Time constrained randomized path planning using spatial networks

Lum, Christopher W.; Rysdyk, Rolf

doi:10.1109/acc.2008.4587073

Cited by 7 publications

(7 citation statements)

References 19 publications

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“…The modular nature of the algorithm benefits analysis since each subproblem can be analyzed independently. This paper focuses on a solution for subproblem (℘ 2 ) and assumes that a predictive algorithm [31] is available to solve (℘ 1 ) and a path planning algorithm [24] exists to solve (℘ 3 ). Furthermore, d affects subproblem (℘ 1 ) and (℘ 3 ) more than (℘ 2 ) and therefore methods to choose its value is beyond the scope of this work.…”

Section: A Algorithm Overviewmentioning

confidence: 99%

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Search Algorithm for Teams of Heterogeneous Agents with Coverage Guarantees

Lum

Vagners

Rysdyk³

2010

Journal of Aerospace Computing, Information, and Communication

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Among common intelligence, reconnaissance, and surveillance tasks, searching for a target in a complex environment is a problem for which autonomous systems are well suited. This work considers the problem of searching for targets using a team of heterogeneous agents. The system maintains a grid-based world model which contains information about the probability that a target is located in a given cell of the map. Agents formulate control decisions for a fixed number of time steps using a modular algorithm that allows parameterizations of agent capabilities. This paper investigates a solution that guarantees total map coverage. The control law for each agent does not require explicit knowledge of other agents. This yields a system which is scalable to a large number of vehicles. The resulting search patterns guarantee an exhaustive search of the map in the sense that all cells will be searched sufficiently to ensure that the probability of a target going unnoticed is driven to zero. Modifications to this algorithm for explicit cooperation between agents is also investigated. Nomenclature Bset ofz values defining spatial domain of occupancy based map B R locations reachable by agent in d steps Bset ofz values defining center of occupancy based map cells B R cell centers reachable by agent in d steps d prediction horizon, number of waypoints in path d i minimum distance from generator i to setB max f χ ( ) reward function for course deviation in (℘ 2 ) f d ( ) reward function for distance in (℘ 2 ) f h ( ) reward function for high score cell in (℘ 2 ) h sensor reliability factor in range [0, 1] I index of agent closer toB max than any other agent In (m) indices corresponding to runs where at least m agents find the target J 0 ( ) total reward function for (℘ 2 )

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Section: A Algorithm Overviewmentioning

confidence: 99%

“…The first [23] (generating a predictive world estimate) and third [24] (path planning) steps have already been addressed in previous works.…”

Section: Introductionmentioning

confidence: 98%

Search Algorithm for Teams of Heterogeneous Agents with Coverage Guarantees

Lum

Vagners

Rysdyk³

2010

Journal of Aerospace Computing, Information, and Communication

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“…Due to their simplicity, grid-based representations are widely used in robotics for the creation of occupancy maps. These representations can be addressed from a 2D [38][39][40] or 3D [41] point of view. For example, Li et al [40] structure the navigable space as 2D squared cells represented by one node and identified by an attribute according to the building element they represent.…”

Section: Discretization Of the Navigable Spacementioning

confidence: 99%

From BIM to Scan Planning and Optimization for Construction Control

et al. 2019

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Scan planning of buildings under construction is a key issue for an efficient assessment of work progress. This work presents an automatic method aimed to determinate the optimal scan positions and the optimal route based on the use of Building Information Models (BIM) and considering data completeness as stopping criteria. The method is considered for a Terrestrial Laser Scanner mounted on a mobile robot following a stop & go procedure. The method starts by extracting floor plans from the BIM model according to the planned construction status, and including geometry and semantics of the building elements considered for construction control. The navigable space is defined from a binary map considering a security distance to building elements. After a grid-based and a triangulation-based distribution are implemented for generating scan position candidates, a visibility analysis is carried out to determine the optimal number and position of scans. The optimal route to visit all scan positions is addressed by using a probabilistic ant colony optimization algorithm. The method has been tested in simulated and real buildings under very dissimilar conditions and structural construction elements. The two approaches for generating scan position candidates are evaluated and results show the triangulation-based distribution as the more efficient approach in terms of processing and acquisition time, especially for large-scale buildings.

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“…Until this technology is verified, validated, and disseminated, UAS operators are currently responsible for maintaining inter-aircraft spacing and ensuring that conflicts and collisions do not occur [11]. Maintaining situational awareness with respect to current and impending conflicts with both aircraft and restricted airspaces can significantly increase operator workload, thereby reducing the operator's bandwidth to focus on other mission tasks such as search and rescue [12], [13], [14] path planning [15], or maintaining formation [16]. Various groups have looked at human/machine interfaces to increase efficiency and synergy between the operator and the automation [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Conservative algorithms for automated collision awareness for multiple Unmanned Aerial Systems

Ueunten

Lum

Creigh

et al. 2015

2015 IEEE Aerospace Conference

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As the Federal Aviation Administration (FAA) prepares to integrate Unmanned Aerial Systems (UAS) into the National Airspace System (NAS), developing technologies that mitigate the risk associated with UAS collisions have become a top priority. Despite advances in detect and avoid technologies, the UAS operator remains the primary controller responsible for maintaining inter-vehicle separation and ensuring conflicts do not occur. This paper examines a collision awareness system which increases the operator's situational awareness by spatially and temporally predicting conflicts between the UAS and entities such as other aviation traffic or restricted airspaces. By modeling entities as 3D point masses, the system can be implemented for various, dissimilar UASs. Furthermore, the system supports aircraft engaged in different flight modes such as free flight, following a flight path, and orbit/loiter behavior. Mixed Gaussian distributions model each entity's future position, where the mean is determined by 3D kinematic motion and the covariance is determined by a continuous time error propagation model. Convolving these mixed distribution with another entity or airspace yields mathematically conservative future conflict estimates. Scenarios are presented to demonstrate the algorithm's capabilities.

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Time constrained randomized path planning using spatial networks

Cited by 7 publications

References 19 publications

Search Algorithm for Teams of Heterogeneous Agents with Coverage Guarantees

Search Algorithm for Teams of Heterogeneous Agents with Coverage Guarantees

From BIM to Scan Planning and Optimization for Construction Control

Conservative algorithms for automated collision awareness for multiple Unmanned Aerial Systems

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