Using state dominance for path planning in dynamic environments with moving obstacles

González, Juan Pablo Nieto; Dornbush, Andrew; Likhachev, Maxim

doi:10.1109/icra.2012.6224773

Cited by 20 publications

(16 citation statements)

References 9 publications

(10 reference statements)

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“…Naturalness [1,6,7,13,27,28,35,37,38,43,45,48,49,50,53,54,79,62,63,66,67,68,69,70,71,72,81,82,83,84,87,88,89,90,91,93,98,99]…”

Section: Challenges Of Human-aware Navigationmentioning

confidence: 99%

“…The temporal dimension in that work is segmented into safe and unsafe intervals instead of regularly discretized time, reducing the search space expansion. Gonzalez et al [28] enhance that idea allowing for continous cost functions as well, a prerequisite to apply social cost functions to search.…”

Section: Temporal Planningmentioning

confidence: 99%

“…Behavior Selection [1,17,19,27,30,33,34,35,43,56,79,69,70,81] 14 Global Planning [6,7,21,28,30,37,38,43,44,48,49,50,54,56,57,59,60,62,65,69,70,71,83,84,87,88,89,90,91,92,97,104,106]…”

mentioning

confidence: 99%

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Human-aware robot navigation: A survey

Kruse

Pandey

Alami

et al. 2013

Robotics and Autonomous Systems

638

374

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Navigation is a basic skill for autonomous robots. In the last years human-robot interaction has become an important research field that spans all of the robot capabilities including perception, reasoning, learning, manipulation and navigation. For navigation, the presence of humans requires novel approaches that take into account the constraints of human comfort as well as social rules. Besides these constraints, putting robots among humans opens new interaction possibilities for robots, also for navigation tasks, such as robot guides. This paper provides a survey of existing approaches to human-aware navigation and offers a general classification scheme for the presented methods.

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“…Naturalness [1,6,7,13,27,28,35,37,38,43,45,48,49,50,53,54,79,62,63,66,67,68,69,70,71,72,81,82,83,84,87,88,89,90,91,93,98,99]…”

Section: Challenges Of Human-aware Navigationmentioning

confidence: 99%

Section: Temporal Planningmentioning

confidence: 99%

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Human-aware robot navigation: A survey

Kruse

Pandey

Alami

et al. 2013

Robotics and Autonomous Systems

638

374

View full text Add to dashboard Cite

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“…Early works explored control sampling approaches [240], and recent state sampling works have made model simplifications to handle the differential constraints in an online manner, such as keeping to a constant ego robot speed [251]. Others have performed planning by graph search over a discrete, time-bounded lattice structure built from motion primitives [252], or a grid cell decomposition of the state space [253], though these methods loose the benefits of sampling based approaches (which are less limited in resolution, and have potential for rewiring and optimization).…”

Section: Planning Subject To Differential Constraintsmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efficiency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehicles. The objective of this paper is to provide a general overview of the recent developments in the realm of autonomous vehicle software systems. Fundamental components of autonomous vehicle software are reviewed, and recent developments in each area are discussed.

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“…Therefore, it is mandatory to constrain the path searches only to high priority space during pathfinding to effectively resolve the efficiency and optimality trade-off. Various approaches have been proposed to speed-up the path computations, such as hierarchical abstractions [42][43][44], symmetry breaking [45,46], jump point search [47][48][49][50], sub-goal graphs [51], compressed path databases [52,53], accurate heuristics [54], swamp hierarchies [55], pruning dominant states [56], influence-aware pathfinders [57], and constraints-aware navigation (CAN) [58]. Despite the success of such enhancements, in most cases, either many locations of the maps are searched needlessly, or path length degrades.…”

Section: Introductionmentioning

confidence: 99%

A Fast Global Flight Path Planning Algorithm Based on Space Circumscription and Sparse Visibility Graph for Unmanned Aerial Vehicle

Majeed

Lee

2018

Electronics

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This paper proposes a new flight path planning algorithm that finds collision-free, optimal/near-optimal and flyable paths for unmanned aerial vehicles (UAVs) in three-dimensional (3D) environments with fixed obstacles. The proposed algorithm significantly reduces pathfinding computing time without significantly degrading path lengths by using space circumscription and a sparse visibility graph in the pathfinding process. We devise a novel method by exploiting the information about obstacle geometry to circumscribe the search space in the form of a half cylinder from which a working path for UAV can be computed without sacrificing the guarantees on near-optimality and speed. Furthermore, we generate a sparse visibility graph from the circumscribed space and find the initial path, which is subsequently optimized. The proposed algorithm effectively resolves the efficiency and optimality trade-off by searching the path only from the high priority circumscribed space of a map. The simulation results obtained from various maps, and comparison with the existing methods show the effectiveness of the proposed algorithm and verify the aforementioned claims.

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Using state dominance for path planning in dynamic environments with moving obstacles

Cited by 20 publications

References 9 publications

Human-aware robot navigation: A survey

Human-aware robot navigation: A survey

Perception, Planning, Control, and Coordination for Autonomous Vehicles

A Fast Global Flight Path Planning Algorithm Based on Space Circumscription and Sparse Visibility Graph for Unmanned Aerial Vehicle

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