A mixed integer programming (MIP) model for evaluating navigation and task planning of human–robot interactions (HRI)

Şenol, Mehmet Burak

doi:10.1007/s11370-019-00275-w

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…Mixed integer linear programming (MILP) is a highly complex combinatorial optimization problem that contains both variables and continuous variables [ 1 ], as well as an NP-complete problem [ 2 ]. Due to the multiple types of its decision variables, it has broad applications in solving managerial planning problems, such as supply chain management [ 3 ], robot control problems [ 4 ], and traffic flow problems [ 5 , 6 ]. A general MILP model is shown as follows:

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Section: Introductionmentioning

confidence: 99%

A Hybrid Algorithm of Ant Colony and Benders Decomposition for Large-Scale Mixed Integer Linear Programming

Shan

Qiu

2022

Computational Intelligence and Neuroscience

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The mixed integer linear programming (MILP) has been widely applied in many fields such as supply chain management and robot control, while how to develop a more efficient algorithm to solve large-scale MILP is still in discussion. This study addresses a hybrid algorithm of the ant colony and Benders decomposition to improve the efficiency. We firstly introduce the design of our algorithm, in which the Benders algorithm decomposes the MILP into a master problem and a slack problem, the ant colony algorithm generates initial solutions for the master problem, and heuristic rules obtain feasible solutions for the slack problem. Then, the computational experiments are carried out to verify efficiency, with a benchmark test and some medium-large scale examples. Compared with other algorithms like CPLEX, GUROBI, and traditional ACA, our algorithm shows a better performance with a 0.3%–4.0% optimality gap, as well as a significant decrease of 54.3% and 33.6% on average in the CPU time and iterations, respectively. Our contribution is to provide a low-workload, time-saving, and high-accuracy hybrid algorithm to solve MILP problems with a large amount of variables, which can be widely used in more commercial solvers and promote the utilization of the artificial intelligence.

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…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Algorithm of Ant Colony and Benders Decomposition for Large-Scale Mixed Integer Linear Programming

Shan

Qiu

2022

Computational Intelligence and Neuroscience

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“…In the same problem, the notion of preference was added by Kala [27] while solving by using decentralized evolutionary computation. Senol [28] accounted for the human-robot interactions while optimizing a mission using a mixed-integer programming technique, without Boolean and temporal constraints. The problem with all the current approaches is that the temporal operators are rarely modelled and accounted for, which restricts the generality of the solver and thus the solver can be used in a few limited scenarios only.…”

Section: Related Workmentioning

confidence: 99%

Multi-robot mission planning using evolutionary computation with incremental task addition

Kala

2021

Intel Serv Robotics

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Mission planning asks the robot to solve complex missions, requiring the robot to execute several actions such that a complex goal condition (called mission) is satisfied. The missions can be specified by using Linear Temporal Logic or any custom domain-specific language. Here, we consider service robots in the home or office environments with multiple users, each specifying a task that together makes a mission. Conventional mission planning techniques assume that the complete mission specification is known in advance, while mission planning is incremental wherein tasks keep getting added and deleted. The exponential computational complexity of the current model verification-based approaches is not a viable solution. The paper presents an evolutionary framework for incrementally solving the problem of mission planning for the problems for which a polynomial-time verification system exists for the tasks. The optimal solution is represented in an encoding-specific format that is compiled to form a linear trajectory of the robots. Optimization is done as the robot operates. Thus, the robot at any time has a partial solution to every task that has already been executed that (along with the robot assigned to the task) cannot be altered, while a part representing the next steps of the robot to be optimized. This acts as a constraint in the continuous optimization. The proposed approach uses Dynamic Programming to integrate the solutions of tasks and as a distance function in the diversity preserving evolutionary computation since the mission constantly changes. Comparisons are made with a baseline evolutionary computation without Dynamic Programming, a non-incremental version of the proposed algorithm, and several greedy strategies. The proposed algorithm is seen to perform better than all other methods. Further, the algorithm is implemented and demonstrated on a Pioneer LX robot.

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Mission planning on preference-based expression trees using heuristics-assisted evolutionary computation

Kala

2023

Applied Soft Computing

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A mixed integer programming (MIP) model for evaluating navigation and task planning of human–robot interactions (HRI)

Cited by 6 publications

References 30 publications

A Hybrid Algorithm of Ant Colony and Benders Decomposition for Large-Scale Mixed Integer Linear Programming

A Hybrid Algorithm of Ant Colony and Benders Decomposition for Large-Scale Mixed Integer Linear Programming

Multi-robot mission planning using evolutionary computation with incremental task addition

Mission planning on preference-based expression trees using heuristics-assisted evolutionary computation

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