Mobile robot navigation amidst humans with intents and uncertainties: A time scaled collision cone approach

Nagariya, Akhil; Gopalakrishnan, Bharath; Singh, Arun Kumar; Gupta, Krishnam; Krishna, K. Madhava

doi:10.1109/cdc.2015.7402636

Cited by 4 publications

(6 citation statements)

References 14 publications

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“…In this approach, the human predictions based on the previously planned path are used. Other approaches [14], [15] try to predict the possible human goals based on some type of reasoning and generate locally optimal motion for the robot. One of the recent approaches [16] suggests the use of probabilistic human predictions to handle various uncertainties and plan robot motion on top of these probabilistic predictions.…”

Section: Related Workmentioning

confidence: 99%

HATEB-2: Reactive Planning and Decision making in Human-Robot Co-navigation

Alami

2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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We propose a new framework combining decision making and planning in the human-robot co-navigation scenario. This new framework, called HATEB-2, introduces different modalities of planning and shift between them based on the situation at hand. These transitions are controlled by the decision making loop present on top of the planning. We also present the improvements made to human prediction and estimation along with the modifications to a few social constraints from our previous work, that are included in HATEB-2. Finally, several experiments are performed in human-robot co-navigation scenarios and results are presented. One of the modalities of HATEB-2 is used in EU-funded MuMMER [1] project (http://mummer-project.eu/).

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Section: Related Workmentioning

confidence: 99%

HATEB-2: Reactive Planning and Decision making in Human-Robot Co-navigation

Alami

2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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“…Using such probability set, this approach predicts human motion towards most probable goal and generates locally optimal motions for multiple robots. The time scaled collision cone based approach aims to solve the same problem, albeit giving same treatment to human and non-human obstacles [25]. While being effective in densely crowded environments, as a virtue of remaining purely reactive, such approaches could lead to needless detours in intricate situations.…”

Section: Planning For the Robot And The Humanmentioning

confidence: 99%

Viewing Robot Navigation in Human Environment as a Cooperative Activity

Khambhaita

Alami

2019

Springer Proceedings in Advanced Robotics

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We claim that navigation in human environments can be viewed as cooperative activity especially in constrained situations. Humans concurrently aid and comply with each other while moving in a shared space. Cooperation helps pedestrians to efficiently reach their own goals and respect conventions such as the personal space of others. To meet human comparable efficiency, a robot needs to predict the human trajectories and plan its own trajectory correspondingly in the same shared space. In this work, we present a navigation planner that is able to plan such cooperative trajectories, simultaneously enforcing the robot's kinematic constraints and avoiding other non-human dynamic obstacles. Using robust social constraints of projected time to a possible future collision, compatibility of human-robot motion direction, and proxemics, our planner is able to replicate human-like navigation behavior not only in open spaces but also in confined areas. Besides adapting the robot trajectory, the planner is also able to proactively propose co-navigation solutions by jointly computing human and robot trajectories within the same optimization framework. We demonstrate richness and performance of the cooperative planner with simulated and real world experiments on multiple interactive navigation scenarios.

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“…The initial position of the goal point was (0, 4). After 70 s, the position of the goal point changed to (−1, −0.2); after 130 s, it changed to (1, 0.1); after 150 s, it changed to (−1, 4); after 200 s, it changed to (1,0). The robot avoided the obstacle and moved to the goal point.…”

Section: Navigation Within Indoor Environment With Dynamic Obstaclesmentioning

confidence: 99%

“…Therefore, navigation is applied to various types of robots, such as mobile robots, humanoid robots, and quadcopters. Moreover, research on robot navigation has been actively conducted .…”

Section: Introductionmentioning

confidence: 99%

Optimization-based humanoid robot navigation using monocular camera within indoor environment

Han¹,

Kim

Hong

2018

ETRI Journal

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Robot navigation allows robot mobility. Therefore, mobility is an area of robotics that has been actively investigated since robots were first developed. In recent years, interest in personal service robots for homes and public facilities has increased. As a result, robot navigation within the home environment, which is an indoor environment, is being actively investigated. However, the problem with conventional navigation algorithms is that they require a large computation time for their building mapping and path planning processes. This problem makes it difficult to cope with an environment that changes in real‐time. Therefore, we propose a humanoid robot navigation algorithm consisting of an image processing and optimization algorithm. This algorithm realizes navigation with less computation time than conventional navigation algorithms using map building and path planning processes, and can cope with an environment that changes in real‐time.

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Mobile robot navigation amidst humans with intents and uncertainties: A time scaled collision cone approach

Cited by 4 publications

References 14 publications

HATEB-2: Reactive Planning and Decision making in Human-Robot Co-navigation

HATEB-2: Reactive Planning and Decision making in Human-Robot Co-navigation

Viewing Robot Navigation in Human Environment as a Cooperative Activity

Optimization-based humanoid robot navigation using monocular camera within indoor environment

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