Social Navigation in a Cognitive Architecture Using Dynamic Proxemic Zones

Ginés, Jonatan; Martı́n, Francisco; Vargas, David; Rodríguez, Francisco; Olivera, Vicente Matellán

doi:10.3390/s19235189

Cited by 19 publications

(17 citation statements)

References 41 publications

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“…Authors declare that the accuracy on facial expression recognition in the wild is considerably lower than the experiments which have been conducted under controlled laboratory conditions. In the context of navigation algorithm for robots, authors of [58] propose a mood detector component. This component takes the image as input and generates a list of emotions detected in the faces existing in the image.…”

Section: B Emotion Recognition In Social Robotsmentioning

confidence: 99%

Adaptive Multimodal Emotion Detection Architecture for Social Robots

et al. 2022

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Emotion recognition is a strategy for social robots used to implement better Human-Robot Interaction and model their social behaviour. Since human emotions can be expressed in different ways (e.g., face, gesture, voice), multimodal approaches are useful to support the recognition process. However, although there exist studies dealing with multimodal emotion recognition for social robots, they still present limitations in the fusion process, dropping their performance if one or more modalities are not present or if modalities have different qualities. This is a common situation in social robotics, due to the high variety of the sensory capacities of robots; hence, more flexible multimodal models are needed. In this context, we propose an adaptive and flexible emotion recognition architecture able to work with multiple sources and modalities of information and manage different levels of data quality and missing data, to lead robots to better understand the mood of people in a given environment and accordingly adapt their behaviour. Each modality is analyzed independently to then aggregate the partial results with a previous proposed fusion method, called EmbraceNet+, which is adapted and integrated to our proposed framework. We also present an extensive review of state-of-the-art studies dealing with fusion methods for multimodal emotion recognition approaches. We evaluate the performance of our proposed architecture by performing different tests in which several modalities are combined to classify emotions using four categories (i.e., happiness, neutral, sadness, and anger). Results reveal that our approach is able to adapt to the quality and presence of modalities. Furthermore, results obtained are validated and compared with other similar proposals, obtaining competitive performance with state-of-the-art models.

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Section: B Emotion Recognition In Social Robotsmentioning

confidence: 99%

Adaptive Multimodal Emotion Detection Architecture for Social Robots

et al. 2022

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“…This method allows the social robots to move among rooms. A novel navigation algorithm for social robots that is acceptable to people is proposed in [6]. By detecting the attitudes of people towards robots, the method establishes the range of influence to generate robot paths that have less impact on human activities.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Path Planning Method for Social Robots in the Home Environment

Zhang

Zhou

et al. 2020

Electronics

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The home environment is a typical dynamic environment with moveable obstacles. The social robots working in home need to search for feasible paths in this complex dynamic environment. In this work, we propose an improved RRT algorithm to plan feasible path in home environment. The algorithm pre-builds a tree that covers the whole map and maintains the effectiveness of all nodes with branch pruning, reconnection, and regrowth process. The method forms a path by searching the nearest node in the tree and then quickly accessing the nodes near the destination. Due to the effectiveness-maintaining process, the proposed method can effectively deal with the complex dynamic environment where the destination and multiple moving obstacles change simultaneously. In addition, our method can be extended to the path-planning problem in 3D space. The simulation experiments verify the effectiveness of the algorithm.

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“…In each state, is set the goal to be achieved by the robot. The central (Gins et al 2019). It takes the context awareness information from the knowledge graph to feed the planning tier part of the architecture, at Tier 2, is a symbolic planner based on PDDL that generates the plan and executes it by delivering the action to the next tiers.…”

Section: Framework Descriptionmentioning

confidence: 99%

“…These last three layers could be associated with the Reactive layer in a hybrid framework. A detailed explanation of the architecture was presented in Gins et al (2019).…”

Section: Framework Descriptionmentioning

confidence: 99%

Depicting probabilistic context awareness knowledge in deliberative architectures

et al. 2020

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Facing long-term autonomy with a cognitive architecture raises several difficulties for processing symbolic and sub-symbolic information under different levels of uncertainty, and deals with complex decision-making scenarios. For reducing environment uncertainty and simplify the decision-making process, this paper establishes a method for translating robot knowledge to a conceptual graph to later extract probabilistic context information that allows to bound of the actions present at the deliberative layer. This research develops two ROS components, one for translating robot knowledge to the conceptual graphs and one for extracting context knowledge from this graph using Bayesian networks. We evaluate these components in a real-world scenario, performing a task where a robot notifies to a user a message of an event at home. Our results show an improvement in task completion when using our approach, decreasing the planning requests by 65% and doing the task in a third of the time.

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Social Navigation in a Cognitive Architecture Using Dynamic Proxemic Zones

Cited by 19 publications

References 41 publications

Adaptive Multimodal Emotion Detection Architecture for Social Robots

Adaptive Multimodal Emotion Detection Architecture for Social Robots

A Dynamic Path Planning Method for Social Robots in the Home Environment

Depicting probabilistic context awareness knowledge in deliberative architectures

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