Towards human-friendly efficient control of multi-robot teams

This paper explores means to increase efficiency in performing tasks with multi-robot teams, in the context of natural Human-Multi-Robot Interfaces (HMRI) for command and control. The motivating scenario is an emergency evacuation by a transport convoy of unmanned ground vehicles (UGVs) that have to traverse, in shortest time, an unknown terrain. In the experiments the operator commands, in minimal time, a group of rovers through a maze. The efficiency of performing such tasks depends on both, the levels of robots' autonomy, and the ability of the operator to command and control the team. The paper extends the classic framework of levels of autonomy (LOA), to levels/hierarchy of autonomy characteristic of Groups (G-LOA), and uses it to determine new strategies for control. An UGVoriented command language (UGVL) is defined, and a mapping is performed from the human-friendly gesture-based HMRI into the UGVL. The UGVL is used to control a team of 3 robots, exploring the efficiency of different G-LOA; specifically, by (a) controlling each robot individually through the maze, (b) controlling a leader and cloning its controls to followers, and (c) controlling the entire group. Not surprisingly, commands at increased G-LOA lead to a faster traverse, yet a number of aspects are worth discussing in this context

A wearable general-purpose solution for Human-Swarm Interaction

Swarms of robots will revolutionize many industrial applications, from targeted material delivery to precision farming. Controlling the motion and behavior of these swarms presents unique challenges for human operators, who cannot yet effectively convey their high-level intentions to a group of robots in application. This work proposes a new human-swarm interface based on novel wearable gesture-control and haptic-feedback devices. This work seeks to combine a wearable gesture recognition device that can detect high-level intentions, a portable device that can detect Cartesian information and finger movements, and a wearable advanced haptic device that can provide real-time feedback. This project is the first to envisage a wearable Human-Swarm Interaction (HSI) interface that separates the input and feedback components of the classical control loop (input, output, feedback), as well as being the first of its kind suitable for both indoor and outdoor environments

3D Formation Control in Multi-Robot Teams Using Artificial Potential Fields

Multi-robot teams find applications in emergency response, search and rescue operations, convoy support and many more. Teams of autonomous aerial vehicles can also be used to protect a cargo of airplanes by surrounding them in some geometric shape. This research develops a control algorithm to attract UAVs to one or a set of bounded geometric shapes while avoiding collisions, re-configuring in the event of departure or addition of UAVs and maneuvering in mission space while retaining the configuration. Using potential field theory, weighted vector fields are described to attract UAVs to a desired formation. In order to achieve this, three vector fields are defined: one attracts UAVs located outside the formation towards bounded geometric shape; one pushes them away from the center towards the desired region and the third controls collision avoidance and dispersion of UAVs within the formation. The result is a control algorithm that is theoretically justified and verified using MATLAB which generates velocity vectors to attract UAVs to a loose formation and maneuver in the mission space while remaining in formation. This approach efficiently scales to different team sizes

Modeling Supervisory Control in Multi Robot Applications

We consider multi robot applications, where a human operator monitors and supervise the team to pursue complex objectives in complex environments. Robots, specially at field sites, are often subject to unexpected events that can not be managed without the intervention of the operator(s). For example, in an environmental monitoring application, robots might face extreme environmental events (e.g. water currents) or moving obstacles (e.g. animal approaching the robots). In such scenarios, the operator often needs to interrupt the activities of individual team members to deal with particular situations. This work focuses on human-multi-robot-interaction in these casts. A widely used approach to monitor and supervise robotic teams are team plans, which allow an operator to interact via high level objectives and use automation to work out the details. The first problem we address in this context, is how human interrupts (i.e. change of action due to unexpected events) can be handled within a robotic team. Typically, after such interrupts, the operator would need to restart the team plan to ensure its success. This causes delays and imposes extra load on the operator. We address this problem by presenting an approach to encoding how interrupts can be smoothly handled within a team plan. Building on a team plan formalism that uses Colored Petri Nets, we describe a mechanism that allows a range of interrupts to be handled smoothly, allowing the team to effectively continue with its task after the operator intervention. We validate the approach with an application of robotic water monitoring. Our experiments show that the use of our interrupt mechanism decreases the time to complete the plan (up to 48% reduction) and decreases the operator load (up to 80% reduction in number of user actions). Moreover, we performed experiments with real robotic platforms to validate the applicability of our mechanism in the actual deployment of robotic watercraft. The second problem we address is how to handle intervention requests from robots to the operator. In this case, we consider autonomous robotic platforms that are able to identify their situation and ask for the intervention of the operator by sending a request. However, large teams can easily overwhelm the operator with several requests, hence hindering the team performance. As a consequence, team members will have to wait for the operator attention, and the operator becomes a bottleneck for the system. Our contribution in this context is to make the robots learn cooperative strategies to best utilize the operator's time and decrease the idle time of the robotic system. In particular, we consider a queuing model (a.k.a balking queue), where robots decide whether or not to join the queue. Such decisions are computed by considering dynamic features of the system (e.g. the severity of the request, number of requests, etc.). We examine several decision making solutions for computing these cooperative strategies, where our goal is to find a trade-off between lower idle time by joining the queue and fewer failures due to the risk of not joining the queue. We validate the proposed approaches in a simulation robotic water monitoring application. The obtained results show the effectiveness of our proposed models in comparison to the queue without balking, when considering team reward and total idle time