Howard Li

KER Journal 2020 Journal Article

A review and comparison of ontology-based approaches to robot autonomy – ADDENDUM

• Alberto Olivares-Alarcos
• Daniel Beßler
• Alaa Khamis
• Paulo Goncalves
• Maki K. Habib
• Julita Bermejo-Alonso
• Marcos Barreto
• Mohammed Diab

KER Journal 2020 Journal Article

Ontologies for cloud robotics

• Edison Pignaton de Freitas
• Julita Bermejo-Alonso
• Alaa Khamis
• Howard Li
• Joanna Isabelle Olszewska

Abstract Cloud robotics (CR) is currently a growing area in the robotic community. Indeed, the use of cloud computing to share data and resources of distributed robotic systems leads to the design and development of cloud robotic systems (CRS) which constitute useful technologies for a wide range of applications such as smart manufacturing, aid and rescue missions. However, in order to get coherent agent-to-cloud communications and efficient agent-to-agent collaboration within these CRS, there is a need to formalize the knowledge representation in CR. Hence, the use of ontologies provides a mean to define formal concepts and their relations in an interoperable way. This paper presents standard robotic ontologies and their extension in the CR domain as well as their possible implementations in the case of a real-world CR scenario.

KER Journal 2019 Journal Article

A review and comparison of ontology-based approaches to robot autonomy

• Alberto Olivares-Alarcos
• Daniel Beßler
• Alaa Khamis
• Paulo Goncalves
• Maki K. Habib
• Julita Bermejo-Alonso
• Marcos Barreto
• Mohammed Diab

Abstract Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.

KER Journal 2019 Journal Article

Ontologies for Industry 4.0

• Veera Ragavan Sampath Kumar
• Alaa Khamis
• Sandro Fiorini
• Joel Luís Carbonera
• Alberto Olivares Alarcos
• Maki Habib
• Paulo Goncalves
• Howard Li

Abstract The current fourth industrial revolution, or ‘Industry 4.0’ (I4.0), is driven by digital data, connectivity, and cyber systems, and it has the potential to create impressive/new business opportunities. With the arrival of I4.0, the scenario of various intelligent systems interacting reliably and securely with each other becomes a reality which technical systems need to address. One major aspect of I4.0 is to adopt a coherent approach for the semantic communication in between multiple intelligent systems, which include human and artificial (software or hardware) agents. For this purpose, ontologies can provide the solution by formalizing the smart manufacturing knowledge in an interoperable way. Hence, this paper presents the few existing ontologies for I4.0, along with the current state of the standardization effort in the factory 4.0 domain and examples of real-world scenarios for I4.0.

IROS Conference 2016 Conference Paper

Optimal search strategies for pollutant source localization

• Behzad Bayat
• Naveena Crasta
• Howard Li
• Auke Jan Ijspeert

This paper is aimed at developing optimal motion planning for a single autonomous surface vehicle (ASV) equipped with an on-board pollutant sensor that will maximize the sensor-related information available for source seeking. The ASV uses a nonlinear diffusion model of the pollutant source to estimate the intensity/level of the pollution at the present ASV location. The rate of detection of particles depends on the relative distance between the ASV and the source. First, we use a probabilistic map of the source location built through the sensor information for a dynamic motion planning of source seeking based on an entropy reduction formulation, where an appropriately defined Fisher information matrix (FIM) is used for entropy reduction or information gain. We derive the FIM for the set-up and investigate optimal trajectories. Next, we present an online nonlinear Monte Carlo algorithm that uses the obtained sensor information about pollutant at different vehicle locations to update a probabilistic uncertainty map of pollutant source location. As the mission unfolds the ASV motion is computed by considering a moving-horizon interval of decision, which will allow for the inclusion of new information available for optimal motion planning. The proposed motion planning approach is extended to take into account external disturbances and it is able to minimize the uncertainty in the pollutant source. Finally, we provide two case studies to demonstrate efficacy of the proposed motion planning algorithm.

IROS Conference 2015 Conference Paper

A hybrid approach for multiple-robot SLAM with particle filtering

• Sajad Saeedi 0001
• Michael Trentini
• Howard Li

In this paper, a hybrid algorithm for multiple-robot SLAM is proposed that combines the advantages of particle filtering and map merging. The proposed algorithm does not rely on rendezvous and calculates the unknown relative poses from the local maps of the robots. As another contribution, the uncertainty of the relative poses is taken into account by propagating the uncertainty to the past and future information using a novel algorithm. Moreover, once the relative poses are known, the integration of the information from all robots is performed using a novel batch-mode algorithm, which is a fast and efficient approach to deal with the time complexity problem. The experimental results show the effectiveness of the proposed algorithms.

ICRA Conference 2014 Conference Paper

Area coverage planning that accounts for pose uncertainty with an AUV seabed surveying application

• Liam Paull
• Mae L. Seto
• Howard Li

This paper presents an overview of our research on accounting for robot pose uncertainty in area coverage applications. In the vast majority of existing literature on robotics area coverage, the location uncertainty of the robot is not considered. An uncertain robot pose results in an uncertain sensor swath, which in turn creates uncertainty about the achieved coverage. Here, we present a general framework where pose estimates are mapped through the coverage sensor model to obtain a probability of coverage over the discretized workspace. This probabilistic representation can then be used to adaptively plan paths for coverage based on an entropy reduction formulation. This framework is particularly well-suited to autonomous underwater vehicles (AUVs) performing seabed surveying operations. The AUV position estimate diverges from the actual AUV position while submerged due to the lack of a global position reference. This discrepancy can result in parts of the seabed being missed, which is unacceptable in safety-critical missions such as mine countermeasures. The proposed information-based path planning approach is able to guarantee area coverage even in the case of severe AUV position estimate drift. In-water experiments with an AUV show the effectiveness of the method.

IROS Conference 2012 Conference Paper

An IEEE standard Ontology for Robotics and Automation

• Craig Schlenoff
• Edson Prestes
• Raj Madhavan 0001
• Paulo J. S. Gonçalves
• Howard Li
• Stephen Balakirsky
• Thomas R. Kramer
• Emilio Miguelanez

This article discusses a newly formed IEEE-RAS working group entitled Ontologies for Robotics and Automation (ORA). The goal of this working group is to develop a standard ontology and associated methodology for knowledge representation and reasoning in robotics and automation, together with the representation of concepts in an initial set of application domains. The standard provides a unified way of representing knowledge and provides a common set of terms and definitions, allowing for unambiguous knowledge transfer among any group of humans, robots, and other artificial systems. In addition to describing the goal and structure of the group, this article gives some examples of how the ontology, once developed, can be used by applications such as industrial kitting.

IROS Conference 2012 Conference Paper

Efficient map merging using a probabilistic generalized Voronoi diagram

• Sajad Saeedi 0001
• Liam Paull
• Michael Trentini
• Mae L. Seto
• Howard Li

Simultaneous Localization and Mapping, or SLAM, is required for mobile robots to be able to explore prior unknown space without a global positioning reference. While multiple robots can achieve the exploration task more quickly, this benefit comes with the cost of added complexity. Probabilistic occupancy grid maps from multiple agents must be merged in real-time without any prior knowledge of their relative transformation. In addition, the probabilistic information of the maps must be accounted for and fused accordingly. In this paper, a probabilistic version of the Generalized Voronoi Diagram (GVD), called the PGVD, is used to determine the relative transformation between maps and fuse them. The new method is effective for finding relative transformations quickly and reliably. In addition, the novel approach accounts for all map uncertainties in the fusion process.

IROS Conference 2012 Conference Paper

Map merging using hough peak matching

• Sajad Saeedi 0001
• Liam Paull
• Michael Trentini
• Mae L. Seto
• Howard Li

One of the major problems for multi-robot SLAM is that the robots only know their positions in their own local coordinate frames, so fusing map data can be challenging. In this research, the mapping process is extended to multiple robots with a novel occupancy grid map fusion algorithm. Map fusion is achieved by transforming individual maps into the Hough space where they are represented in an abstract form. Properties of the Hough transform are used to find the common regions in the maps, which are then used to calculate the unknown transformation between the maps. Results are shown from tests performed on benchmark data sets and real-world experiments with multiple robotic platforms.

IROS Conference 2012 Conference Paper

Sensor driven online coverage planning for autonomous underwater vehicles

• Liam Paull
• Sajad Saeedi 0001
• Mae L. Seto
• Howard Li

At present, autonomous underwater vehicle (AUV) mine countermeasure (MCM) surveys are pre-planned by operators using ladder or zig-zag paths. Such surveys are often conducted with side-looking sonar sensors whose performance is dependant on a number of environment factors, as well as lateral range from the AUV track. This research presents a sensor driven online approach to seabed coverage for MCM. A method is presented where paths are planned adaptively using a multi-objective optimization. Information theory is combined with a new concept coined branch entropy based on a hexagonal cell decomposition. The result is a planning algorithm that often produces shorter paths than conventional means and is also capable of accounting for environmental factors detected in situ. Hardware-in-the-loop simulations and in water trials conducted on the IVER2 AUV show the effectiveness of the proposed method.

IROS Conference 2012 Conference Paper

Towards an Ontology for Autonomous Robots

• Liam Paull
• Gaëtan Séverac
• Guilherme V. Raffo
• Julian Mauricio Angel
• Harold Boley
• Phillip J. Durst
• Wendell Gray
• Maki K. Habib

The IEEE RAS Ontologies for Robotics and Automation Working Group is dedicated to developing a methodology for knowledge representation and reasoning in robotics and automation. As part of this working group, the Autonomous Robots sub-group is tasked with developing ontology modules for autonomous robots. This paper describes the work in progress on the development of ontologies for autonomous systems. For autonomous systems, the focus is on the cooperation, coordination, and communication of multiple unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and autonomous underwater vehicles (AUVs). The ontologies serve as a framework for working out concepts of employment with multiple vehicles for a variety of operational scenarios with emphasis on collaborative and cooperative missions.

IROS Conference 2011 Conference Paper

Multiple robot simultaneous localization and mapping

• Sajad Saeedi 0001
• Liam Paull
• Michael Trentini
• Howard Li

In this research, a decentralized platform for SLAM with multiple robots has been developed. An EKF-based single-robot SLAM is extended to multiple-robot SLAM with a novel occupancy grid map fusion algorithm. Map fusion is achieved through a multi-step process that includes image preprocessing, segmentation, cross correlation, approximating the relative transformation matrix, tuning of the transformation through the Radon image transform and similarity index, and then verification of the result using either map entropy or a verification index. Results are shown from tests performed in a real environment with multiple robotic platforms.

IROS Conference 2011 Conference Paper

Neural network-based multiple robot Simultaneous Localization and Mapping

• Sajad Saeedi 0001
• Liam Paull
• Michael Trentini
• Howard Li

In this paper, a decentralized platform for Simultaneous Localization and Mapping (SLAM) with multiple robots is developed. A novel occupancy grid map fusion algorithm is proposed. Map fusion is achieved through a multi-step process that includes image pre-processing, map learning, relative transformation extraction and then verification of the results. The proposed map learning method is a process based on the Self Organizing Map (SOM). In the learning phase, the obstacles of the map are learned by clustering the occupied cells of the map. The clusters represent the spatial form of the map and make further analyses of the map easier and faster. Also, clusters can be interpreted as features extracted from the occupancy grid map so the map fusion problem becomes a task of matching features. Results of the experiments from tests performed on a real environment with multiple robots prove the effectiveness of the proposed solution.