This paper presents a sound source localization strategy that relies on a microphone array embedded in an unmanned ground vehicle and an asynchronous close-talking microphone near the operator. A signal coarse alignment strategy is combined with a time-domain acoustic echo cancellation algorithm to estimate a time-frequency ideal ratio mask to isolate the target speech from interferences and environmental noise. This allows selective sound source localization, and provides the robot with the direction of arrival of sound from the active operator, which enables rich interaction in noisy scenarios. Results demonstrate an average angle error of 4 degrees and an accuracy within 5 degrees of 95% at a signal-to-noise ratio of 1dB, which is significantly superior to the state-of-the-art localization methods.

This paper presents a comprehensive leader-follower system designed to address the multifaceted challenges of real-world human-robot following scenarios, emphasizing defense applications. Our approach integrates perception, motion planning, and control into a unified framework. By embracing a curvilinear planning domain and treating the leader-follower problem as a teach-and-repeat-style path-following task, we facilitate high-quality formation control for large-scale robotic vehicles. Extensive field trials, conducted across 2 km of driving in an unstructured urban environment, underscore the system’s reliability and adaptability across a spectrum of formation types, even in the presence of difficult leader path singularities.

We extend the behaviour of generic sample-based motion planners to support obstacle avoidance during long-range path following by introducing a new edge-cost metric paired with a curvilinear planning space. The resulting planner generates naturally smooth paths that avoid local obstacles while minimizing lateral path deviation to best exploit prior terrain knowledge from the reference path. In this adaptation, we explore the nuances of planning in the curvilinear configuration space and describe a mechanism for natural singularity handling to improve generality. We then shift our focus to the trajectory generation problem, proposing a novel Model Predictive Control (MPC) architecture to best exploit our path planner for improved obstacle avoidance. Through rigorous field robotics trials over 5 km, we compare our approach to the more common direct path-tracking MPC method and discuss the promise of these techniques for reliable long-term autonomous operations.

We propose a novel low-complexity lidar gesture recognition system for mobile robot control robust to gesture variation. Our system uses a modular approach, consisting of a pose estimation module and a gesture classifier. Pose estimates are predicted from lidar scans using a Convolutional Neural Network trained using an existing stereo-based pose estimation system. Gesture classification is accomplished using a Long Short-Term Memory network and uses a sequence of estimated body poses as input to predict a gesture. Breaking down the pipeline into two modules reduces the dimensionality of the input, which could be lidar scans, stereo imagery, or any other modality from which body keypoints can be extracted, making our system lightweight and suitable for mobile robot control with limited computing power. The use of lidar contributes to the robustness of the system, allowing it to operate in most outdoor conditions, to be independent of lighting conditions, and for input to be detected 360 degrees around the robot. The lidar-based pose estimator and gesture classifier use data augmentation and automated labeling techniques, requiring a minimal amount of data collection and avoiding the need for manual labeling. We report experimental results for each module of our system and demonstrate its effectiveness by testing it in a real-world robot teleoperation setting.

Recent military research has focused on how militaries can effectively employ technology in mega-cities deemed Contested Urban Environments (CUE). This paper looks at aspects of the CUE environment and discusses how autonomous systems may be used to gain a tactical advantage. Recent experiments at The Technical Cooperation Program (TTCP) CUE 18 experiment, held in Montreal, looked at the use of autonomous systems, among other technologies, in roles such as surveillance, mapping, sensor deployment, etc. A key focus of the work was to improve the situational awareness of the soldiers at the tactical edge. Employment and delivery of unmanned assets at the tactical edge demands robustness to communication and GPS loss, ease of use, and full-scale integration with larger command and control structures to ensure maximum effectiveness of the assets. Several systems were deployed at the CUE Montreal trial to test these environmental stressors. For instance, UAVs carrying vision and lidar-based sensors were used to produce detail 3D maps of the environment without relying on GPS information. A novel concept for deploying Unattended Ground Sensor (UGS) from a UAV was developed and used to deploy the sensors to strategic locations. Data from the unmanned vehicles and the UGS were shared through the use of the Open Standards for Unattended Sensors (OSUS) and accessible to both the Canadian and allied Common Operating Picture (COP) being tested at the trial. The remainder of this paper discusses the results of the CUE Montreal experimentation and uses the results to suggest future directions of autonomous research in CUE environments.

Redundant navigation systems are critical for safe operation of UAVs in high-risk environments. Since most commercial UAVs almost wholly rely on GPS, jamming, interference and multi-pathing are real concerns that usually limit their operations to low-risk environments and Visual Line-Of-Sight. This paper presents a vision-based route-following system for the autonomous, safe return of UAVs under primary navigation failure such as GPS jamming. Using a Visual Teach & Repeat framework to build a visual map of the environment during an outbound flight, we show the autonomous return of the UAV by visually localising the live view to this map when a simulated GPS failure occurs, controlling the vehicle to follow the safe outbound path back to the launch point. Using gimbal-stabilised stereo vision alone, without reliance on external infrastructure or inertial sensing, visual odometry and localisation are achieved at altitudes of 5-25 m and flight speeds up to 55 km/h. We examine the performance of the visual localisation algorithm under a variety of conditions and also demonstrate closed-loop autonomy along a complicated 450 m path.

A time-optimal speed schedule results in a mobile robot driving along a planned path at or near the limits of the robot's capability. However, deriving models to predict the effect of increased speed can be very difficult. In this paper, we present a speed scheduler that uses previous experience, instead of complex models, to generate time-optimal speed schedules. The algorithm is designed for a vision-based, path-repeating mobile robot and uses experience to ensure reliable localization, low path-tracking errors, and realizable control inputs while maximizing the speed along the path. To our knowledge, this is the first speed scheduler to incorporate experience from previous path traversals in order to address system constraints. The proposed speed scheduler was tested in over 4 km of path traversals in outdoor terrain using a large Ackermann-steered robot travelling between 0.5 m/s and 2.0 m/s. The approach to speed scheduling is shown to generate fast speed schedules while remaining within the limits of the robot's capability.

This paper describes a multi-sensor appearance-based place recognition system suitable for robotic mapping. Unlike systems that extract features from visual imagery only, here we apply the well known Bag-of-Words approach to features extracted from both visual and range sensors. By applying this technique to both sensor streams simultaneously we can overcome the deficiencies of each individual sensor. We show that lidar-based place recognition using a generative model learnt from Variable Dimensional Local Shape Descriptors can be used to perform place recognition regardless of lighting conditions or large changes in orientation, including traversing loops backward. Likewise, we are still able to exploit the feature rich place recognition that visual systems provide. Using a pose verification system we are able to effectively discard false positive loop detections. We present experimental results that highlight the strength of our approach and investigate alternative techniques for combining the results from the individual sensor streams. The multi-sensor approach enables the two sensors to complement each other well in large urban and rural environments under variable lighting conditions.

A key component in the emerging localization and mapping paradigm is an appearance-based place recognition algorithm that detects when a place has been revisited. This algorithm can run in the background at a low frame rate and be used to signal a global geometric mapping algorithm when a loop is detected. An optimization technique can then be used to correct the map by 'closing the loop'. This allows an autonomous unmanned ground vehicle to improve localization and map accuracy and successfully navigate large environments. Image-based place recognition techniques lack robustness to sensor orientation and varying lighting conditions. Additionally, the quality of range estimates from monocular or stereo imagery can decrease the loop closure accuracy. Here, we present a lidar-based place recognition system that is robust to these challenges. This probabilistic framework learns a generative model of place appearance and determines whether a new observation comes from a new or previously seen place. Highly descriptive features called the Variable Dimensional Local Shape Descriptors are extracted from lidar range data to encode environment features. The range data processing has been implemented on a graphics processing unit to optimize performance. The system runs in real-time on a military research vehicle equipped with a highly accurate, 360 degree field of view lidar and can detect loops regardless of the sensor orientation. Promising experimental results are presented for both rural and urban scenes in large outdoor environments.

Navigating unstructured environments requires reliable perception that generates an appropriate world representation. This representation must encompass all types of impediments to traversal, whether they be insurmountable obstacles, or mobility inhibitors such as soft soil. Traditionally, traversability and obstacle avoidance have represented separate capabilities with individual rangefinders dedicated to each task. This paper presents a statistical technique that, through the analysis of the underlying 21=2 D terrain map, determines the probability of an obstacle. This integrated approach eliminates the need for multiple data sources and is applicable to range data from various sources, including laser rangefinders and stereo vision. The proposed obstacle detection technique has been tested in simulated environments and under real world conditions, and these experiments revealed that it accurately identifies obstacles.

Unmanned systems are designed to reduce risk and magnify the impact of manned forces. Expanding unmanned involvement in military operations will require greater vehicle autonomy and the adoption of new concepts of operations. This paper discusses the technical challenges of unmanned systems in support of dismounted operations and research efforts by Defence R&D Canada to support unmanned vehicles in this role. New projects have been formulated to address these technical challenges.

The Multi-Agent Tactical Sentry Unmanned Ground Vehicle, developed at Defence R&D Canada - Suffield, has been in service with the Canadian Forces for five years. This tele-operated wheeled vehicle provides a capability for point detection of chemical, biological, radiological, and nuclear agents. Based on user experience, it is obvious that a manipulator capability would greatly enhance the vehicle's utility and increase its mobility in urban terrain. This paper details technical components of this development, and describes a number of trials undertaken to perform tasks with a manipulator arm such as picking up objects, opening vehicle and building doors, recording video, and creating 3D models of the environment. The lessons learned from these trials will guide further development of the technology.

The classical engineering fields have evolved standards and techniques for developing complex systems. For example, both mechanical and electrical engineers have a wide variety of standard components, with defined capabilities, that they can draw upon (e.g., gears, transistors) in the design of complex systems. On the other hand, software engineering has struggled with the basic idea of reusability. Software engineering approaches, such as the use of components that promote the concept of information hiding and the introduction of structured programming languages, offer a roadmap to an improved software reuse. Unfortunately, their adoption by robotics researchers has been slow, impeded by the tradition of individual research groups crafting independent and incompatible solutions to common problems.

We present a novel perception system for mapping of indoor/outdoor environments with an Unmanned GroundVehicle (UGV). The system uses image classification techniques to determine the operational environment of theUGV (indoor or outdoor). Based on the classification results, the appropriate mapping system is then deployed.Image features are extracted from video imagery andused to train a classification function using supervisedlearning techniques. This classification function is thenused to classify new imagery. A perception module observesthe classification results and switches the UGV's perception system, according to current needs and available (reliable) data as the UGV transitions from indoors to outdoors or vice versa. A terrain map that exploits GPS and Inertial Measurement Unit (IMU) data is used when operatingoutdoors, while a 2D laser based Simultaneous Localization and Mapping (SLAM) technique is used when operating indoors. Globally consistent maps are generated bytransforming the indoor map data into the global referenceframe, a capability unique to this algorithm.

Abstract : Researchers in the field of robotics have been seeking methods to both control and monitor their vehicles. Unfortunately the programs they have developed to perform these tasks are normally dependent on the robotic software infrastructure or are very difficult to understand for an outside user. This paper looks to tackle the problem of monitoring and controlling a robotics system using a web browser. The goal of this paper is to describe the potential for a system that will control and monitor a CORBA based robotics framework from a simple HTTP based browser.

Unmanned vehicles (UxV) operate in numerous environments, with air, ground and marine representing the majority of the implementations. All unmanned vehicles, when traversing unknown space, have similar requirements. They must sense their environment, create a world representation, and, then plan a path that safely avoids obstacles and hazards. Traditionally, each unmanned vehicle class used environment specific assumptions to create a unique world representation that was tailored to it operating environment. Thus, an unmanned aerial vehicle (UAV) used the simplest possible world representation, where all space above the ground plane was free of obstacles. Conversely, an unmanned ground vehicle (UGV) required a world representation that was suitable to its complex and unstructured environment. Such a clear cut differentiation between UAV and UGV environments is no longer valid as UAVs have migrated down to elevations where terrestrial structures are located. Thus, the operating environment for a low flying UAV contains similarities to the environments experienced by UGVs. As a result, the world representation techniques and algorithms developed for UGVs are now applicable to UAVs, since low flying UAVs must sense and represent its world in order to avoid obstacles. Defence R&D Canada (DRDC) conducts research and development in both the UGV and UAV fields. Researchers have developed a platform neutral world representation, based upon a uniform 21/2-D elevation grid, that is applicable to many UxV classes, including aerial and ground vehicles. This paper describes DRDC's generic world representation, known as the Global Terrain map, and provides an example of unmanned ground vehicle implementation, along with details of it applicability to aerial vehicles.