Aerial and Ground Robot Collaboration for Autonomous Mapping in Search and Rescue Missions

Abstract

Humanitarian Crisis scenarios typically require immediate rescue intervention. In many cases, the conditions at a scene may be prohibitive for human rescuers to provide instant aid, because of hazardous, unexpected, and human threatening situations. These scenarios are ideal for autonomous mobile robot systems to assist in searching and even rescuing individuals. In this study, we present a synchronous ground-aerial robot collaboration approach, under which an Unmanned Aerial Vehicle (UAV) and a humanoid robot solve a Search and Rescue scenario locally, without the aid of a commonly used Global Navigation Satellite System (GNSS). Specifically, the UAV uses a combination of Simultaneous Localization and Mapping and OctoMap approaches to extract a 2.5D occupancy grid map of the unknown area in relation to the humanoid robot. The humanoid robot receives a goal position in the created map and executes a path planning algorithm in order to estimate the FootStep navigation trajectory for reaching the goal. As the humanoid robot navigates, it localizes itself in the map while using an adaptive Monte-Carlo Localization algorithm by combining local odometry data with sensor observations from the UAV. Finally, the humanoid robot performs visual human body detection while using camera data through a Darknet pre-trained neural network. The proposed robot collaboration scheme has been tested under a proof of concept setting in an exterior GNSS-denied environment.

Multi-Robot Robust Motion Planning based on Model Predictive Priority Contouring Control with Double-Layer Corridors

“…The planning process is carried out in the SE(2) space and state x m,k includes 2-D position (x m,k , y m,k ) and orientation ϕ m,k . The robot model f (•) can be written as Equation (1).…”

“…Multi-robot systems play a vital role in next-generation factories, urban search and rescue, and package delivery, and they are anticipated to be applied in space exploration [1,2]. One of the key ingredients to a multi-robot system is the motion planning module.…”

Development and Evaluation of the Traction Characteristics of a Crawler EOD Robot

“…Mobile intervention robots have the following main features: deg lowing a varied trajectory in terms of shape, length, and number of points [44]; speed: it is between 1-10 m/s, depending on the environment The general structure of an EOD mobile robot [https://www.telerob.com/en/products/ teodor-evo (accessed on 18 February 2021)] mechanical and drive system [38]; location system [39]; environmental perception and security system [40]; data interpretation and task management system [41]. Operation in the environment is defined by agility and maneuverability: agility is the ability of the mobile robot to overcome obstacles [42]; maneuverability is defined by the minimum area required for a maneuver [43].…”

Sobel Potential Field: Addressing Responsive Demands for UAV Path Planning Techniques

“…The utilization of Unmanned Aerial Vehicles (UAVs) has witnessed a growing interest within the last few years due to the significant advancements in the primary robotic fields including computer science, control theory, and mechanical engineering. Today, UAVs are involved to perform critical tasks in several vivid fields including fire fighting, delivery services, and geographic mapping [1][2][3][4][5]. The criticality of the UAV tasks has triggered the need to achieve these operations with high precision and accuracy levels.…”