Risk-optimal path planning in stochastic dynamic environments

Subramani, Deepak N.; Lermusiaux, Pierre F. J.

doi:10.1016/j.cma.2019.04.033

Cited by 40 publications

(24 citation statements)

References 37 publications

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“…Additional areas of development for VISIR may include some problems already addressed through the LSE, such as interceptions and multi-waypoint missions [29], [55], [56], energy optimization [54], onboard learning [57], and clustering of vessels to low-risk routes in uncertain flow environments [67]- [69].…”

Section: Discussionmentioning

confidence: 99%

Graph-Search and Differential Equations for Time-Optimal Vessel Route Planning in Dynamic Ocean Waves

Mannarini

Subramani

Lermusiaux

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Time-optimal paths are evaluated by VISIR ("dis-coVerIng Safe and effIcient Routes"), a graph-search ship routing model, with respect to the solution of the fundamental differential equations governing optimal paths in a dynamic wind-wave environment. The evaluation exercise makes use of identical setups: topological constraints, dynamic wave environmental conditions, and vessel-ocean parametrizations, while advection by external currents is not considered. The emphasis is on predicting the time-optimal ship headings and Speeds Through Water constrained by dynamic ocean wave fields. VISIR upgrades regarding angular resolution, time-interpolation, and static navigational safety constraints are introduced. The deviations of the graph-search results relative to the solution of the exact differential equations in both the path duration and length are assessed. They are found to be of the order of the discretization errors, with VISIR's solution converging to that of the differential equation for sufficient resolution.

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Section: Discussionmentioning

confidence: 99%

Graph-Search and Differential Equations for Time-Optimal Vessel Route Planning in Dynamic Ocean Waves

Mannarini

Subramani

Lermusiaux

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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“…An agent travels from the (2, 2) to the blue cell (24,8). During walking, gap (10,13) is temporarily occupied by other agents. According to the path planning principle , the agent falls into the local minimum point of the artificial potential field.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…In Figure 8(a), the agent falls into the local minimum cell (10,13) at the 75 th time step. After delaying 4 time steps, the agent will jump out at the 83rd time step and delay passing through the cell (11,13), where the obstacle is located, and reach the destination at the 158 th time step.…”

Section: Case Studymentioning

confidence: 99%

“…e robot moves in the gradient direction, in which the potential energy drops fastest to avoid obstacles. Deepak Subramani and Lermusiaux combined the decision theory with the essential stochastic time-optimal path planning to establish the uncertain, strong, and dynamic risk-optimal path planning scheme based on partial differential equations [10]. Antonio Sedeño-noda and Colebrook extended the Dijkstra algorithm to the two-objective shortest path problem [11].…”

Section: Introductionmentioning

confidence: 99%

“…Ulises Orozco-Rosas et al constructed a membrane evolution potential energy field based on a multiprocessor, which improves the computational efficiency of robot path planning [14]. Deepak N. Subramani et al addressed the optimal path planning problem in a stochastic dynamic environment by combinatorial decision-making and time optimization theory [10], ant colony algorithm [15], simulated annealing method [16], firefly algorithm [17], and Q-learning [18]. e studies mentioned above mainly focus on the optimal path planning in various dynamic scenes under certain perceptual conditions from the view of system control, and they are not suitable for pedestrian simulation with physiological, psychological, and social uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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A Generalized Dynamic Potential Energy Model for Multiagent Path Planning

Liu

Guo

et al. 2020

Journal of Advanced Transportation

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Path planning for the multiagent, which is generally based on the artificial potential energy field, reflects the decision-making process of pedestrian walking and has great importance on the field multiagent system. In this paper, after setting the spatial-temporal simulation environment with large cells and small time segments based on the disaggregation decision theory of the multiagent, we establish a generalized dynamic potential energy model (DPEM) for the multiagent through four steps: (1) construct the space energy field with the improved Dijkstra algorithm, and obtain the fitting functions to reflect the relationship between speed decline rate and space occupancy of the agent through empirical cross experiments. (2) Construct the delay potential energy field based on the judgement and psychological changes of the multiagent in the situations where the other pedestrians have occupied the bottleneck cell. (3) Construct the waiting potential energy field based on the characteristics of the multiagent, such as dissipation and enhancement. (4) Obtain the generalized dynamic potential energy field by superposing the space potential energy field, delay potential energy field, and waiting potential energy field all together. Moreover, a case study is conducted to verify the feasibility and effectiveness of the dynamic potential energy model. The results also indicate that each agent’s path planning decision such as forward, waiting, and detour in the multiagent system is related to their individual characters and environmental factors. Overall, this study could help improve the efficiency of pedestrian traffic, optimize the walking space, and improve the performance of pedestrians in the multiagent system.

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