1,802 research outputs found
Learning Multi-Agent Navigation from Human Crowd Data
The task of safely steering agents amidst static and dynamic obstacles has many applications in robotics, graphics, and traffic engineering. While decentralized solutions are essential for scalability and robustness, achieving globally efficient motions for the entire system of agents is equally important. In a traditional decentralized setting, each agent relies on an underlying local planning algorithm that takes as input a preferred velocity and the current state of the agent\u27s neighborhood and then computes a new velocity for the next time-step that is collision-free and as close as possible to the preferred one. Typically, each agent promotes a goal-oriented preferred velocity, which can result in myopic behaviors as actions that are locally optimal for one agent is not necessarily optimal for the global system of agents. In this thesis, we explore a human-inspired approach for efficient multi-agent navigation that allows each agent to intelligently adapt its preferred velocity based on feedback from the environment. Using supervised learning, we investigate different egocentric representations of the local conditions that the agents face and train various deep neural network architectures on extensive collections of human trajectory datasets to learn corresponding life-like velocities. During simulation, we use the learned velocities as high-level, preferred velocities signals passed as input to the underlying local planning algorithm of the agents. We evaluate our proposed framework using two state-of-the-art local methods, the ORCA method, and the PowerLaw method. Qualitative and quantitative results on a range of scenarios show that adapting the preferred velocity results in more time- and energy-efficient navigation policies, allowing agents to reach their destinations faster as compared to agents simulated with vanilla ORCA and PowerLaw
Towards Autonomous and Safe Last-mile Deliveries with AI-augmented Self-driving Delivery Robots
In addition to its crucial impact on customer satisfaction, last-mile
delivery (LMD) is notorious for being the most time-consuming and costly stage
of the shipping process. Pressing environmental concerns combined with the
recent surge of e-commerce sales have sparked renewed interest in automation
and electrification of last-mile logistics. To address the hurdles faced by
existing robotic couriers, this paper introduces a customer-centric and
safety-conscious LMD system for small urban communities based on AI-assisted
autonomous delivery robots. The presented framework enables end-to-end
automation and optimization of the logistic process while catering for
real-world imposed operational uncertainties, clients' preferred time
schedules, and safety of pedestrians. To this end, the integrated optimization
component is modeled as a robust variant of the Cumulative Capacitated Vehicle
Routing Problem with Time Windows, where routes are constructed under uncertain
travel times with an objective to minimize the total latency of deliveries
(i.e., the overall waiting time of customers, which can negatively affect their
satisfaction). We demonstrate the proposed LMD system's utility through
real-world trials in a university campus with a single robotic courier.
Implementation aspects as well as the findings and practical insights gained
from the deployment are discussed in detail. Lastly, we round up the
contributions with numerical simulations to investigate the scalability of the
developed mathematical formulation with respect to the number of robotic
vehicles and customers
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
A major challenge to deploying robots widely is navigation in human-populated
environments, commonly referred to as social robot navigation. While the field
of social navigation has advanced tremendously in recent years, the fair
evaluation of algorithms that tackle social navigation remains hard because it
involves not just robotic agents moving in static environments but also dynamic
human agents and their perceptions of the appropriateness of robot behavior. In
contrast, clear, repeatable, and accessible benchmarks have accelerated
progress in fields like computer vision, natural language processing and
traditional robot navigation by enabling researchers to fairly compare
algorithms, revealing limitations of existing solutions and illuminating
promising new directions. We believe the same approach can benefit social
navigation. In this paper, we pave the road towards common, widely accessible,
and repeatable benchmarking criteria to evaluate social robot navigation. Our
contributions include (a) a definition of a socially navigating robot as one
that respects the principles of safety, comfort, legibility, politeness, social
competency, agent understanding, proactivity, and responsiveness to context,
(b) guidelines for the use of metrics, development of scenarios, benchmarks,
datasets, and simulators to evaluate social navigation, and (c) a design of a
social navigation metrics framework to make it easier to compare results from
different simulators, robots and datasets.Comment: 43 pages, 11 figures, 6 table
A Survey on Human-aware Robot Navigation
Intelligent systems are increasingly part of our everyday lives and have been
integrated seamlessly to the point where it is difficult to imagine a world
without them. Physical manifestations of those systems on the other hand, in
the form of embodied agents or robots, have so far been used only for specific
applications and are often limited to functional roles (e.g. in the industry,
entertainment and military fields). Given the current growth and innovation in
the research communities concerned with the topics of robot navigation,
human-robot-interaction and human activity recognition, it seems like this
might soon change. Robots are increasingly easy to obtain and use and the
acceptance of them in general is growing. However, the design of a socially
compliant robot that can function as a companion needs to take various areas of
research into account. This paper is concerned with the navigation aspect of a
socially-compliant robot and provides a survey of existing solutions for the
relevant areas of research as well as an outlook on possible future directions.Comment: Robotics and Autonomous Systems, 202
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