In this work, we address the challenges of employing robots in the Search-and-Rescue (SAR) domain, where they can benefit rescue workers to quickly obtain Situational Awareness (SA). Missions with autonomous mobile robots are heavily dependent on environmental representations. Representations have been steadily increasing in the richness that they can capture, in addition to geometry we can now represent objects and their interrelations. These richer environmental representations, such as the 3D scene graph, have provided an opportunity to integrate representations with prior knowledge to make them more actionable and improve the SA they provide. The use of such representations for planning is limited. In previous work, the main approach was to augment and extend the scene graph to enable traditional symbolic planning. The limitations of these methods are that they are offline, scale poorly, and require full observability, making them unsuitable for the SAR domain. The main contributions of this work are as follows. First, we propose the behavior-oriented situational graph, a data structure that integrates data-driven perception with prior knowledge following a novel situational affordance schema. This schema connects situations with robot behaviors and mission objectives, allowing for autonomous mission planning. Second, we propose an efficient method to obtain task utilities from the proposed behavior-oriented situational graph through planning. Finally, we propose an exploration component to discover and select tasks online in dynamic environments that are potentially partially observable. This work is evaluated in several simulation scenarios, showing improved efficiency in mission completion compared to offline methods for the specific SAR domain. Finally, the methods are implemented and tested in a real-world scenario using a mobile Spot robot, showing its effectiveness in practice.

The area surveillance problem is the problem of surveying a known or an unknown area with the main purpose of detecting objects. This thesis will tackle the problem of how to employ a group of mobile-sensors for surveying an unobstructed area in an optimal manner. The mobile-sensors should make use of their onboard computers and they should iteratively compute their waypoints until they successfully surveyed the area. To solve the area surveillance problem in an optimal manner, the mobile-sensors should be able to coordinate their actions. The Distributed Constraint Optimization Problem (DCOP) framework will be employed to define the area surveillance problem. In the DCOP, the mobile-sensors can define their optimal position for the next discrete time step by communicating with the other mobile-sensors that participate in the problem.
For solving this DCOP, the Distributed Pseudo-tree Optimization Procedure (DPOP) will be utilized. The DPOP is a complete solver that can find the optimal solution of a DCOP in a decentralized manner. However, the main limitation of DPOP is that the size of the largest message that the mobile-sensors will have to exchange is space-exponential in the induced width of the pseudo-tree. Considering that the mobile-sensors have to use their onboard computers for solving the problem, exchanging huge size of messages is not desired. To overcome this limitation of DPOP, a new extension, known as the MST-DPOP will be presented in this Thesis. The MST-DPOP makes use of the Maximum Spanning Tree (MST) algorithm to reduce the size of the largest message that the mobile-sensors have to exchange. Employing MST along with DPOP can bound the size of the largest message and the required computations for constructing the utility messages. However, the new extension cannot guarantee that its solution will be the optimal. The experimental results shows that the MST-DPOP is able to define a solution with an average error less than $2\%$. Moreover, the MST-DPOP requires on average around $1$ discrete time step more than the DPOP to solve the area surveillance problem. Consequently, given that the MST-DPOP overcomes the high memory requirements of DPOP, it is preferred for solving the area surveillance problem.