We present a simple probabilistic framework for multimodal sensor fusion that allows a mobile robot to reliably locate and approach the most promising interaction partner among a group of people, in an uncontrolled environment. Our demonstration integrates three complementary sensor modalities, each of which detects features of nearby people. The output is an occupancy grid approximation of a probability density function over the locations of people that are actively seeking interaction with the robot. We show empirically that simply driving towards the peak of this distribution is sufficient to allow the robot to correctly engage an interested user in a crowd of bystanders.

We propose a novel method for non-uniform terrain coverage using Unmanned aerial vehicles (UAVs). The existing methods for coverage path planning consider a uniformly interesting target area and hence all the regions are covered with high resolution. However in many real world applications items of interest are not uniformly distributed but form patches of locally high interest. Therefore, with sparse sampling of uninteresting sections of the environment and high resolution sampling of interesting patches, the trajectory of the robot can become shorter. In this paper, we present a novel coverage strategy based on Space-Filling Curves that can accomplish non-uniform coverage of regions in the target area. Simulations and real robot experiments indicate that with the new strategy, travel time / cost of the task can be (almost) always less than a regular ‘lawnmower’ coverage pattern.

We present the first demonstration of establishing mutual attention between an outdoor UAV in autonomous normal flight and an uninstrumented human user. We use the familiar periodic waving gesture as a signal to attract the UAV's attention. The UAV can discriminate this gesture from human walking and running that appears similarly periodic. Once a signaling person is observed and tracked, the UAV acknowledges that the user has its attention by hovering and performing a “wobble” behavior. Both parties are now ready for further interaction. The system works on-board the UAV using a single camera for input and is demonstrated working reliably in real-robot trials.

We present an integrated human-robot interaction system that enables a user to select and command a team of two Unmanned Aerial Vehicles (UAV) using voice, touch, face engagement and hand gestures. This system integrates multiple human [multi]-robot interaction interfaces as well as a navigation and mapping algorithm in a coherent semi-realistic scenario. The task of the UAVs is to explore and map a simulated Mars environment.

We present a multi-modal multi-robot interaction whereby a user can identify an individual or a group of robots using haptic stimuli, and name them using a voice command (e.g."You two are green"). Subsequent commands can be addressed to the same robot(s) by name (e.g. "Green! Take off!"). We demonstrate this as part of a real-world integrated system in which a user commands teams of autonomous robots in a coordinated exploration task.

We present a path planning method for MAVs with vision-only MonoSLAM that generates safe paths to a goal according to the information richness of the environment. The planner runs on top of monocular SLAM and uses the available information about structure of the environment and features visibility to find trajectories that maintain visual contact with feature-rich areas. The MAV continuously re-plans as it explores and updates the feature-points in the map. In real-world experiments we show that our system is able to avoid paths that lead into visually-poor sections of the environment by considering the distribution of visual features. If the same system ignores the availability of visually-informative regions in the planning, it is unable to estimate its state accurately and fails to reach its goal.

Area coverage is one of the compelling applications of UAVs. The existing methods for coverage path planning assume a uniformly interesting target area. However in many real world applications items of interest are not uniformly distributed but form clusters. Here it can be advantageous to only sample regions of interest while skipping uninteresting sections of the environment. In this paper, we present a coverage tree structure that can accommodate non-uniform coverage of regions in the target area. Three strategies are proposed to traverse the coverage tree. Experiments indicate that in some situations our method can cover the interesting regions with about half the travel time / cost of a naive regular `lawnmower' coverage pattern.

We describe a system whereby multiple humans and mobile robots interact robustly using a combination of sensing and signalling modalities. Extending our previous work on selecting an individual robot from a population by face-engagement, we show that reaching toward a robot - a specialization of pointing - can be used to designate a particular robot for subsequent one-on-one interaction. To achieve robust operation despite frequent sensing problems, the robots use three phases of human detection and tracking, and emit audio cues to solicit interaction and guide the behaviour of the human. A series of real-world trials demonstrates the practicality of our approach.

Extending our previous work in real-time vision-based Human Robot Interaction (HRI) with multi-robot systems, we present the first example of creating, modifying and commanding teams of UAVs by an uninstrumented human. To create a team the user focuses attention on an individual robot by simply looking at it, then adds or removes it from the current team with a motion-based hand gesture. Another gesture commands the entire team to begin task execution. Robots communicate among themselves by wireless network to ensure that no more than one robot is focused, and so that the whole team agrees that it has been commanded. Since robots can be added and removed from the team, the system is robust to incorrect additions. A series of trials with two and three very low-cost UAVs and off-board processing demonstrates the practicality of our approach.

In this paper we present two task-allocation strategies for a multi-robot transportation system. The first strategy is based on a centralized planner that uses domain knowledge to solve the assignment problem in linear time. In contrast in the second strategy, individual robots make rule-based allocation decisions using only locally obtainable information and single value communication. Both methods are tested and analysed in simulation experiments. We show that the rule-based method performs well but the lack of information has to be paid for with increased energy consumption.

We investigate the problem of a robot maximizing its long-term average rate of return on work. We present a means to obtain an estimate of the instantaneous rate of return when work is rewarded in discrete atoms, and a method that uses this to recursively maximize the long-term average return when work is available in localized patches, each with locally diminishing returns. We examine a puck-foraging scenario, and test our method in simulation under a variety of conditions. However, the analysis and approach applies to the general case.

We study the problem of construction by autonomous mobile robots focusing on the coordination strategy employed by the robots to solve a simple construction problem efficiently. In particular we address the problem of constructing a linear 2D structure in a planar bounded environment. A "minimalist" single-robot solution to the problem is given, as well as two multi-robot solutions, which are natural extensions to the single-robot approach, with varying degrees of inter-robot communication. Results show that with minimal inter-robot communication (1 bit of state), there is a significant improvement in the system performance. This improvement is invariant with respect to the size of the environment.