Ben Grocholsky

Information based adaptive robotic exploration

Exploration involving mapping and concurrent localization in an unknown environment is a pervasive task in mobile robotics. In general, the accuracy of the mapping process depends directly on the accuracy of the localization process. This paper address the problem of maximizing the accuracy of the map building process during exploration by adaptively selecting control actions that maximize localisation accuracy. The map building and exploration task is modeled using an Occupancy Grid (OG) with concurrent localisation performed using a feature-based Simultaneous Localisation And Mapping (SLAM) algorithm. Adaptive sensing aims at maximizing the map information by simultaneously maximizing the expected Shannon information gain (Mutual Information) on the OG map and minimizing the uncertainty of the vehicle pose and map feature uncertainty in the SLAM process. The resulting map building system is demonstrated in an indoor environment using data from a laser scanner mounted on a mobile platform.

Cooperative air and ground surveillance

Unmanned aerial vehicles (UAV) can be used to cover large areas searching for targets. However, sensors on UAVs are typically limited in their accuracy of localization of targets on the ground. On the other hand, unmanned ground vehicles (UGV) can be deployed to accurately locate ground targets, but they have the disadvantage of not being able to move rapidly or see through such obstacles as buildings or fences. In this paper, we describe how we can exploit this synergy by creating a seamless network of UAVs and UGVs. The keys to this are our framework and algorithms for search and localization, which are easily scalable to large numbers of UAVs and UGVs and are transparent to the specificity of individual platforms. We describe our experimental testbed, the framework and algorithms, and some results

Information-theoretic coordinated control of multiple sensor platforms

This paper describes an information-theoretic approach to distributed and coordinated control of a multi-robot sensor system. The approach is based on techniques long established for the related problem of decentralised data fusion (DDF). The DDF architecture uses information measures to communicate state estimates in a network of sensors. For coordinated control of robot sensors, the control objective becomes maximisation of these information measures. This yields platform trajectories, which maximise the total information, gained by the system. This approach inherits the many benefits of the DDF method including scalability, robustness to sub-system failure and addition, and interoperability among heterogeneous systems. The approach is applied to a practical bearings-only multi-feature localisation problem.

Adaptive Teams of Autonomous Aerial and Ground Robots for Situational Awareness

This is a preprint of an article accepted for publication in the Journal of Field Robotics, copyright 2007. Journal of Field Robotics 24(11), 991–1014 (2007)

Scalable control of decentralised sensor platforms

This paper describes an information-theoretic approach to decentralised and coordinated control of multi-robot sensor systems. It builds on techniques long established for the related problem of Decentralised Data Fusion (DDF). The DDF architecture uses information measures to communicate state estimates in a network of sensors. For coordinated control of robot sensors, the control objective becomes maximisation of these information measures. A decentralised coordinated control architecture is presented. The approach taken seeks to achieve scalable solutions that maintain consistent probabalistic sensor fusion and payoff formulations. It inherits the many benefits of the DDF method including scalability, seamless handling of sub-system activation and deactivation, and interoperability among heterogeneous units. These features are demonstrated through application to practical multi-feature localisation problems on a team of indoor robots equipped with laser range finders.

A Low-Cost, Redundant Inertial Measurement Unit for Unmanned Air Vehicles

This paper discusses the development of a low-cost, redundant, strapdown inertial measurement unit (IMU). The unit comprises four ceramic vibrating structure gyroscopes and four QLC 400 accelerometers configured on a truncated tetrahedron design. This design allows for the optimal configuration of the eight sensors, which in turn provides for a theoretical 33% increase in information. The redundant sensor configuration also allows for fault detection, which is required for many autonomous applications. This initiative is a precursor for future developments with more sensors to provide fault isolation. The paper will also present a navigation system implementing the redundant IMU with the global positioning system. Results are provided of this navigation system being implemented in an unmanned air vehicle.

Experiments in multirobot air-ground coordination

This paper addresses the problem of coordinating aerial and ground vehicles in tasks that involve exploration, identification of targets and maintaining a connected communication network. We focus on the problem of localizing vehicles in urban environments where GPS signals are often unreliable or unavailable. We first describe our multi-robot testbed and the control software used to coordinate ground and aerial vehicles. We present the results of experiments in air-ground localization analyzing three complementary approaches to determining the positions of vehicles on the ground. We show that the coordination of aerial vehicles with ground vehicles is necessary to get accurate estimates of the state of the system.

A decentralized architecture for Active Sensor Networks

The paper presents a decentralized approach to the solution of the distributed information gathering problem. The main design objectives are scalability with the number of network components, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. The results of two deployment scenarios on an indoor sensor network are presented.

Control for Localization of Targets using Range-only Sensors

We present an application of a novel framework and algorithms for: (1) conservatively and recursively incorporating information obtained through sensors that yield observations that are non-linear functions of the state; and (2) finding control inputs that continuously improve the quality of the resulting estimates. We present an experimental study of the application of our framework to mobile robots utilizing range-only sensors, and demonstrate its effectiveness in dealing with problems of target localization with one or more robots where traditional approaches involving linearization fail to consistently capture uncertainty.

Information Driven Coordinated Air-Ground Proactive Sensing

This paper concerns the problem of actively searching for and localizing ground features by a coordinated team of air and ground robotic sensor platforms. The approach taken builds on well known Decentralized Data Fusion (DDF) methodology. In particular, it brings together established representations developed for identification and linearized estimation problems to jointly address feature detection and localization. This provides transparent and scalable integration of sensor information from air and ground platforms. As in previous studies, an Information-theoretic utility measure and local control strategy drive the robots to uncertainty reducing team configurations. Complementary characteristics in terms of coverage and accuracy are revealed through analysis of the observation uncertainty for air and ground on-board cameras. Implementation results for a detection and localization example indicate the ability of this approach to scalably and efficiently realize such collaborative potential.