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  1. This paper addresses the problem of dynamic allocation of robot resources to tasks with hierarchical representations and multiple types of execution constraints, with the goal of enabling single-robot multitasking capabilities. Although the vast majority of robot platforms are equipped with more than one sensor (cameras, lasers, sonars) and several actuators (wheels/legs, two arms), which would in principle allow the robot to concurrently work on multiple tasks, existing methods are limited to allocating robots in their entirety to only one task at a time. This approach employs only a subset of a robot's sensors and actuators, leaving other robot resources unused. Our aim is to enable a robot to make full use of its capabilities by having an individual robot multitask, distributing its sensors and actuators to multiple concurrent activities. We propose a new architectural framework based on Hierarchical Task Trees that supports multitasking through a new representation of robot behaviors that explicitly encodes the robot resources (sensors and actuators) and the environmental conditions needed for execution. This architecture was validated on a two-arm, mobile, PR2 humanoid robot, performing tasks with multiple types of execution constraints. 
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    Free, publicly-accessible full text available December 12, 2024
  2. null (Ed.)
    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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  3. As systems that utilize computer vision move into the public domain, methods of calibration need to become easier to use. Though multi-plane LiDAR systems have proven to be useful for vehicles and large robotic platforms, many smaller platforms and low-cost solutions still require 2D LiDAR combined with RGB cameras. Current methods of calibrating these sensors make assumptions about camera and laser placement and/or require complex calibration routines. In this paper we propose a new method of feature correspondence in the two sensors and an optimization method capable of using a calibration target with unknown lengths in its geometry. Our system is designed with an inexperienced layperson as the intended user, which has led us to remove as many assumptions about both the target and laser as possible. We show that our system is capable of calibrating the 2-sensor system from a single sample in configurations other methods are unable to handle. 
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  4. For socially assistive robots (SAR) to be accepted into complex and stochastic human environments, it is important to account for subtle social norms. In this paper, we propose a novel approach to socially-aware navigation (SAN) which garnered an immense interest in the Human-Robot Interaction(HRI) community. We use a multi-objective optimization tool called the Pareto Concavity Elimination Transformation (PaCcET) to capture the non-linear human navigation behavior, a novel contribution to the community. We use autonomously sensed distance-based features that captures the social norms and associated social costs for a given trajectory point towards the goal. Rather than use a finely-tuned linear combination of these costs, we use PaCcET to select an optimized future trajectory point, associated with a non-linear combination of the costs. Existing research in this domain concentrates on geometric reasoning, model-based, and learning approaches, which have their own pros and cons. This approach is distinct from prior work in this area. We showed in a simulation that the PaCcET based trajectory planner not only is able to avoid collisions and reach the intended destination in static and dynamic environments but also considers a human’s personal space in the trajectory selection process. 
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