Sampling-based Minimum Risk path planning in multiobjective configuration spaces

Shan, Tixiao; Englot, Brendan

doi:10.1109/cdc.2015.7402330

Cited by 11 publications

(7 citation statements)

References 13 publications

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“…The scale factor parameter sf meas is intended to model this. The FPAA's complexity can be estimated as (16) where d is the solution length.…”

Section: A Complexity Analysismentioning

confidence: 99%

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Multi-Objective Path Planning for Autonomous Robots Using Reconfigurable Analog VLSI

Koziol

2020

IEEE Access

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This paper presents a Multi-Objective path planning approach using reconfigurable Analog-Very-Large-Scale-Integrated (AVLSI) circuits. It is significant because it is the first example of floating-gate based analog resistive grid circuits used for Multi-Objective path planning. The two path planning objectives are 1) minimizing path length and 2) minimizing path cost. Three hardware experimental results are presented that implement the approach using a Field Programmable Analog Array (FPAA) circuit. First, an example demonstrates a simple proof-of-concept. Second, an example shows how the FPAA solution compares to an entire solution set for a specific Start and Goal path planning problem. Third, an example shows how the FPAA solution compares to two edge-cases. The edge-cases are the two ideals: ideal lowest cost path, and ideal shortest distance path. Based on these foundational proof-of-concept hardware results, larger environment grids than are currently implementable on the FPAA hardware were simulated to predict performance if a custom FPAA application specific integrated circuit (ASIC) was built for this Multi-Objective path planning purpose. Finally, analysis is presented to address this method's computational complexity.

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“…The scale factor parameter sf meas is intended to model this. The FPAA's complexity can be estimated as (16) where d is the solution length.…”

Section: A Complexity Analysismentioning

confidence: 99%

“…Previous Multi-Objective path planning has been accomplished using techniques such as genetic algorithms [10], Pareto fronts [11], A* [12], Multi-Step A* [13], Multi-Objective D* lite [14], Rapidly Exploring Random Tree (RRT) based algorithms [15], [16], Neuromorphic systems [17], and Dijkstra's algorithm [11], [18].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Path Planning for Autonomous Robots Using Reconfigurable Analog VLSI

Koziol

2020

IEEE Access

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“…This is intended to capture problems in which robots must manage resources such as collision risk, access to valuable measurements or following certain rules, which are present in some regions of the environment, and absent in others. For example, [12] proposed a sampled-based planning algorithm for minimum risk planning. Risk is only penalized in the regions of the environment where collision is possible.…”

Section: Related Workmentioning

confidence: 99%

A Receding Horizon Multi-Objective Planner for Autonomous Surface Vehicles in Urban Waterways

Shan

Wang

Englot

et al. 2020

Preprint

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We propose a novel receding horizon planner for an autonomous surface vehicle (ASV) performing path planning in urban waterways. Feasible paths are found by repeatedly generating and searching a graph reflecting the obstacles observed in the sensor field-of-view. We also propose a novel method for multi-objective motion planning over the graph by leveraging the paradigm of lexicographic optimization and applying it to graph search within our receding horizon planner. The competing resources of interest are penalized hierarchically during the search. Higher-ranked resources cause a robot to incur non-negative costs over the paths traveled, which are occasionally zero-valued. The framework is intended to capture problems in which a robot must manage resources such as risk of collision. This leaves freedom for tie-breaking with respect to lower-priority resources; at the bottom of the hierarchy is a strictly positive quantity consumed by the robot, such as distance traveled, energy expended or time elapsed. We conduct experiments in both simulated and real-world environments to validate the proposed planner and demonstrate its capability for enabling ASV navigation in complex environments.

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“…Motion planners typically optimize solutions over path distance, time, and obstacle/terrain avoidance with benchmarks as discussed in [13]. Recent papers have presented flight risk metrics that augment traditional distance/time/obstacle avoidance cost terms [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Urban Metric Maps for Small Unmanned Aircraft Systems Motion Planning

Ochoa¹,

Atkins²

2021

Preprint

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Low-altitude urban flight planning for small Unmanned Aircraft Systems (UAS) requires accurate vehicle, environment maps, and risk models to assure flight plans consider the urban landscape as well as airspace constraints. This paper presents a suite of motion planning metrics designed for small UAS urban flight. We define map-based and path-based metrics to holistically characterize motion plan quality. Proposed metrics are examined in the context of representative geometric, graph-based, and sampling-based motion planners applied to a multicopter small UAS. A novel multi-objective heuristic is proposed and applied for graphbased and sampling motion planners at four urban UAS flight altitude layers. Monte Carlo case studies in a New York City urban environment illustrate metric map properties and planner performance. Motion plans are evaluated as a function of planning algorithm, location, range, and flight altitude.

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Sampling-based Minimum Risk path planning in multiobjective configuration spaces

Cited by 11 publications

References 13 publications

Multi-Objective Path Planning for Autonomous Robots Using Reconfigurable Analog VLSI

Multi-Objective Path Planning for Autonomous Robots Using Reconfigurable Analog VLSI

A Receding Horizon Multi-Objective Planner for Autonomous Surface Vehicles in Urban Waterways

Urban Metric Maps for Small Unmanned Aircraft Systems Motion Planning

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