The environments in which the collaboration of a robot would be the most helpful to a person are frequently uncontrolled and cluttered with many objects present. Legible robot arm motion is crucial in tasks like these in order to avoid possible collisions, improve the workflow and help ensure the safety of the person. Prior work in this area, however, focuses on solutions that are tested only in uncluttered environments and there are not many results taken from cluttered environments. In this research we present a measure for clutteredness based on an entropic measure of the environment, and a novel motion planner based on potential fields. Both our measures and the planner were tested in a cluttered environment meant to represent a more typical tool sorting task for which the person would collaborate with a robot. The in-person validation study with Baxter robots shows a significant improvement in legibility of our proposed legible motion planner compared to the current state-of-the-art legible motion planner in cluttered environments. Further, the results show a significant difference in the performance of the planners in cluttered and uncluttered environments, and the need to further explore legible motion in cluttered environments. We argue that the inconsistency of our results in cluttered environments with those obtained from uncluttered environments points out several important issues with the current research performed in the area of legible motion planners.

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Vehicle pedestrian communication is extremely important when developing autonomy for an autonomous vehicle. Enabling bidirectional nonverbal communication between pedestrians and autonomous vehicles will lead to an improvement of pedestrians' safety in autonomous driving. If a pedestrian wants to communicate, the autonomous vehicle should provide feedback to the human about what it is about to do. The user study presented in this paper investigated several possible options for an external vehicle display for effective nonverbal communication between an autonomous vehicle and a human. The result of this study will guide the development of the feedback module in future studies, optimizing for public acceptance and trust in the autonomous vehicle's decision while being legible to the widest range of potential users. The results of this study show that participants prefer symbols over text, lights and road projection. Additionally, participants prefer the combination of symbols and text as interaction modes to be displayed if the autonomous vehicle is not driving. Further, the results show that the text interaction mode option "Safe to cross" should be used combined with the symbol interaction mode option that displays a symbol of a walking person. We plan to elaborate and focus on the selected interaction modes via Virtual Reality and in the real world in ongoing and future studies.