Cooperative behaviors in multi-robot systems emerge as an excellent alternative for collaboration in search and rescue tasks to accelerate the finding survivors process and avoid risking additional lives. Although there are still several challenges to be solved, such as communication between agents, power autonomy, navigation strategies, and detection and classification of survivors, among others. The research work presented by this paper focuses on the navigation of the robot swarm and the consensus of the agents applied to the victims detection. The navigation strategy is based on the application of particle swarm theory, where the robots are the agents of the swarm. The attraction and repulsion forces that are typical in swarm particle systems are used by the multi-robot system to avoid obstacles, keep group compact and navigate to a target location. The victims are detected by each agent separately, however, once the agents agree on the existence of a possible victim, these agents separate from the general swarm by creating a sub-swarm. The sub-swarm agents use a modified rendezvous consensus algorithm to perform a formation control around the possible victims and then carry out a consensus of the information acquired by the sensors with the aim to determine the victim existence. Several experiments were conducted to test navigation, obstacle avoidance, and search for victims. Additionally, different situations were simulated with the consensus algorithm. The results show how swarm theory allows the multi-robot system navigates avoiding obstacles, finding possible victims, and settling down their possible use in search and rescue operations.

Transporting objects using aerial robots has been widely studied in the literature, but those approaches always assume that the connection between the quadrotor and the load is made in a previous stage. However, that previous stage usually requires human intervention, and autonomous procedures to locate and attach the object were not considered. Additionally, most of the approaches assume cables as rigid links, but manipulating cables requires considering the state when the cables are hanging. In this work, we design and control a Catenary Robot. Our robot is able to navigate and pull objects in the environment. The robotic system is composed of two quadrotors attached to the two ends of a cable. Defining the catenary curve with five degrees of freedom, position in 3-D, orientation in one axis, and span, we can use the two quadrotors to track a given trajectory. We validate our approach with simulations and real robots. We present three different scenarios of experiments. Our numerical solution is computationally fast and can be executed in real-time.

This paper presents a control strategy for multiple heterogeneous robots oriented to the assistance in situations of search and rescue from two complementary perspectives, the real time control and the discrete task allocation. The real time control laws are in charge of the correct execution of the tasks assigned by the reallocation algorithm respecting constraints in the connectivity range, the collision avoiding and the full completion of each task. The considered tasks are exploration of the mission environment, search and identification of victims, delivery of medical supplies to victims unable to move and evacuation of victims capable to move. During the development of each task, the robots take routes optimized considering distance metrics based on the breath-first search algorithm and an exploration algorithm for non-convex environments. In order to assign the tasks to the robots thorough the mission, we present an optimized task reallocation algorithm focused on the minimization of the time required to attend all the victims in the mission environment. This algorithm allows to assign each of the robots to an appropriate task considering that the robots may differ in the capability of completing each task as well as in their moving capabilities. This complete framework allows a team of autonomous robots to bring a valuable assistance in a search and rescue situation and its behavior is presented in a non-trivial simulation scenario.

This paper proposes the use of reinforcement learning to provide navigation and adaptation skills to a mobile robot in unknown environments. The use of sensor information is developed instead of the spatial location of the robot with the aim to perform the navigation and exploration process. The use of the Q-learning algorithm is proposed, however, the states are established based on the information coming from the sensors. A reward policy is applied to focus on guide the robot away from obstacles and allowing the exploration of unknown environments. Additionally, an exploration policy is generated which collaborates in the adaptation process of the robot. The complete system is evaluated using a robot simulation environment known as V-Rep. The results show that the robot learns to navigate avoiding to collide with obstacles, besides presenting skills to explore unknown environments.