Co-Evolutionary Algorithm for Motion Planning of Two Industrial Robots with Overlapping Workspaces

Ćurković, Petar; Jerbić, Bojan; Stipančić, Tomislav

doi:10.5772/54991

Cited by 7 publications

(4 citation statements)

References 20 publications

(22 reference statements)

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“…While the Model is developed, an environment is simulated locally using a dedicated host PC Server. COMOR is also in a phase of implementation on real robots applied on industrial assembly assignments within a real environment in the Lab [3,[34][35][36].…”

Section: Application Implementationmentioning

confidence: 99%

A context-aware approach in realization of socially intelligent industrial robots

Stipančić¹,

Jerbić²,

Ćurković³

2016

Robotics and Computer-Integrated Manufacturing

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Section: Application Implementationmentioning

confidence: 99%

A context-aware approach in realization of socially intelligent industrial robots

Stipančić¹,

Jerbić²,

Ćurković³

2016

Robotics and Computer-Integrated Manufacturing

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“…A chromosome consists of n lists representing robot poses. [7] introduce a Pareto-based co-evolutionary algorithm that provides the path for two 6-DOF FANUC robots. However, planning is not focused on a cooperation between the robots but rather to coordinate the two robots in such a way that they do not collide.…”

Section: Related Workmentioning

confidence: 99%

Path planning of cooperating industrial robots using evolutionary algorithms

Larsen

Kim

2021

Robotics and Computer-Integrated Manufacturing

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Under the concept of "Industry 4.0", production processes will be pushed to be increasingly interconnected, information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization goes beyond the traditional aim of capacity maximization, contributing also for organization's profitability and value. Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of maximization. The study of capacity optimization and costing models is an important research topic that deserves contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical model for capacity management based on different costing models (ABC and TDABC). A generic model has been developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization's value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity optimization might hide operational inefficiency.

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“…The last parameter was share radius σ share , which defines how many niches can be maintained. Common values are in the range of 5 to 10 [16], in this algorithm value of 10 was taken to promote more niches and consequently larger diversity.…”

Section: Fitness Sharingmentioning

confidence: 99%

“…A more realistic scenario is using a 3D model of the robot, with movement in an environment containing obstacles of different shapes and positions. In this case, a simple solution of going-through nodes does not suffice, because the body of the robot plays a critical role in checking for collision-free motion [15,16]. In this paper, an evolutionary algorithm-based path planner is proposed to solve the problem of path planning both for a simplified model of a mobile robot and a realistic 3 DOF stationary robot.The former will be presented as a point in space, while the later will be actual RRR model including links and presence of obstacles.…”

mentioning

confidence: 99%

Diversity Maintenance for Efficient Robot Path Planning

Ćurković¹,

Cehulic²

2020

Applied Sciences

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Path planning is present in many areas, such as robotics, video games, and unmanned autonomous vehicles. In the case of robots, it is a primary low-level prerequisite for the successful execution of high-level tasks. It is a known and difficult problem to solve, especially in terms of finding optimal paths for robots working in complex environments. Recently, population-based methods for multi-objective optimization, i.e., swarm and evolutionary algorithms successfully perform on different path planning problems. Knowing the nature of the problem is hard for optimization algorithms, it is expected that population-based algorithms might benefit from some kind of diversity maintenance implementation. However, advantages and potential traps of implementing specific diversity maintenance methods into the evolutionary path planner have not been clearly spelled out and experimentally demonstrated. In this paper, we fill this gap and compare three diversity maintenance methods and their impact on the evolutionary planner for problems of different complexity. Crowding, fitness sharing, and novelty search are tailored to fit specific problems, implemented, and tested for two scenarios: mobile robot operating in a 2D maze, and 3 degrees of freedom (DOF) robot operating in a 3D environment including obstacles. Results indicate that the novelty search outperforms the other two methods for problem domains of higher complexity.Path planning can generally be classified based on the nature of the environment as static or dynamic. In the case of the static environment, the layout, once perceived by the robot remains unchanged. Dynamic environments include movable objects i.e., other robots.According to the algorithm used for planning, the planner can either be local-meaning sensor-based and reactive, or global-meaning map-based and planning the trajectory from the initial to the final position at once. Based on the completeness, planning can be classified either as complete or heuristic. In the case of complete planning, the algorithm will always find a path in finite time if the path exists, or report that no feasible path exists in finite time. These algorithms are preferred for the systems with a low number of degrees of freedom over the heuristics algorithms [2][3][4][5][6].In the case of a 3D problem considered in this paper, both the trajectory and the robot's body have to avoid obstacles present in the environment and thus checked for collisions. This makes planning significantly more complex when compared to simple point-wise planning. It is known that for similar problems, population-based heuristic planning methods, i.e., swarm and evolutionary approaches are successfully utilized to find high-quality solutions in acceptable time [7][8][9][10]. It is also proven that the path planning problem is NP-complete, with the complexity of tasks increasing exponentially with the DOF number [11]. This is an additional motivation of exploring alternative methods to path planning, seeking for high quality (instead of optimal) solut...

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Co-Evolutionary Algorithm for Motion Planning of Two Industrial Robots with Overlapping Workspaces

Cited by 7 publications

References 20 publications

A context-aware approach in realization of socially intelligent industrial robots

A context-aware approach in realization of socially intelligent industrial robots

Path planning of cooperating industrial robots using evolutionary algorithms

Diversity Maintenance for Efficient Robot Path Planning

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