our distributed algorithms on a self-reconfiguring system.

Reconfiguration planning for heterogeneous self-reconfiguring robots

Current research in self-reconfiguring robots focuses predominantly on systems of identical modules. However, allowing modules of varying types, with different sensors, for example, is of practical interest. In this paper, we propose the development of an algorithmic basis for heterogeneous self-reconfiguring systems. We demonstrate algorithmic feasibility by presenting O(n/sup 2/) time centralized and O(n/sup 3/) time decentralized solutions to the reconfiguration problem for n non-identical modules. As our centralized time bound is equal to the best published homogeneous solution, we argue that space, as opposed to time, is the critical resource in the reconfiguration problem. Our results encourage the development both of applications that use heterogeneous self-reconfiguration, and also heterogeneous hardware systems.

Online decentralized information gathering with spatial–temporal constraints

We are interested in coordinating a team of autonomous mobile sensor agents in performing a cooperative information gathering task while satisfying mission-critical spatial–temporal constraints. In particular, we present a novel set of constraint formulations that address inter-agent collisions, collisions with static obstacles, network connectivity maintenance, and temporal-coverage in a resource-efficient manner. These constraints are considered in the context of the target search problem, where the team plans trajectories that maximize the probability of target detection. We model constraints continuously along the agents’ trajectories and integrate these constraint models into decentralized team planning using a computationally efficient solution method based on the Lagrangian formulation and decentralized optimization. We validate our approach in simulation with five UAVs performing search, and through hardware experiments with four indoor mobile robots. Our results demonstrate team planning with spatial–temporal constraints that preserves the performance of unconstrained information gathering and is feasible to implement with reasonable computational and communication resources.

Model-based design: a report from the trenches of the DARPA Urban Challenge

The impact of model-based design on the software engineering community is impressive, and recent research in model transformations, and elegant behavioral specifications of systems has the potential to revolutionize the way in which systems are designed. Such techniques aim to raise the level of abstraction at which systems are specified, to remove the burden of producing application-specific programs with general-purpose programming. For complex real-time systems, however, the impact of model-driven approaches is not nearly so widespread. In this paper, we present a perspective of model-based design researchers who joined with software experts in robotics to enter the DARPA Urban Challenge, and to what extent model-based design techniques were used. Further, we speculate on why, according to our experience and the testimonies of many teams, the full promises of model-based design were not widely realized for the competition. Finally, we present some thoughts for the future of model-based design in complex systems such as these, and what advancements in modeling are needed to motivate small-scale projects to use model-based design in these domains.

Distributed goal recognition algorithms for modular robots

Modular robots are systems composed of a number of independent units that can be reconfigured to fit the task at hand. When the modules are computationally independent, they form a large distributed system with no central controller. We are concerned with the ability of such modular robots to easily recognize the achievement (or lack thereof) of a given goal configuration. We present algorithms for a class of 2D and 3D modular robots, along with correctness and running time analysis. We have successfully implemented the 2D algorithm on the second-generation Crystalline Atomic robot, a self-reconfigurable modular robot under development in our laboratory and we present implementation details and experimental results.

Decentralized Coordinated Tracking with Mixed Discrete–Continuous Decisions

In this paper, we study the problem of dynamically positioning a team of mobile robots for target tracking. We treat the coordination of mobile robots for target tracking as a joint team optimization to minimize uncertainty in target state estimates over a fixed horizon. The optimization is inherently a function of both the positioning of robots in continuous space and the assignment of robots to targets in discrete space. Thus, the robot team must make decisions over discrete and continuous variables. In contrast to methods that decouple target assignments and robot positioning, our approach avoids the strong assumption that a robots utility for observing a target is independent of other robots’ observations. We formulate the optimization as a mixed integer nonlinear program and apply integer relaxation to develop an approximate solution in decentralized form. We demonstrate our coordinated multirobot tracking algorithm both in simulation and using a pair of mobile robotic sensor platforms to track moving pedestrians. Our results show that coupling target assignment and robot positioning realizes coordinated behaviors that are not possible with decoupled methods.

Energy-constrained motion planning for information gathering with autonomous aerial soaring

Autonomous aerial soaring presents a unique opportunity to extend the flight duration of Unmanned Aerial Vehicles (UAVs). In this paper, we examine the problem of a gliding UAV searching for a ground target while simultaneously collecting energy from known thermal energy sources. The problem is posed as a tree search problem by noting that a long-duration mission can be divided into similar segments of flying between and climbing in thermals. The algorithm attempts to maximise the probability of detecting a target by exploring a tree of the possible thermal-to-thermal transitions to a fixed search depth and executing the highest utility plan. The sensitivity of the algorithm to different search depths is explored, and the method is compared against a locally-optimal myopic search algorithm. In larger, more complicated problems, the suggested method outperforms myopic search by sacrificing short-term utility to reach more valuable exploration areas later in the mission.

Vision-based Obstacle Detection and Navigation for an Agricultural Robot

This paper describes a vision-based obstacle detection and navigation system for use as part of a robotic solution for the sustainable intensification of broad-acre agriculture. To be cost-effective, the robotics solution must be competitive with current human-driven farm machinery. Significant costs are in high-end localization and obstacle detection sensors. Our system demonstrates a combination of an inexpensive global positioning system and inertial navigation system with vision for localization and a single stereo vision system for obstacle detection. The paper describes the design of the robot, including detailed descriptions of three key parts of the system: novelty-based obstacle detection, visually-aided guidance, and a navigation system that generates collision-free kinematically feasible paths. The robot has seen extensive testing over numerous weeks of field trials during the day and night. The results in this paper pertain to one particular 3 h nighttime experiment in which the robot performed a coverage task and avoided obstacles. Additional results during the day demonstrate that the robot is able to continue operating during 5 min GPS outages by visually following crop rows.

3D rectilinear motion planning with minimum bend paths

Computing rectilinear shortest paths in two dimensions has been solved optimally using a number of different techniques. A variety of related problems have been solved, including minimizing the number of bends in the path, the total rectilinear distance, or some combination of both. However, solutions to the 3D versions of these problems are less common. We propose a solution to the 3D minimum-bend path problem, which has theoretical as well as practical interest. Applications include motion planning problems where straight line motion is preferred over taking arbitrary turns. We employ our results in motion planning for self-repair in self-reconfigurable robots.