Formation-Based Tracking Method for Human Following Robot

Ahn, Joonwoo; Kim, Min Soo; Kim, Se-Jin; Lee, Sang Yup; Park, Jaeheung

doi:10.1109/urai.2018.8441820

Cited by 5 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In such cases, a target-centric approach is adopted. First, the locally sensed information is used to create a partial map of the environment; traditional SLAM-based techniques are most commonly used for this purpose (Ahn et al, 2018; Cosgun et al, 2016). As illustrated in Figure 13, the UAV creates a 3D (depth) map of the partially observed environment in order to find the optimal path for person-following (Skydio, 2018).…”

Section: State-of-the-art Approachesmentioning

confidence: 99%

“…Standard path planners typically represent the state space using cells, grids, or potential fields and then apply various search methods to find the optimal source-to-destination path. For instance, the navigation space (and the locations of relevant objects) is often interpreted using an occupancy grid , and graph search-based algorithms, such as A * , D * , or IDA * (Iterative Deepening A * ), are used to find the optimal path (Ahn et al, 2018; Huskić et al, 2017; Müller et al, 2008). Another approach is to randomly sample the state space and attempt to establish source-to-destination connectivity using such techniques as rapidly exploring random trees (RRTs) (Triebel et al, 2016), RRT * , or probabilistic road maps (Hoeller et al, 2007).…”

Section: State-of-the-art Approachesmentioning

confidence: 99%

“…The global map of the environment (including static objects) is used as prior knowledge. Typically a static planner keeps track of the robot's location within the map and its admissible waypoints by taking into account the static obstacles in the environment (Ahn et al, 2018). The dynamic planner then refines these waypoints by considering the motions of the dynamic objects in the environment (Figure 12).…”

Section: Map-assisted Vs Target-centric Planningmentioning

confidence: 99%

See 2 more Smart Citations

Person-following by autonomous robots: A categorical overview

Islam

Hong

Sattar

2019

The International Journal of Robotics Research

View full text Add to dashboard Cite

A wide range of human-robot collaborative applications in diverse domains such as manufacturing, health care, the entertainment industry, and social interactions, require an autonomous robot to follow its human companion. Different working environments and applications pose diverse challenges by adding constraints on the choice of sensors, the degree of autonomy, and dynamics of a person-following robot. Researchers have addressed these challenges in many ways and contributed to the development of a large body of literature. This paper provides a comprehensive overview of the literature by categorizing different aspects of person-following by autonomous robots. Also, the corresponding operational challenges are identified based on various design choices for ground, underwater, and aerial scenarios. In addition, state-of-the-art methods for perception, planning, control, and interaction are elaborately discussed and their applicability in varied operational scenarios are presented. Then, some of the prominent methods are qualitatively compared, corresponding practicalities are illustrated, and their feasibility is analyzed for various usecases. Furthermore, several prospective application areas are identified, and open problems are highlighted for future research.

show abstract

Section: State-of-the-art Approachesmentioning

confidence: 99%

Section: State-of-the-art Approachesmentioning

confidence: 99%

Section: Map-assisted Vs Target-centric Planningmentioning

confidence: 99%

See 1 more Smart Citation

Person-following by autonomous robots: A categorical overview

Islam

Hong

Sattar

2019

The International Journal of Robotics Research

View full text Add to dashboard Cite

show abstract

“…Works such as [18], [19], or [20] utilize sensors and data processing approaches to track and follow humans. Ahn et al [21] propose a method to predict a set of recommended and safe locations based on the person's location. The researchers demonstrate that using their approach, the robot can follow the human more accurately and safely.…”

Section: Introductionmentioning

confidence: 99%

Human-Following and -guiding in Crowded Environments using Semantic Deep-Reinforcement-Learning for Mobile Service Robots

Kästner¹,

Fatloun²,

Shen³

et al. 2022

Preprint

View full text Add to dashboard Cite

Assistance robots have gained widespread attention in various industries such as logistics and human assistance. The tasks of guiding or following a human in a crowded environment such as airports or train stations to carry weight or goods is still an open problem. In these use cases, the robot is not only required to intelligently interact with humans, but also to navigate safely among crowds. Thus, especially highly dynamic environments pose a grand challenge due to the volatile behavior patterns and unpredictable movements of humans. In this paper, we propose a Deep-Reinforcement-Learningbased agent for human-guiding and -following tasks in crowded environments. Therefore, we incorporate semantic information to provide the agent with high-level information like the social states of humans, safety models, and class types. We evaluate our proposed approach against a benchmark approach without semantic information and demonstrated enhanced navigational safety and robustness. Moreover, we demonstrate that the agent could learn to adapt its behavior to humans, which improves the human-robot interaction significantly.

show abstract