Optimal trajectory generation under homology class constraints

Kim, Soonkyum; Sreenath, Koushil; Bhattacharya, Subhrajit; Kumar, Vijay

doi:10.1109/cdc.2012.6425970

Cited by 19 publications

(13 citation statements)

References 15 publications

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“…Topological frameworks have been also employed on applications in sensor networks and robotics. Particularly, these methods have been used for coverage and hole detection in sensor networks [39,40], motion planning [41], and localization [42,43]. However, these studies focused on stationary networks, and are mainly concerned about the coverage holes in the sensing domain of the network rather than the characterization of the physical environment itself.…”

Section: Related Workmentioning

confidence: 99%

A framework for mapping with biobotic insect networks: From local to global maps

Dirafzoon

Bozkurt

Lobatón

2017

Robotics and Autonomous Systems

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We present an approach for global exploration and mapping of unknown environments using a swarm of cyborg insects, known as biobots, for emergency response scenarios under minimal sensing and localization constraints. We exploit natural stochastic motion models and controlled locomotion of biobots in conjunction with an aerial leader to explore and map a domain of interest. A sliding window strategy is adopted to construct local maps from coordinate free encounter information of the agents by means of local metric estimation. Robust topological features from these local representations are extracted using topological data analysis and a classification scheme. These maps are then merged into a global map which can be visualized using a graphical representation, that integrates geometric as well as topological features of the environment. Simulation and experimental results with biologically inspired robotic platform are presented to illustrate and verify the correctness of our approach, which provides building blocks for SLAM with biobotic insects.

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Section: Related Workmentioning

confidence: 99%

A framework for mapping with biobotic insect networks: From local to global maps

Dirafzoon

Bozkurt

Lobatón

2017

Robotics and Autonomous Systems

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“…This kind of problems often requires that each robot follow different paths to cover or sense the whole workspace as in search-and-rescue problems [44]. It is usually formulated as finding navigation pathes under homotopy class constraints [45], [46]. Unfortunately, existing attempts at classifying navigation paths of different homotopy classes require centralized operations and computation, yielding them infeasible for distributed sensor networks as discussed in this paper.…”

Section: Path Planning and Navigation Of Robotsmentioning

confidence: 99%

CANS: Towards Congestion-Adaptive and Small Stretch Emergency Navigation with Wireless Sensor Networks

Wang

Lin

Jiang

2016

IEEE Trans. on Mobile Comput.

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One of the major applications of wireless sensor networks (WSNs) is the navigation service for emergency evacuation, the goal of which is to assist people in escaping from a hazardous region safely and quickly when an emergency occurs. Most existing solutions focus on finding the safest path for each person, while ignoring possible large detours and congestions caused by plenty of people rushing to the exit. In this paper, we present CANS, a Congestion-Adaptive and small stretch emergency Navigation algorithm with WSNs. Specifically, CANS leverages the idea of level set method to track the evolution of the exit and the boundary of the hazardous area, so that people nearby the hazardous area achieve a mild congestion at the cost of a slight detour, while people distant from the danger avoid unnecessary detours. CANS also considers the situation in the event of emergency dynamics by incorporating a local yet simple status updating scheme. To the best of our knowledge, CANS is the first WSN-assisted emergency navigation algorithm achieving both mild congestion and small stretch, where all operations are in-situ carried out by cyber-physical interactions among people and sensor nodes. CANS does not require location information, nor the reliance on any particular communication model. It is also distributed and scalable to the size of the network with limited storage on each node. Both experiments and simulations validate the effectiveness and efficiency of CANS.

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“…We are principally interested in the homology classes of H 1 (K) because these correspond to trajectory cycles in a given space. Intuitively two cycles are homologous if the region they enclose contains no holes [33], [30]. As such we subsequently use the term homology classes when referring to homology classes of H 1 (K).…”

Section: Upper Star Filtrationmentioning

confidence: 99%

Unsupervised trajectory compression

Corcoran

Mooney

Huang

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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We present a method for compressing trajectories in an unsupervised manner. Given a set of trajectories sampled from a space we construct a basis for compression whose elements correspond to paths in the space which are topologically distinct. This is achieved by computing a canonical representative for each element in a generating set for the first homology group and decomposing these representatives into a set of distinct paths. Trajectory compression is subsequently accomplished through representation in terms of this basis. Robustness with respect to outliers is achieved by only considering those elements of the first homology group which exist in the super-level sets of the Kernel Density Estimation (KDE) above a threshold. Robustness with respect to small scale topological artifacts is achieved by only considering those elements of the first homology group which exist for a sufficient range in the super-level sets. We demonstrate this approach to trajectory compression in the context of a large set of crowd-sourced GPS trajectories captured in the city of Chicago. On this set, the compression method achieves a mean geometrical accuracy of 108 meters with a compression ratio of over 12.

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Optimal trajectory generation under homology class constraints

Cited by 19 publications

References 15 publications

A framework for mapping with biobotic insect networks: From local to global maps

A framework for mapping with biobotic insect networks: From local to global maps

CANS: Towards Congestion-Adaptive and Small Stretch Emergency Navigation with Wireless Sensor Networks

Unsupervised trajectory compression

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