Suspended loads

This paper presents a novel Extended Hybrid Reciprocal Velocity Obstacle (EHRVO) algorithm for multi-agent collision avoidance that incorporates linked agent constraints. The proposed approach introduces proximity constraints between paired gents while adapting the collision avoidance geometry to maintain these relationships. This research work extends the HRVO framework to create a hierarchical set of constraints that prioritize maintenance of spatial consistent agent pairs or triads while ensuring collision-free trajectories. .

This study introduces a novel trajectory planning approach for the transportation of cable-suspended loads employing three quadrotors, relying on a reinforcement learning (RL) algorithm. The primary objective of this path planning method is to transport the cargo smoothly while avoiding its swing.

his study of the state of the art examines the impact of AI on improving drone autonomous behavior, covering from automation to complex real-time decision making.

This paper presents a NOx Sensor machine learning model for a highly efficient 2MW power generation lean burn gas engine with an active pre-chamber.

In this article, we propose a reciprocal collision avoidance system for autonomous drones, based on computer vision and using relative positioning in an indoor environment.

In this paper, we focus on the current state of the art of aerial transportation systems with suspended loads by a single UAV and a team of them and present a review of different dynamic cable models and control systems.

The paper’s main contribution is the formulation of the hybrid selective model of the DLO sections as either catenaries or parabolas, which allows us to overcome these sagging conditions. We provide the specific decision rule to shift between DLO models. Simulation results demonstrate the performance of the proposed approach under stringent conditions.

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