This paper presents a framework for telepresence robot navigation in dynamic environments with network-induced time delays. The proposed system introduces a predictive control model that processes sensor data, implements real-time control algorithms, and transmits commands to enable robust remote navigation. To address visual and control discrepancies caused by latency, a state estimation model is employed to minimise the visual disparity between the robot’s actual and perceived positions. Additionally, a simulation-based predictive controller anticipates operator commands to improve teleoperation accuracy. A key contribution of this work is the development of a low-cost, simulation-based telepresence platform that enables controlled experiments without relying on expensive physical infrastructure. The system is designed for flexibility, allowing parameter adjustments to suit a range of experimental conditions. By integrating predictive technologies and addressing latency-related challenges, this research advances the state-of-the-art in telepresence robotics and provides a practical, reproducible foundation for future studies in remote robot navigation.

This study tackles the problem of many-objective sequence optimization for semi-automated robotic disassembly operations. To this end, we employ a many-objective genetic algorithm (MaOGA) inspired by the non-dominated sorting genetic algorithm (NSGA)-III, along with robotic-disassembly-oriented constraints and objective functions derived from geometrical and robot simulations using three-dimensional (3D) geometrical information stored in a 3D computer-aided design (CAD) model of the target product. The MaOGA begins by generating a set of initial chromosomes based on a contact and connection graph (CCG), rather than random chromosomes, to avoid falling into a local minimum and yield repeatable convergence. The optimization imposes constraints on feasibility and stability as well as objective functions regarding difficulty, efficiency, prioritization, and allocability to generate a sequence that satisfies many preferred conditions under mandatory requirements for semi-automated robotic disassembly. The NSGA-III-inspired MaOGA also utilizes non-dominated sorting and niching with reference lines to further encourage steady and stable exploration and uniformly lower the overall evaluation values. Our sequence generation experiments for a complex product (36 parts) demonstrated that the proposed method can consistently produce feasible and stable sequences with a 100% success rate, bringing the multiple preferred conditions closer to the optimal solution required for semi-automated robotic disassembly operations.