Michael Trentini

PAW: a hybrid wheeled-leg robot

This paper discusses current wheeled mobility work on a hybrid wheeled-leg robot called PAW. In addition to providing design details, controllers are proposed for inclined turning and sprawled braking which take advantage of the hybrid nature of the platform and improve stability. Power consumption values for a number of its basic behaviours are given, as is the range of the robot

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.

Bounding with Active Wheels and Liftoff Angle Velocity Adjustment

The bounding gait for the Platform for Ambulating Wheels (PAW), a new and unique hybrid wheeled—leg system is presented. Two hypotheses are tested and discussed: first, that the robot’s forward speed can be increased by increasing the leg liftoff angles and, second, that the addition of distally mounted actuated wheels can be used in running gaits such as the bound. Both hypotheses were tested experimentally and found to be valid.

Multi-Camera Tracking and Mapping for Unmanned Aerial Vehicles in Unstructured Environments

Pose estimation for small unmanned aerial vehicles has made large improvements in recent years, leading to vehicles that use a suite of sensors to navigate and explore various environments. In particular, cameras have become popular due to their low weight and power consumption, as well as the large amount of data they capture. However, processing this data to extract useful information has proved challenging, as the pose estimation problem is inherently nonlinear and, depending on the cameras’ field of view, potentially ill-posed. Results from the field of multi-camera egomotion estimation show that these issues can be reduced or eliminated by using multiple cameras positioned appropriately. In this work, we make use of these insights to develop a multi-camera visual pose estimator using ultra wide angle fisheye cameras, leading to a system that has many advantages over traditional visual pose estimators. The system is tested in a variety of configurations and flight scenarios on an unprepared urban rooftop, including landings and takeoffs. To our knowledge, this is the first time a visual pose estimator has been shown to be able to continuously track the pose of a small aerial vehicle throughout the landing and subsequent takeoff maneuvers.

Bounding Gait in a Hybrid Wheeled-Leg Robot

This paper discusses the first implementation of a dynamically stable bounding gait on a hybrid wheeled-leg robot. Design of the robot is reviewed and the controllers which allow this mode of mobility to occur are discussed. Experimental results demonstrating the key dynamic characteristics of the gait, including footfall patterns, are given. The hypothesis that varying leg takeoff angles can lead to regulation of forward speed of the bounding gait is presented and verified. In addition, comparisons are made between the bounding gait which uses active wheel control and bounding which uses passive mechanical blocking of the wheels

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.

A software framework for multi-agent control of multiple autonomous underwater vehicles for underwater mine counter-measures

In this study, a novel robot control framework is presented for multiple autonomous underwater vehicles. In this framework, we incorporate sonar sensor data and integrated navigation system position data in a simulation environment, called UNBeatable-Sim, where complex control behaviors can be executed and analyzed. UNBeatable-Sim is developed by the COllaboration Based Robotics and Automation (COBRA) research group at the University of New Brunswick, Canada. Range and pose sensor data are accumulated in an ocean environment constructed using seabed data collected at Bedford Basin, Nova Scotia, Canada by DRDC Atlantic. A seabed map is generated from the real-world data using UNBeatable-Sim. The underwater vehicle and the seabed are simulated and visualized using OpenGL. An external controller implemented using Matlab and Simulink is used to control the robot model. Simulations of multiple underwater vehicles to navigate in the ocean environment to sense and map the seabed are performed using UNBeatable-Sim to assess the system architecture and controller performance.