An official website of the United States government
We present a context classification pipeline to allow a robot to change its navigation strategy based on the observed social scenario. Socially-Aware Navigation considers social behavior in order to improve navigation around people. Most of the existing research uses different techniques to incorporate social norms into robot path planning for a single context. Methods that work for hallway behavior might not work for approaching people, and so on. We developed a high-level decision-making subsystem, a model-based context classifier, and a multi-objective optimization-based local planner to achieve socially-aware trajectories for autonomously sensed contexts. Using a context classification system, the robot can select social objectives that are later used by Pareto Concavity Elimination Transformation (PaCcET) based local planner to generate safe, comfortable, and socially appropriate trajectories for its environment. This was tested and validated in multiple environments on a Pioneer mobile robot platform; results show that the robot could select and account for social objectives related to navigation autonomously.
more »
« less
Information sampling for contingency planning.
From navigation in unfamiliar environments to career plan- ning, people typically first sample information before com- mitting to a plan. However, most studies find that people adopt myopic strategies when sampling information. Here we challenge those findings by investigating whether contingency planning is a driver of information sampling. To this aim, we developed a novel navigation task that is a shortest path find- ing problem under uncertainty of bridge closures. Participants (n = 109) were allowed to sample information on bridge sta- tuses prior to committing to a path. We developed a computa- tional model in which the agent samples information based on the cost of switching to a contingency plan. We find that this model fits human behavior well and is qualitatively similar to the approximated optimal solution. Together, this suggests that humans use contingency planning as a driver of information sampling.
more »
« less
- Award ID(s):
- 2021060
- PAR ID:
- 10267228
- Date Published:
- Journal Name:
- Proceedings of the 43rd Annual Conference of the Cognitive Science Society
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Autonomous navigation in construction environments is particularly challenging due to dynamic obstacles and uncertain surroundings. While recent advances in Building Information Modeling (BIM)-based planning have leveraged spatial and semantic information to improve navigation, most prior work assumes precise localization of the BIM model to enable global path planning. In contrast, this paper introduces an online replanning framework that registers obstacles on discovery within BIM and replans according to the updated semantic map. Our method integrates object-aware path planning by utilizing large language models (LLMs) to extract semantic danger sentiments from BIM-annotated objects and their spatial information about the mission environment. Additionally, we demonstrate practical feasibility by integrating a path tracking control, ensuring generated paths are not only safer but also realistically executable by mobile robots. Experimental results demonstrate an improved obstacle avoidance by 2.8× compared to traditional A* algorithms in dynamically updated environments.more » « less
-
We present an adaptive fast-approximation for sensor configuration which finds near-optimal placements and sensor field of views (FoV). The fast-approximation, either via partition-based or density-based cluster analysis, adapts based on the relation between statistical uncertainty of the path plan and environmental uncertainty. The sensor configurations are performed over regions of interest which most directly influence the path-planning efforts. These regions of interest can include exploratory paths by sampling the probabilistic environment model. The path-planning efforts aim to decide upon a path which minimizes an agent’s exposure to threats in an unknown static environment. The noisy sensor network observations are used to construct a threat field estimate using Gaussian Process Regression each iteration with a stationary kernel and heteroscedastic gaussian likelihood. The optimization of a task-driven information gain determines optimal sensor configurations when maximized. The numerical performance of the direct optimization and the adaptive cluster analysis method is presented. Finally, we show that the cluster centers can be utilized as a dimensionality reduction technique for FoV optimization whereby we only optimize FoV radial coverage.more » « less
-
null (Ed.)Most path planning techniques use exact, global information of the environment to make optimal or near-optimal plans. In contrast, humans navigate using only local information, which they must augment with their understanding of typical building layouts to guess what lies ahead, while integrating what they have seen already to form mental representations of building structure. Here, we propose Scene Planning Networks (SPNets), a neural network based approach for formulating the long-range navigation problem as a series of local decisions similar to what humans face when navigating. Agents navigating using SPNets build additive neural representations of previous observations to understand local obstacle structure, and use a network-based planning approach to plan the next steps towards a fuzzy goal region. Our approach reproduces several important aspects of human behavior that are not captured by either full global planning or simple local heuristics.more » « less
-
Goal-based navigation in public places is critical for independent mobility and for breaking barriers that exist for blind or visually impaired (BVI) people in a sight-centric society. Through this work we present a proof-of-concept system that autonomously leverages goal-based navigation assistance and perception to identify socially preferred seats and safely guide its user towards them in unknown indoor environments. The robotic system includes a camera, an IMU, vibrational motors, and a white cane, powered via a backpack-mounted laptop. The system combines techniques from computer vision, robotics, and motion planning with insights from psychology to perform 1) SLAM and object localization, 2) goal disambiguation and scoring, and 3) path planning and guidance. We introduce a novel 2-motor haptic feedback system on the cane’s grip for navigation assistance. Through a pilot user study we show that the system is successful in classifying and providing haptic navigation guidance to socially preferred seats, while optimizing for users’ convenience, privacy, and intimacy in addition to increasing their confidence in independent navigation. The implications are encouraging as this technology, with careful design guided by the BVI community, can be adopted and further developed to be used with medical devices enabling the BVI population to better independently engage in socially dynamic situations like seat choice.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
