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The problem of estimating the minimum forces extracted by robot fingers on the surface of a grasped rigid object is very crucial to guarantee the stability of the grip without causing defect or damage to the grasped object. Solving this problem is investigated in this paper. Moreover, the optimum sets of parameters used to tune the algorithm are also studied here.

Ant Colony Optimization (ACO), which is a swarm intelligence‐based method, is used in this work to solve this problem. The problem under scope is a complex, constraint optimization problem. We develop our own approach to calculate those minimum forces. Ants ability to reorganize and behave collectively is modelled here. The required forces are a result of the final ants distribution around the fingers contact points. Ants move from contact point to another following the maximum pheromone level direction until they settle on a solution that accomplishes the given criteria. Ants number on a contact point constitutes the total force exerted by a finger on that contact point. The process is repeated until optimum solution is found. Simulations are repeated to track down most suitable ACO parameters for this type of problems and with different fingers configurations.

The results show that ACO can find optimum fingers forces for grasping rigid objects. These objects could be any polygon with or without friction between the fingers tips and the object surface. The method is computationally acceptable and can be applied with different fingers configurations and with different friction coefficients. We found that the optimal set of parameters used to tune ACO is independent of the initial number of ants on each location.

In this paper we present a very original, new, and interesting technique used to solve the optimum grasping forces of rigid objects. It is a well‐known fact that standard optimization techniques have their own requirements and limitations. This technique is based on swarm intelligence. This work opens the door for further investigations on how nature based methods can be used to solve complex problems. ACO offers a simple, yet structure approach to solve nonlinear constraint optimization problems.

Robot tools, or in more general terms, end‐of‐arm tools, or robot end‐effectors are general purpose, programmable or task‐oriented devices connected between the robot wrist and the object or load to be manipulated and/or processed by the robot. They can offer and/or limit the versatility of grasping and/or processing of different components, sensing their characteristics and working together with the robot control system to provide a reliable “service” throughout the component manipulation cycle. Reconfigurable robot tooling enables the robot to rapidly change its end‐effectors or fingers of its end‐effectors, typically under programmable software control. The importance of providing lean‐flexibility by means of reconfigurable, automated robot hand changers (ARHC), particularly in small‐batch robotic welding, assembly, machine loading and in other flexible robot cells, is discussed with examples. Some known systems are demonstrated and the “Ranky‐type” ARHC design is illustrated in more detail.