Socially aware path planning for mobile robots

Kodagoda, Sarath; Sehestedt, Stephan; Dissanayake, Gamini

doi:10.1017/s0263574714001611

Cited by 4 publications

(5 citation statements)

References 21 publications

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“…Robot navigation is one of the most important functions for human-symbiotic autonomous mobile robots that are expected to provide various services such as logistics [1], housework for elderly or handicap people [2,3], personal assistance in an office [4], and attending to a person in a museum or airport lobby [5]. Such mobile robots require the ability to safely move with reasonable efficiency in arbitrary human-coexisting environments, which include relatively open environments, e.g., a city squire (Fig.…”

Section: Introductionmentioning

confidence: 99%

A Preliminary Study of Interactive Navigation Framework with Situation-Adaptive Multimodal Inducement: Pass-By Scenario

Kamezaki

Kobayashi

Yokoyama

et al. 2019

Int J of Soc Robotics

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Human-aware navigation is an essential requirement for autonomous robots in human-coexisting environments. The goal of conventional navigation is to find a path for a robot to pass through safely and efficiently without colliding with human. Note that if such a path cannot be found, the robot stops until a path is clear. Thus, such collision-avoidance based passive navigation does not work in a congested or narrow space. To avoid this freezing problem, the robot should induce humans to make a space for passing by an adequate inducement method, such as body movement, speech, and touch, depending on the situation. A robot that deliberately clears a path with such actions may make humans uncomfortable, so the robot should also utilize inducements to avoid causing negative feelings. In this study, we propose a fundamental framework of interactive navigation with situation-adaptive multimodal inducement. For a preliminary study, we target a passing scenario in a narrow corridor where two humans are standing and adopt a model-based approach focusing on common parameters. The suitable inducement basically varies depending on the largest space through which a robot can pass, distance between the robot and a human, and human behavior such as conversing. We thus develop a situation-adaptive inducement selector on the basis of the relationship between human-robot proximity and allowable inducement strength, considering robot efficiency and human psychology. The proposed interactive navigation system was tested across some contextual scenarios and compared with a fundamental path planner. The experimental results indicated that the proposed system solved freezing problems, provided a safe and efficient trajectory, and improved humans' psychological reaction although the evidence was limited to robot planner and hardware design we used as well as certain scenes, contexts, and participants.

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Section: Introductionmentioning

confidence: 99%

A Preliminary Study of Interactive Navigation Framework with Situation-Adaptive Multimodal Inducement: Pass-By Scenario

Kamezaki

Kobayashi

Yokoyama

et al. 2019

Int J of Soc Robotics

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show abstract

“…Using the same initial values, convolution-based trajectory planning for a smooth Bezier curve was conducted for the planned path. The sampling time was configured to 20 ms. Figure 8 shows the central velocity profile generated using the recursive form of convolution in (5) and its incorporation with a Bezier curve using (8).…”

Section: Simulation Examplementioning

confidence: 99%

“…In a known environment, mobile robot navigation is a systematic operation composed of three main phases: path planning [4][5], trajectory generation [6][7] and tracking control [8]. Path planning considers the generation of a predetermined path for the mobile robot to follow from an initial point to a desired terminal point in a working environment.…”

Section: Introductionmentioning

confidence: 99%

A Practical Joint-Space Trajectory Generation Method Based on Convolution in Real-Time Control

Yang

Delgado

Choi

2016

International Journal of Advanced Robotic Systems

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This paper proposes a joint-space trajectory generation method for practical navigation with a high curvature path of mobile robots. A technique to generate central velocity commands using a convolution operator that considers only the physical limits of a mobile robot was discussed. In practical application, controlling the heading angles along a curved path is required and the existence of obstacles is inevitable. First, we suggested an algorithm that generates a trajectory to consider the heading angles along a smooth Bezier curve by redefinition of the curve parameter. However, the presence of an obstacle along the planned path requires redirection to a new path where geometrical limitations such as high curvature turning points exist, resulting in tracking error. We propose a method that manages a variation of linear interpolation to generate a feasible trajectory while conserving the high curvature path and the merits of convolution. Joint-space trajectories are produced by scaling down the generated central velocity through reduction of the given maximum velocity limit. We show through a simulation example that the proposed method is able to generate a trajectory that can accurately track a planned path on a designed platform based on actual parameters. Finally, an experiment is successfully conducted on a two-wheeled mobile robot, Tetra DS-III, in a real-time control system. The experiment results display distinct advantages in the criteria of time optimality and periodicity of control tasks, while conserving all possible limitations that could occur during navigation compared with previous studies.

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“…Current mobile robots demonstrate the capability to navigate safely within their operational environments, effectively avoiding both static and dynamic obstacles [5]. However, from the human perspective, there is a preference for maintaining a certain distance from robots to ensure safety and comfort within the environment [6, 7]. Thus, treating humans merely as dynamic obstacles to be avoided is insufficient.…”

Section: Introductionmentioning

confidence: 99%

A novel Human-Aware Navigation algorithm based on behavioral intention cognition

Li,

Zhang,

2024

Robotica

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In order to ensure safe and comfortable human–robot navigation in close proximity, it is imperative for robots to possess the capability to understand human behavioral intention. With this objective in mind, this paper introduces a Human-Aware Navigation (HAN) algorithm. The HAN system combines insights from studies on human detection, social behavioral model, and behavior prediction, all while incorporating social distance considerations. This information is integrated into a layer dedicated to human behavior intention cognition, achieved through the fusion of data from laser radar and Kinect sensors, employing Gaussian functions to account for individual private space and movement trend. To cater to the mapping requirements of the HAN system, we have reduced the computational complexity associated with traditional multilayer cost map by implementing a “first-come, first-served” expansion method. Subsequently, we have enhanced the trajectory optimization equation by incorporating an improved dynamic triangle window method that integrates human behavior intention cognition, leading to the determination of an appropriate trajectory for the robot. Finally, experimental evaluations have been conducted to assess and validate the efficacy of the human behavior intention cognition and the HAN system. The results clearly demonstrate that the HAN system outperforms the traditional Dynamic Window Approach algorithm in ensuring the safety and comfort of humans in human–robot coexistence environments.

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Socially aware path planning for mobile robots

Cited by 4 publications

References 21 publications

A Preliminary Study of Interactive Navigation Framework with Situation-Adaptive Multimodal Inducement: Pass-By Scenario

A Preliminary Study of Interactive Navigation Framework with Situation-Adaptive Multimodal Inducement: Pass-By Scenario

A Practical Joint-Space Trajectory Generation Method Based on Convolution in Real-Time Control

A novel Human-Aware Navigation algorithm based on behavioral intention cognition

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