This paper develops an autonomous tethered aerial visual assistant for robot operations in unstructured or confined environments. Robotic tele-operation in remote environments is difficult due to lack of sufficient situational awareness, mostly caused by the stationary and limited field-of-view and lack of depth perception from the robot's onboard camera. The emerging state of the practice is to use two robots, a primary and a secondary that acts as a visual assistant to overcome the perceptual limitations of the onboard sensors by providing an external viewpoint. However, problems exist when using a tele-operated visual assistant: extra manpower, manually chosen suboptimal viewpoint, and extra teamwork demand between primary and secondary operators. In this work, we use an autonomous tethered aerial visual assistant to replace the secondary robot and operator, reducing human robot ratio from 2:2 to 1:2. This visual assistant is able to autonomously navigate through unstructured or confined spaces in a risk-aware manner, while continuously maintaining good viewpoint quality to increase the primary operator's situational awareness. With the proposed co-robots team, tele-operation missions in nuclear operations, bomb squad, disaster robots, and other domains with novel tasks or highly occluded environments could benefit from reduced manpower and teamwork demand, along with improved visual assistance quality based on trustworthy risk-aware motion in cluttered environments.
The ability to map challenging subarctic environments opens new horizons for robotic deployments in industries such as forestry, surveillance, and open-pit mining. In this paper, we explore the possibilities of large-scale lidar mapping in a boreal forest. Computational and sensory requirements with regards to contemporary hardware are considered as well. The lidar mapping is often based on the Simultaneous Localization and Mapping (SLAM) technique relying on pose graph optimization, which fuses the Iterative Closest Point (ICP) algorithm, Global Navigation Satellite System (GNSS) positioning, and Inertial Measurement Unit (IMU) measurements. To handle those sensors directly within the ICP minimization process, we propose an alternative approach of embedding external constraints. Furthermore, a novel formulation of a cost function is presented and cast into the problem of handling uncertainties from GNSS and lidar points. To test our approach, we acquired a large-scale dataset in the Forêt Montmorency research forest. We report on the technical problems faced during our winter deployments aiming at building 3D maps using our new cost function. Those maps demonstrate both global and local consistency over 4.1 km.
In this paper, we present a path planner for low-altitude terrain coverage in known environments with unmanned rotary-wing micro aerial vehicles (MAVs). Airborne systems can assist humanitarian demining by surveying suspected hazardous areas (SHAs) with cameras, ground-penetrating synthetic aperture radar (GPSAR), and metal detectors. Most available coverage planner implementations for MAVs do not consider obstacles and thus cannot be deployed in obstructed environments. We describe an open-source framework to perform coverage planning in polygon flight corridors with obstacles. Our planner extends boustrophedon coverage planning by optimizing over different sweep combinations to find the optimal sweep path, and considers obstacles during transition flights between cells. We evaluate the path planner on 320 synthetic maps and show that it is able to solve realistic planning instances fast enough to run in the field. The planner achieves 14% lower path costs than a conventional coverage planner. We validate the planner on a real platform where we show low-altitude coverage over a sloped terrain with trees.
This paper highlights the need for a new power model for skid-steered wheeled robots driving on loose soil, and lays the groundwork to develop such a model. State-of-the-art power modeling assumes hard ground; under typical assumptions, this predicts constant power consumption over a range of small turning radii where the inner wheels rotate backwards. However, experimental results performed both in the field and in a controlled laboratory show that, on sand, power is not in fact constant with respect to turning radius in this case. Power peaks in a newly identified range of turns where the inner wheels rotate backwards but are dragged forward. Data shows higher motor torque and wheel sinkage in this range. A skidding wheel that is sunk into loose soil bulldozes a pile of sand; initial modeling of this phenomenon reproduces the trend in additional power with respect to turning radius. As work on a full power model for loose soil continues, this work identifies turning radii to avoid whenever possible in practice.
While most autonomous driving efforts reported are directed for general driving and mainly on major roads, there are numerous applications for autonomous vehicles for last mile mobility—from person mobility and mail delivery to flexible recharging of cars in parking structures. Over the last year, we have designed vehicles for the micro-mobility challenge. Our approach was based on adoption of the open-source Autoware system. The system was taken as a starting point for the design of a robust solution. Proposed requirements include a robust control design, a shift toward increased use of image data over LiDAR data, handling of a richer set of vehicles/pedestrians in a last mile scenario, and overall system characterization and evaluation. We present an overview of the overall design and the design decisions for construction of vehicles for last-mile delivery.
