In addition, the engineers sealed its most sensitive components inside a dry pressure compartment. They also painted its exposed parts with commercially available coatings that can protect them against the corrosive properties of saltwater. Both measures seem to have worked well during their experiments: the drone the researchers kept in sea water for two months showed no signs of damage. CRACUNS doesn't have any metal parts that can rust and malfunction in the water -- best of all, it's lightweight and doesn't cost much. Those factors make it a good candidate for big research or military operations.

This paper and use of autonomous weapons systems has been will discuss the moral and ethical questions that arise in the one of the outcomes of the counterterrorism and counterinsurgency use of lethally autonomous technology for military purposes operations in Iraq and Afghanistan. The asymmetrical and how the forms of subjectivity and moral agency that battlefields of these theaters, where no frontline it creates could be highly counterproductive to mission provides a buffer between combatants and civilians and effectiveness, diplomacy and conflict resolution and prevention.

It is nowadays our responsibility to convince our contemporary citizens that AI devices as UGVs (Unmanned Ground Vehicles) and UAVs (Unmanned Aerial Vehicles) are crucial actors of today’s life in a dual domains, both civilian and military. In particular, the decision process is the main component of every military operation and is of high interest because of two main reasons : it is necessary designed to cope with conflict issues and it requires a very complex planning process to be successful. The difficulty to find a good plan is worse in urban areas because of the high uncertainty due to the topology of these areas, the presence of civilians, who can be hostile or friendly, and the unpredictable nature of enemies. The idea in that paper is to qualify what can be a valid computed plan in that context , i.e. welldesigned for recovering of peace, rescue operations after a bombing event, hostage salvage, non-combatant evacuation operations, civil-military co-operation, ...., in urban areas. This planning process leads to associate actually four components, the representation of the tactical scheme, the implementation of the tactical scheme as the behaviour of special forces, military units or emergency squads, the proof process or the explanation process, and finally the handling of external factors depending on the current environment or the current context in which the operation takes place. This paper uses a quaternary representation called the epistemological quadriptych, in order to highlight that the integration of UGVs or UAVs devices requires actually to understand the role of knowledge and behaviour and to provide secure and valid action plans, i.e. which can be explained and justified.

This paper presents an exploratory study where participants piloted a commercial UAS (unmanned aerial system) through an obstacle course. The goal was to determine how varying the instructions given to participants affected their performance. Preliminary data suggests future studies to perform, as well as guidelines for human-robot interaction, and some best practices for learning from demonstration studies.

We consider a setting where a team of humans oversee the coordination of multiple Unmanned Aerial Vehicles (UAVs) to perform a number of search tasks in dynamic environments that may cause the UAVs to drop out. Hence, we develop a set of multi-UAV supervisory control interfaces and a multi-agent coordination algorithm to support human decision making in this setting. To elucidate the resulting interactional issues, we compare manual and mixed-initiative task allocation in both static and dynamic environments in lab studies with 40 participants and observe that our mixed-initiative system results in lower workloads and better performance in re-planning tasks than one which only involves manual task allocation. Our analysis points to new insights into the way humans appropriate flexible autonomy.

Cooperative pathfinding is a multi-agent path planning problem where a group of vehicles searches for a corresponding set of non-conflicting space-time trajectories. Many of the practical methods for centralized solving of cooperative pathfinding problems are based on the prioritized planning strategy. However, in some domains (e.g., multi-robot teams of unmanned aerial vehicles, autonomous underwater vehicles, or unmanned ground vehicles) a decentralized approach may be more desirable than a centralized one due to communication limitations imposed by the domain and/or privacy concerns. In this paper we present an asynchronous decentralized variant of prioritized planning ADPP and its interruptible version IADPP. The algorithm exploits the inherent parallelism of distributed systems and allows for a speed up of the computation process. Unlike the synchronized planning approaches, the algorithm allows an agent to react to updates about other agents' paths immediately and invoke its local spatio-temporal path planner to find the best trajectory, as response to the other agents' choices. We provide a proof of correctness of the algorithms and experimentally evaluate them on synthetic domains.

We present a methodology for the deployment of the max-sum algorithm, a well known decentralised algorithm for coordinating autonomous agents, for problems related to situational awareness. In these settings, unmanned autonomous vehicles are deployed to collect information about an unknown environment. Our methodology then helps identify the choices that need to be made to apply the algorithm to these problems. Next, we present a case study where the methodology is used to develop a system for disaster management in which a team of unmanned aerial vehicles coordinate to provide the first responders of the area of a disaster with live aerial imagery. To evaluate this system, we deploy it on two unmanned hexacopters in a variety of scenarios. Our tests show that the system performs well when confronted with the dynamism and the heterogeneity of the real world.

Complex mission or task specification languages play a fundamentally important role in human/robotic interaction. In realistic scenarios such as emergency response, specifying temporal, resource and other constraints on a mission is an essential component due to the dynamic and contingent nature of the operational environments. It is also desirable that in addition to having a formal semantics, the language should be sufficiently expressive, pragmatic and abstract. The main goal of this paper is to propose a mission specification language that meets these requirements. It is based on extending both the syntax and semantics of a well-established formalism for reasoning about action and change, Temporal Action Logic (TAL), in order to represent temporal composite actions with constraints. Fixpoints are required to specify loops and recursion in the extended language. The results include a sound and complete proof theory for this extension. To ensure that the composite language constructs are adequately grounded in the pragmatic operation of robotic systems, Task Specification Trees (TSTs) and their mapping to these constructs are proposed. The expressive and pragmatic adequacy of this approach is demonstrated using an emergency response scenario.

Current practice in Wilderness Search and Rescue (WiSAR) is analogous to an intelligent system designed to gather and analyze information to find missing persons in remote areas. The system consists of multiple parts - various tools for information management (maps, GPS, etc) distributed across personnel with different skills and responsibilities. Introducing a camera-equipped mini-UAV into this task requires autonomy and information technology that itself is an integrated intelligent system to be used by a sub-team that must be integrated into the overall intelligent system. In this paper, we identify key elements of the integration challenges along two dimensions: (a) attributes of intelligent system and (b) scale, meaning individual or group. We then present component technology that offload or supplement many responsibilities to autonomous systems, and finally describe how autonomy and information are integrated into user interfaces to better support distributed search across time and space. The integrated system was demoed for Utah County Search and Rescue personnel. A real searcher flew the UAV after minimal training and successfully located the simulated missing person in a wilderness area.

Unmanned aircraft systems use a variety of techniques to plan collision-free flight paths given a map of obstacles and no-fly zones. However, maps are not perfect and obstacles may change over time or be detected during flight, which may invalidate paths that the aircraft is already following. Thus, dynamic in-flight replanning is required. Numerous strategies can be used for replanning, where the time requirements and the plan quality associated with each strategy depend on the environment around the original flight path. In this paper, we investigate the use of machine learning techniques, in particular support vector machines, to choose the best possible replanning strategy depending on the amount of time available. The system has been implemented, integrated and tested in hardware-in-the-loop simulation with a Yamaha RMAX helicopter platform.