3D path planning with novel multiple 2D layered approach for complex human-robot interaction

2009 ◽  
Author(s):  
Thomas A. Smith ◽  
Rui C. V. Loureiro ◽  
William S. Harwin
Keyword(s):  
Path Planning ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Layered Approach

A Multimodal Path Planning Approach to Human Robot Interaction Based on Integrating Action Modeling

2020 ◽  
Vol 100 (3-4) ◽  
pp. 955-972
Author(s):  
Yosuke Kawasaki ◽  
Ayanori Yorozu ◽  
Masaki Takahashi ◽  
Enrico Pagello
Keyword(s):  
Path Planning ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Planning Approach

A Multi-Objective Optimization Approach for Multi-Vehicle Path Planning Problems considering Human-Robot Interactions

2022 ◽  
pp. 1-14
Author(s):  
Venkata Sirimuvva Chirala ◽  
Saravanan Venkatachalam ◽  
Jonathon Smereka ◽  
Sam Kassoumeh
Keyword(s):  
Path Planning ◽  
Human Robot Interaction ◽  
Planning Problem ◽  
Optimization Approach ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Robot Interaction ◽  
Ground Vehicle ◽  
Multi Objective ◽  
Vehicle Path

Abstract There has been unprecedented development in the field of unmanned ground vehicles (UGVs) over the past few years. UGVs have been used in many fields including civilian and military with applications such as military reconnaissance, transportation, and search and research missions. This is due to their increasing capabilities in terms of performance, power, and tackling risky missions. The level of autonomy given to these UGVs is a critical factor to consider. In many applications of multi-robotic systems like “search-and-rescue” missions, teamwork between human and robots is essential. In this paper, given a team of manned ground vehicles (MGVs) and unmanned ground vehicles (UGVs), the objective is to develop a model which can minimize the number of teams and total distance traveled while considering human-robot interaction (HRI) studies. The human costs of managing a team of UGVs by a manned ground vehicle (MGV) are based on human-robot interaction (HRI) studies. In this research, we introduce a combinatorial, multi objective ground vehicle path planning problem which takes human-robot interactions into consideration. The objective of the problem is to find: ideal number of teams of MGVs-UGVs that follow a leader-follower framework where a set of UGVs follow an MGV; and path for each team such that the missions are completed efficiently.

scholarly journals Socially aware path planning for mobile robots

Robotica ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 513-526 ◽  
Author(s):  
Sarath Kodagoda ◽  
Stephan Sehestedt ◽  
Gamini Dissanayake
Keyword(s):  
Path Planning ◽  
Model Building ◽  
Human Motion ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Partial Observations ◽  
Motion Patterns ◽  
Office Environment ◽  
Planning Algorithm ◽  
Path Planning Algorithm

SUMMARYHuman–robot interaction is an emerging area of research where a robot may need to be working in human-populated environments. Human trajectories are generally not random and can belong to gross patterns. Knowledge about these patterns can be learned through observation. In this paper, we address the problem of a robot's social awareness by learning human motion patterns and integrating them in path planning. The gross motion patterns are learned using a novel Sampled Hidden Markov Model, which allows the integration of partial observations in dynamic model building. This model is used in the modified A* path planning algorithm to achieve socially aware trajectories. Novelty of the proposed method is that it can be used on a mobile robot for simultaneous online learning and path planning. The experiments carried out in an office environment show that the paths can be planned seamlessly, avoiding personal spaces of occupants.

scholarly journals Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map

2020 ◽  
Vol 10 (24) ◽  
pp. 8991
Author(s):  
Jiadong Zhang ◽  
Wei Wang ◽  
Xianyu Qi ◽  
Ziwei Liao
Keyword(s):  
Path Planning ◽  
Success Rate ◽  
Autonomous Navigation ◽  
Indoor Navigation ◽  
Human Robot Interaction ◽  
Target Space ◽  
Service Robots ◽  
Robot Interaction ◽  
Topological Map ◽  
Semantic Grid

For the indoor navigation of service robots, human–robot interaction and adapting to the environment still need to be strengthened, including determining the navigation goal socially, improving the success rate of passing doors, and optimizing the path planning efficiency. This paper proposes an indoor navigation system based on object semantic grid and topological map, to optimize the above problems. First, natural language is used as a human–robot interaction form, from which the target room, object, and spatial relationship can be extracted by using speech recognition and word segmentation. Then, the robot selects the goal point from the target space by object affordance theory. To improve the navigation success rate and safety, we generate auxiliary navigation points on both sides of the door to correct the robot trajectory. Furthermore, based on the topological map and auxiliary navigation points, the global path is segmented into each topological area. The path planning algorithm is carried on respectively in every room, which significantly improves the navigation efficiency. This system has demonstrated to support autonomous navigation based on language interaction and significantly improve the safety, efficiency, and robustness of indoor robot navigation. Our system has been successfully tested in real domestic environments.

A Novel Cognitive Approach for Measuring the Trust in Robots

2019 ◽  
Vol 12 (3) ◽  
pp. 60-73
Author(s):  
Akash Dutt Dubey ◽  
Bimal Aklesh Kumar
Keyword(s):  
Path Planning ◽  
Empirical Evaluation ◽  
Trust Model ◽  
Human Robot Interaction ◽  
Learning Cycle ◽  
Cognitive Approach ◽  
Robot Interaction ◽  
Human Capabilities ◽  
Trust Relationship

One of the major challenges in human-robot interaction is to determine the trustworthiness of the robot. In order to enhance and augment the human capabilities by establishing a human robot partnership, it is important to evaluate the reliability and dependability of the robots for the specific tasks. The trust relationship between the human and robot becomes critical especially in the cases where there is strong cohesion between humans and robots. In this article, a cognition based-trust model has been developed which measures the trust and other related cognitive parameters of the robot. This trust model has been applied on a customized robot which performs path planning tasks using three different algorithms. The simulation of the model has been done to evaluate the trust of the robot for the three algorithms. The results show that with each learning cycle of each method, the trust of the robot increases. An empirical evaluation has also been done to validate the model.

Augmenting an Educational and Research Human Robot Interaction Environment With a Multi-Obstacle Avoidance Algorithm

2015 ◽  
Author(s):  
Christopher E. Ábrego
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Current State ◽  
Planning Algorithm ◽  
Holonomic Robot ◽  
Multiple Interaction ◽  
Path Planning Algorithm ◽  
Two Degree Of Freedom

In this manuscript, the development and current state of an inexpensive platform for educational purposes and research in the interaction between humans and robots (human-robot interaction) is described. The platform is based on the ubiquitous LabVIEW programming language and an in-house developed two degree of freedom non-holonomic robot. The platform includes multiple interaction modalities, which will be described, between the robot and the user. The procedures followed for the successful software and hardware implementation are explicated. Furthermore, a demonstration of an obstacle avoidance path planning algorithm for a single obstacle is validated in hardware, as well as simulation demonstration of the multiple obstacle avoidance algorithm. These implementations to the platform further demonstrate the ease of augmenting the existing platform to additional modalities. The algorithm uses a vision acquisition system to identify the location and size of an obstacle, in addition to orientation patterns and calibration points, in the workspace and generate the robot path to reach a desired goal while avoiding the obstacle. The manuscript describes into the current research of path planning in the presence of multiple obstacles. The development of a set of criteria, Generation Succession, Arrival Departure, and Side Consistency, for the algorithm are elucidated in the manuscript. The algorithm has been demonstrated to be successful in simulation by avoiding multiple obstacle in various layouts.

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