The paper deals with the generation of optimal trajectories for industrial robots in machining and additive manufacturing applications. The proposed method uses an Ant Colony algorithm to solve a kinodynamic motion planning problem. It exploits the kinematic redundancy that is often present in these applications to optimize the execution of trajectory. At the same time, the robot kinematics and dynamics constraints are respected and robot collisions are avoided. To reduce the computational burden, the task workspace is discretized enabling the use of efficient network solver based on Ant Colony theory. The proposed method is validated in robotic milling and additive manufacturing real-world scenarios.

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Performing an open-loop movement, or docking, for an industrial mobile robot (IMR), is a common necessary procedure when relying on environmental sensors is not possible. This procedure precision and outcome, solely depend on the IMR forward kinematic and odometry correctness, which is tied to the kinematics parameters, depending on the IMR kind. Calibrating the kinematic parameters of an IMR is a time consuming and mandatory procedure, since the mechanical tolerances and the assembly procedure may introduce a large variation from the nominal parameters. Furthermore, calibration inaccuracies might introduce severe inconsistencies in tasks such as localization, mapping, and navigation in general. In this work, we focus on the so-called kinematic parameter calibration. We propose the use of the unscented Kalman filter to perform a calibration procedure of the geometrical kinematic parameters of a mobile platform. The mobile platform is externally tracked during the calibration phase, using a fixed temporary external sensor that retrieves the position of a visual tag fixed to the platform. The unscented Kalman filter, using the calibration phase collected data, estimates the enlarged system state, which is comprised of the parameters that have to be estimated, the platform odometry and the visual tag position. The method can either be used online, to identify parameters and monitor their value while the system is operating, or offline, on logged data. We validate this method on two different devices, a 4 mecanum-wheel IMR , and a Turtlebot 3, using a camera to track the movement trough a reference chessboard, for then comparing the original path to its corrected version. INDEX TERMSMobile robot calibration, Unscented Kalman filter I. INTRODUCTION Generally, industrial mechanical systems need to have a good parameter calibration to perform accordingly to the standard, hence, a calibration procedure is needed. This calibration procedure, or parameter estimation, is obtainable by means of general purpose algorithms, or specifically tailored methods for particular mechanical systems. Concerning IMRs, the value of the kinematic parameters incorporated in the model, hugely modify the performance of the system in all of its uses, from the mapping phase, where the IMR position infers the map conception, in navigation, where the planning algorithms use the IMR kinematics to devise a path and control the robot movements, and in localization, where the relative position and velocity are used to estimate the displacement progression in an environment. The overall impact of the IMR odometry correctness arises in specific cases such as docking, in which we cannot rely on localization algorithms (e.g. highly de-structured environments, dynamic environments or lacking of localization sensors), 19 and high precision relative displacements are performed in 20 open loop on the IMR kinematic model. Generally, the 21 constant parameters that are taken into account in an IMR 22 kinematic are the wheels radius, which is usually the same 2...

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