Mathematical programming for Multi-Vehicle Motion Planning problems

Abichandani, Pramod; Ford, Gabriel; Benson, Hande Y.; Kam, Moshe

doi:10.1109/icra.2012.6225141

Cited by 31 publications

(9 citation statements)

References 37 publications

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“…This usually is achieved using mixed integer linear programming (MILP) constraints to model obstacles as multiple convex polygons. 2 Currently, this is commonly used for MPC approaches.…”

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confidence: 99%

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

2014

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SUMMARYWe review a range of techniques related to navigation of unmanned vehicles through unknown environments with obstacles, especially those that rigorously ensure collision avoidance (given certain assumptions about the system). This topic continues to be an active area of research, and we highlight some directions in which available approaches may be improved. The paper discusses models of the sensors and vehicle kinematics, assumptions about the environment, and performance criteria. Methods applicable to stationary obstacles, moving obstacles and multiple vehicles scenarios are all reviewed. In preference to global approaches based on full knowledge of the environment, particular attention is given to reactive methods based on local sensory data, with a special focus on recently proposed navigation laws based on model predictive and sliding mode control.

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“…This usually is achieved using mixed integer linear programming (MILP) constraints to model obstacles as multiple convex polygons. 2 Currently, this is commonly used for MPC approaches.…”

mentioning

confidence: 99%

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

2014

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“…Fukushima et al (2013) reduced an optimal control problem to mixed-integer quadratic programming (MIQP) problem for multiple vehicles formation. For a review of mathematical programming techniques applied to motion planning problems see Abichandani et al (2012).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Trajectory planning for autonomous underwater vehicles in the presence of obstacles and a nonlinear flow field using mixed integer nonlinear programming

Wang

Lima

Giraldi

et al. 2019

Computers & Operations Research

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This paper addresses the time-optimal trajectory planning for autonomous underwater vehicles. A detailed mixed integer nonlinear programming (MINLP) model is presented, explicitly taking into account vehicle kinematic constraints, obstacle avoidance, and a nonlinear flow field to represent the ocean current. MINLP problems pose great challenges because of the combinatorial complexity and nonconvexities introduced by the nature of the flow field. A novel solution approach in an optimization framework is developed to address associated difficulties. The main benefit of the proposed methodology is the ability to find multiple local minima. The contribution of the paper is fourfold: 1) a novel approach to integrate the flow field into the MINLP model; 2) a diversified initialization strategy using multiple waypoints, different solvers and approximated models, namely, a mixed integer linear programming model and the MINLP model with and without the flow field; 3) an algorithm that forces the solver to seek improved solutions; and 4) a parallel computing approach capitalizing on diversified initialization. The performance of the resulting methodology is illustrated on idealized case studies, and the results are used to gain insight into trajectory planning in the presence of flow fields.

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“…It determines how safely the mobile robot reaches its goal position (GP) taken into account several criteria such as shortest distance and minimum energy. Several methodologies have been suggested to solve path planning problem, among them the classical approaches such as, mathematical programming, 1 cell decomposition, 2 roadmap approach, 3 and potential fields. 4 The most downsides of these techniques are their inefficiency due to the high computational cost and inaccuracy due to the trapping in the local minimum.…”

Section: Introductionmentioning

confidence: 99%

Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment

Ajeil

Ibraheem

Azar

et al. 2020

International Journal of Advanced Robotic Systems

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The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. Two modifications are suggested to improve the searching process of the standard bat algorithm with the result of two novel algorithms. The first algorithm is a Modified Frequency Bat algorithm, and the second is a hybridization between the Particle Swarm Optimization with the Modified Frequency Bat algorithm, namely, the Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithm. Both Modified Frequency Bat and Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithms have been integrated with a proposed technique for obstacle detection and avoidance and are applied to different static and dynamic environments using free-space modeling. Moreover, a new procedure is proposed to convert the infeasible solutions suggested via path the proposed swarm-inspired optimization-based path planning algorithm into feasible ones. The simulations are run in MATLAB environment to test the validation of the suggested algorithms. They have shown that the proposed path planning algorithms result in superior performance by finding the shortest and smoothest collision-free path under various static and dynamic scenarios.

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Mathematical programming for Multi-Vehicle Motion Planning problems

Cited by 31 publications

References 37 publications

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

Trajectory planning for autonomous underwater vehicles in the presence of obstacles and a nonlinear flow field using mixed integer nonlinear programming

Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment

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