RaNDT SLAM: Radar SLAM Based on Intensity-Augmented Normal Distributions Transform

Rescue robotics sets high requirements to perception algorithms due to the unstructured and potentially vision-denied environments. Pivoting Frequency-Modulated Continuous Wave radars are an emerging sensing modality for SLAM in this kind of environment. However, the complex noise characteristics of radar SLAM makes, particularly indoor, applications computationally demanding and slow. In this work, we introduce a novel radar SLAM framework, RaNDT SLAM, that operates fast and generates accurate robot trajectories. The method is based on the Normal Distributions Transform augmented by radar intensity measures. Motion estimation is based on fusion of motion model, IMU data, and registration of the intensity-augmented Normal Distributions Transform. We evaluate RaNDT SLAM in a new benchmark dataset and the Oxford Radar RobotCar dataset. The new dataset contains indoor and outdoor environments besides multiple sensing modalities (LiDAR, radar, and IMU).

Exploring Capability-Based Control Distributions of Human-Robot Teams Through Capability Deltas: Formalization and Implications

The implicit assumption that human and autonomous agents have certain capabilities is omnipresent in modern teaming concepts. However, none formalize these capabilities in a flexible and quantifiable way. In this paper, we propose Capability Deltas, which establish a quantifiable source to craft autonomous assistance systems in which one agent takes the leader and the other the supporter role. We deduct the quantification of human capabilities based on an established assessment and documentation procedure from occupational inclusion of people with disabilities. This allows us to quantify the delta, or gap, between a team's current capability and a requirement established by a work process. The concept is then extended to the multi-dimensional capability space, which then allows to formalize compensation behavior and assess required actions by the autonomous agent.

Matching Input and Output Devices and Physical Disabilities for Human-Robot Workstations

As labor shortage is rising at an alarming rate, it is imperative to enable all people to work, particularly people with disabilities and elderly people. Robots are often used as universal tool to assist people with disabilities. However, for such human-robot workstations universal design fails. We mitigate the challenges of selecting an individualized set of input and output devices by matching devices required by the work process and individual disabilities adhering to the Convention on the Rights of Persons with Disabilities passed by the United Nations. The objective is to facilitate economically viable workstations with just the required devices, hence, lowering overall cost of corporate inclusion and during redesign of workplaces. Our work focuses on developing an efficient approach to filter input and output devices based on a person's disabilities, resulting in a tailored list of usable devices. The methodology enables an automated assessment of devices compatible with specific disabilities defined in International Classification of Functioning, Disability and Health. In a mock-up, we showcase the synthesis of input and output devices from disabilities, thereby providing a practical tool for selecting devices for individuals with disabilities.

Situational Adaptive Motion Prediction for Firefighting Squads in Indoor Search and Rescue

Firefighting is a complex, yet low automated task. To mitigate ergonomic and safety related risks on the human operators, robots could be deployed in a collaborative approach. To allow human-robot teams in firefighting, important basics are missing. Amongst other aspects, the robot must predict the human motion as occlusion is ever-present. In this work, we propose a novel motion prediction pipeline for firefighters' squads in indoor search and rescue. The squad paths are generated with an optimal graph-based planning approach representing firefighters' tactics. Paths are generated per room which allows to dynamically adapt the path locally without global re-planning. The motion of singular agents is simulated using a modification of the headed social force model. We evaluate the pipeline for feasibility with a novel data set generated from real footage and show the computational efficiency.