Integrating Affective Expressions into Robot-Assisted Search and Rescue to Improve Human-Robot Communication

Unexplained or ambiguous behaviours of rescue robots can lead to inefficient collaborations between humans and robots in robot-assisted SAR teams. To date, rescue robots do not have the ability to interact with humans on a social level, which is believed to be an essential ability that can improve the quality of interactions. This thesis research proposes to bring affective robot expressions into the SAR context to provide rescue robots social capabilities. The first experiment presented in Chapter 3 investigates whether there is consensus in mapping emotions to messages/situations in Urban Search and Rescue (USAR) scenarios, where efficiency and effectiveness of interactions are crucial to success. We studied mappings between 10 specific messages, presented in two different communication styles, reflecting common situations that might happen during search and rescue missions and the emotions exhibited by robots in those situations. The data was obtained through a Mechanical Turk study with 78 participants. The findings support the feasibility of using emotions as an additional communication channel to improve multi-modal human-robot interaction for urban search and rescue robots and suggest that these mappings are robust, i.e., are not affected by the robot’s communication style. The second experiment was conducted on Amazon Mechanical Turk as well with 223 participants. We used Affect Control Theory (ACT) as a method for deriving the mappings between situations and emotions (similar to the ones in the first experiment) and as an alternative method to obtaining mappings that can be adjusted for different emotion sets (Chapter 4). The results suggested that there is consistency in the choice of emotions for a robot to show in different situations between the two methods used in the first and second experiment, indicating the feasibility of using emotions as an additional modality in SAR robots. After validating the feasibility of bringing emotions to SAR context based on the findings from the first two experiments, we created affective expressions based on Evaluation, Potency and Activity (EPA) dimensions of ACT with the help of LED lights on a rescue robot called Husky. We evaluated the effect of emotions on rescue workers’ situational awareness through an online Amazon Mechanical Turk Study with 151 participants (Chapter 5). Findings indicated that participants who saw Husky with affective expressions (conveyed through lights) had better perception accuracy of the situation happening in the disaster scene than participants who saw the videos of the Husky robot without any affective lights. In other words, Husky with affective lights improved participants’ situational awareness

A comparison study of search heuristics for an autonomous multi-vehicle air-sea rescue system

The immense power of the sea presents many life-threatening dangers to humans, and many fall foul of its unforgiving nature. Since manned rescue operations at sea (and indeed other search and rescue operations) are also inherently dangerous for rescue workers, it is common to introduce a level of autonomy to such systems. This thesis investigates via simulations the application of various search algorithms to an autonomous air-sea rescue system, which consists of an unmanned surface vessel as the main hub, and four unmanned helicopter drones. The helicopters are deployed from the deck of the surface vessel and are instructed to search certain areas for survivors of a stricken ship. The main aim of this thesis is to investigate whether common search algorithms can be applied to the autonomous air-sea rescue system to carry out an efficient search for survivors, thus improving the present-day air-sea rescue operations. Firstly, the mathematical model of the helicopter is presented. The helicopter model consists of a set of differential equations representing the translational and rotational dynamics of the whole body, the flapping dynamics of the main rotor blades, the rotor speed dynamics, and rotational transformations from the Earth-fixed frame to the body frame. Next, the navigation and control systems are presented. The navigation system consists of a line-of-sight autopilot which points each vehicle in the direction of its desired waypoint. Collision avoidance is also discussed using the concept of a collision cone. Using the mathematical models, controllers are developed for the helicopters: Proportional-Integral-Derivative (PID) and Sliding Mode controllers are designed and compared. The coordination of the helicopters is carried out using common search algorithms, and the theory, application, and analysis of these algorithms is presented. The search algorithms used are the Random Search, Hill Climbing, Simulated Annealing, Ant Colony Optimisation, Genetic Algorithms, and Particle Swarm Optimisation. Some variations of these methods are also tested, as are some hybrid algorithms. As well as this, three standard search patterns commonly used in maritime search and rescue are tested: Parallel Sweep, Sector Search, and Expanding Square. The effect of adding to the objective function a probability distribution of target locations is also tested. This probability distribution is designed to indicate the likely locations of targets and thus guide the search more effectively. It is found that the probability distribution is generally very beneficial to the search, and gives the search the direction it needs to detect more targets. Another interesting result is that the local algorithms perform significantly better when given good starting points. Overall, the best approach is to search randomly at the start and then hone in on target areas using local algorithms. The best results are obtained when combining a Random Search with a Guided Simulated Annealing algorithm