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 consider the classical task of transporting resources from source to home by a group of autonomous robots. The robots use ant-like trail following to navigate between home and source. This paper studies the effect on global performance of changing the field of view of each robot's trail-following sensor. It is shown that, under certain conditions, a narrow field of view can improve system performance. We argue that the benefit is obtained by selectively degrading the individuals' trail-following ability so that more work space is exploited in parallel, thus decreasing mutual spatial interference. This “worse-is-better” idea may be applicable to other large-scale multi-robot systems.