Path planning is one of the key steps in the application of industrial robotic manipulators. The process of determining trajectories can be time-intensive and mathematically complex, which raises the complexity and error proneness of this task. For these reasons, the authors tested the application of a genetic algorithm (GA) on the problem of continuous path planning based on the Ho–Cook method. The generation of trajectories was optimized with regard to the distance between individual segments. A boundary condition was set regarding the minimal values that the trajectory parameters can be set in order to avoid stationary solutions. Any distances between segments introduced by this condition were addressed with Bezier spline interpolation applied between evolved segments. The developed algorithm was shown to generate trajectories and can easily be applied for the further path planning of various robotic manipulators, which indicates great promise for the use of such algorithms.

View Video Presentation: https://doi.org/10.2514/6.2023-1655.vid In the multiple fields covered by Artificial Intelligence (AI), robotic path planning is undoubtedly one of the issues that cover a wide range of research lines. This paper introduces recently developed Aquila Optimization algorithm specifically configured for Multi-Robot space exploration. The framework is a unique combination of both deterministic Coordinated Multi-robot Exploration (CME) and a swarm based Aquila Optimizer (AO), combinely known as Coordinated Multi-robot Exploration Aquila Optimizer (CME-AO). The proposed hybrid strategy also incorporates a novel parallel communication protocol, to improve multi-robot space exploration process while simultaneously minimizing both the computation complexity and time. This ensures acquisition of a optimal collision-free path in a barrier-filled environment via generating a finite map. The architecture starts by determining the cost and utility values of neighbouring cells around the robot using deterministic CME. Aquila Optimization technique is then incorporated to increase the overall solution accuracy. Algorithm validity and effectiveness was then validated utilizing different condition environment whose relative complexity was varied by varying parameters such as exploration space dimension and obstacle size, number and relative orientation. A perspective analysis is then performed to compare the performance of the proposed CME-AO algorithm with latest contemporary algorithms such as conventional CME and CME-WO (CME augmented Whale Optimizer). Results indicate efficacy of the proposed algorithm as it presents two distinct advantages a) enhanced map exploration in cluttered environment and b) significantly reduced computation complexity and execution time. This makes the suggested methodology particularly suitable for on-board utilization in an obstacle-cluttered environment, where other contemporary CME based techniques either fails (stuck locally) or takes longer exploration time.

The development of AI and robotics has led to an explosion of research and the number of implementations in automated systems. However, whilst commonplace in manufacturing, these approaches have not impacted chemistry due to difficulty in developing robot systems that are dexterous enough for experimental operation. In this paper, a control system for desktop experimental manipulators based on an audio-visual information fusion algorithm was designed. The robot could replace the operator to complete some tedious and dangerous experimental work by teaching it the arm movement skills. The system is divided into two parts: skill acquisition and movement control. For the former, the visual signal was obtained through two algorithms of motion detection, which were realized by an improved two-stream convolutional network; the audio signal was extracted by Voice AI with regular expressions. Then, we combined the audio and visual information to obtain high coincidence motor skills. The accuracy of skill acquisition can reach more than 81%. The latter employed motor control and grasping pose recognition, which achieved precise controlling and grasping. The system can be used for the teaching and control work of chemical experiments with specific processes. It can replace the operator to complete the chemical experiment work while greatly reducing the programming threshold and improving the efficiency.

The recently emerging multi-portfolio selection problem lacks a proper framework to ensure that client privacy and database secrecy remain intact. Since privacy is of major concern these days, in this paper, we propose a variant of Beetle Antennae Search (BAS) known as Distributed Beetle Antennae Search (DBAS) to optimize multi-portfolio selection problems without violating the privacy of individual portfolios. DBAS is a swarm-based optimization algorithm that solely shares the gradients of portfolios among the swarm without sharing private data or portfolio stock information. DBAS is a hybrid framework, and it inherits the swarm-like nature of the Particle Swarm Optimization (PSO) algorithm with the BAS updating criteria. It ensures a robust and fast optimization of the multi-portfolio selection problem whilst keeping the privacy and secrecy of each portfolio intact. Since multi-portfolio selection problems are a recent direction for the field, no work has been done concerning the privacy of the database nor the privacy of stock information of individual portfolios. To test the robustness of DBAS, simulations were conducted consisting of four categories of multi-portfolio problems, where in each category, three portfolios were selected. To achieve this, 200 days worth of real-world stock data were utilized from 25 NASDAQ stock companies. The simulation results prove that DBAS not only ensures portfolio privacy but is also efficient and robust in selecting optimal portfolios.