Tool paths of a complex contour machining generated by commercial CAD/CAM systems are mainly composed of many short linear/circular blocks. Though the look-ahead algorithms can improve speed and accuracy in the machining of short linear/circular segments, most of them just deal with linear segments with trapezoid acceleration and deceleration (acc/dec). In addition, the look-ahead algorithms with S-curve acc/dec are too complex to adopt the equivalent S-curve profile by approximation algorithm. To increase the smoothness of feedrate profile and machining efficiency of continuous short line and circle machining, this paper presents a feedrate profile generation approach and corresponding look-ahead algorithm with whole S-curve acc/dec. With the proposed look-ahead scheme, the feedrate profiles with S-curve acc/dec can work efficiently in a lot of short line and circle segments. Thus, the machining productivity can be increased and the feedrate profiles are smooth. The simulation and experiments verify the feasibility and validity of the proposed approach.

This paper proposes a smooth and accurate trajectory planning for industrial robots using geodesics. The workspace of a robot is split into positional and orientational parts. A Riemannian metric is given on each space such that the desired motion is a geodesic for the given metric. By regarding joint variables as local coordinates of the position space and the orientation space, Cartesian trajectories are represented by joint trajectories. A smooth and accurate motion of the robot end-effector and smooth joint trajectories corresponding to the motion can be obtained by calculating geodesics on the position space and the orientation space. To demonstrate the effectiveness of the proposed method, simulation experiments are conducted using the PUMA 560 robot.

Butterfly optimization algorithm (BOA) is a new swarm intelligence algorithm mimicking the behaviors of butterflies. However, there is still much room for improvement. In order to enhance the convergence speed and accuracy of the BOA, we present an improved algorithm SCLBOA based on SIBOA, which incorporates a logical mapping and a Lévy flight mechanism. The logical chaotic map is used for population initialization, and then the Lévy flight mechanism is integrated into the SCLBOA algorithm. To evaluate the performance of the SCLBOA, we conducted many experiments on standard test functions. The simulation results suggest that the SCLBOA is capable of high-precision optimization, fast convergence, and effective global optimization, all of which show that our method outperforms other methods in solving mathematical optimization problems. Finally, the BP network is optimized according to the SCLBOA (SCLBOA-BP) to further verify the availability of the algorithm. Simulation experiments prove the practicability of this method by building a Boston housing price prediction model for training.

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