G Sajad Saeedi

Control and Navigation Framework for Quadrotor Helicopters

This paper presents the development of a nonlinear quadrotor simulation framework together with a nonlinear controller. The quadrotor stabilization and navigation problems are tackled using a nested loops control architecture. A nonlinear Backstepping controller is implemented for the inner stabilization loop. It asymptotically tracks reference attitude, altitude and heading trajectories. The outer loop controller generates the reference trajectories for the inner loop controller to reach the desired waypoint. To ensure boundedness of the reference trajectories, a PD controller with a saturation function is used for the outer loop. Due to the complexity involved in controller development and testing, a simulation framework has been developed. It is based on the Gazebo 3D robotics simulator and the Open Dynamics Engine (ODE) library. The framework can effectively facilitate the development and validation of controllers. It has been released and is available at Gazebo quadrotor simulator (2012).

Multiple-Robot Simultaneous Localization and Mapping: A Review

Simultaneous localization and mapping SLAM in unknown GPS-denied environments is a major challenge for researchers in the field of mobile robotics. Many solutions for single-robot SLAM exist; however, moving to a platform of multiple robots adds many challenges to the existing problems. This paper reviews state-of-the-art multiple-robot systems, with a major focus on multiple-robot SLAM. Various issues and problems in multiple-robot SLAM are introduced, current solutions for these problems are reviewed, and their advantages and disadvantages are discussed.

Neural Network-Based Multiple Robot Simultaneous Localization and Mapping

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 multistep 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.

Towards an Ontology for Autonomous Robots

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.

Multiple robot simultaneous localization and mapping

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.

Group Mapping: A Topological Approach to Map Merging for Multiple Robots

Simultaneous localization and mapping (SLAM) is required for mobile robots to be able to explore a prior unknown space without a global positioning reference. Multiple robots can achieve exploration tasks more quickly but with added complexity. A useful representation of the map for SLAM purposes is as an occupancy grid map. In the most general case of multiple-robot SLAM, 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 article, the generalized Voronoi diagram (GVD) is extended to encapsulate the probabilistic information encoded in the occupancy grid map. The new construct called the probabilistic GVD (PGVD) operates directly on occupancy grid maps and is used to determine the relative transformation between maps and fuse them. This approach has three major benefits over past methods: 1) it is effective at finding relative transformations quickly and reliably, 2) the uncertainty associated with transformations used to fuse the maps is accounted for, and 3) the parts of the maps that are more certain are preferentially used in the merging process because of the probabilistic nature of the PGVD.

Efficient map merging using a probabilistic generalized Voronoi diagram

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.

Map merging using hough peak matching

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.

An information gain based adaptive path planning method for an autonomous underwater vehicle using sidescan sonar

The majority of path planning research has focused on robots equipped with forward-facing sensors. Algorithms using cell decomposition and information gain are effective at planning paths through obstacle-laden environments, but have not been applied to robots with side-looking sensors whose goal is complete coverage. In addition, the assumptions made about the environment can often prove false, leading to poor mission plans being given by deliberative path planning methods. As a result, adaptive path planning methods which can change the vehicles path based on in situ measurements of the environment are needed. 9 In this paper, the information gain approach is extended to apply to adaptive path planning for an autonomous underwater vehicle (AUV) equipped with a sidescan sonar, where the goal is to achieve complete coverage of an area. A new regular exact hexagonal decomposition is used, which is shown to be particularly well suited to side-looking sensors. In addition, the concept of branch entropy in the directed acyclic graph is proposed to help the AUV achieve its global goals while keeping the path planning reactive, a task that is not possible with information gain alone. The results show that for high desired confidence thresholds, the new path planning method with branch entropy outperforms the more conventional information gain approach.

Requirements for building an ontology for autonomous robots

IEEE Ontologies for Robotics and Automation Working Group were divided into subgroups that were in charge of studying industrial robotics, service robotics and autonomous robotics. This paper aims to present the work in-progress developed by the autonomous robotics (AuR) subgroup. This group aims to extend the core ontology for robotics and automation to represent more specific concepts and axioms that are commonly used in autonomous robots.,For autonomous robots, various concepts for aerial robots, underwater robots and ground robots are described. Components of an autonomous system are defined, such as robotic platforms, actuators, sensors, control, state estimation, path planning, perception and decision-making.,AuR has identified the core concepts and domains needed to create an ontology for autonomous robots.,AuR targets to create a standard ontology to represent the knowledge and reasoning needed to create autonomous systems that comprise robots that can operate in the air, ground and underwater environments. The concepts in the developed ontology will endow a robot with autonomy, that is, endow robots with the ability to perform desired tasks in unstructured environments without continuous explicit human guidance.,Creating a standard for knowledge representation and reasoning in autonomous robotics will have a significant impact on all R&A domains, such as on the knowledge transmission among agents, including autonomous robots and humans. This tends to facilitate the communication among them and also provide reasoning capabilities involving the knowledge of all elements using the ontology. This will result in improved autonomy of autonomous systems. The autonomy will have considerable impact on how robots interact with humans. As a result, the use of robots will further benefit our society. Many tedious tasks that currently can only be performed by humans will be performed by robots, which will further improve the quality of life. To the best of the authors’knowledge, AuR is the first group that adopts a systematic approach to develop ontologies consisting of specific concepts and axioms that are commonly used in autonomous robots.