Seyed Abbas Sadat

Feature-rich path planning for robust navigation of MAVs with Mono-SLAM

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.

UAV, do you see me? Establishing mutual attention between an uninstrumented human and an outdoor UAV in flight

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 UAVs 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.

You are green: a touch-to-name interaction in an integrated multi-modal multi-robot HRI system

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.Categories and Subject DescriptorsI.2.9 [Articial Intelligence]: Robotics, Human Multi-Robot Interaction, User Interfaces

BRaVO: Biased Reciprocal Velocity Obstacles Break Symmetry in Dense Robot Populations

We present an extension to the Reciprocal Velocity Obstacles (RVO) approach to multi-robot collision avoidance with the aim of alleviating the problem of congestion caused by symmetrical situations in dense conditions. We show that in a resource transportation task RVO robots are unable to make progress due to crowds of robots with opposing navigation goals at source and sink. We introduce Biased Reciprocal Velocity Obstacles (BRVO), which breaks the symmetry among robots by giving priority to the robots leaving a task-related place of interest. BRVO is compared to RVO in two experiments and it is shown that BRVO is able to resolve the congestion much more quickly than RVO.

Integrating multi-modal interfaces to command UAVs

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.