Mobile robots are increasingly populating homes, hospitals, shopping malls, factory floors, and other human environments. Human society has social norms that people mutually accept; obeying these norms is an essential signal that someone is participating socially with respect to the rest of the population. For robots to be socially compatible with humans, it is crucial for robots to obey these social norms. In prior work, we demonstrated a Socially-Aware Navigation (SAN) planner, based on Pareto Concavity Elimination Transformation (PaCcET), in a hallway scenario, optimizing two objectives so the robot does not invade the personal space of people. This article extends our PaCcET-based SAN planner to multiple scenarios with more than two objectives. We modified the Robot Operating System’s (ROS) navigation stack to include PaCcET in the local planning task. We show that our approach can accommodate multiple Human-Robot Interaction (HRI) scenarios. Using the proposed approach, we achieved successful HRI in multiple scenarios such as hallway interactions, an art gallery, waiting in a queue, and interacting with a group. We implemented our method on a simulated PR2 robot in a 2D simulator (Stage) and a pioneer-3DX mobile robot in the real-world to validate all the scenarios. A comprehensive set of experiments shows that our approach can handle multiple interaction scenarios on both holonomic and non-holonomic robots; hence, it can be a viable option for a Unified Socially-Aware Navigation (USAN).

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With the recent increase in the use of Unmanned Aerial Vehicles (UAVs) comes a surge of inexperienced aviators who may not have the requisite skills to react appropriately if weather conditions quickly change while their aircraft are in flight. This creates a dangerous situation, in which the pilot cannot safely land the vehicle. In this work we examine the use of the MAP-Elites algorithm to search for sets of weights for use in an artificial neural network. This neural network directly controls the thrust and pitching torque of a simulated 3-degree of freedom (2 linear, 1 rotational) fixed-wing UAV, with the goal of obtaining a smooth landing profile. We then examine the use of the same algorithm in high-wind conditions, with gusts up to 30 knots.Our results show that MAP-Elites is an effective method for searching for control policies, and by evolving two separate controllers and switching which controller is active when the UAV is near-ground level, we can produce a wider variety of phenotypic behaviors. The best controllers achieved landing at a vertical speed of <1 m s −1 and at an angle of approach of <1°degree.

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