Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

The small battery capacities of the mobile robot and the un-optimized planning efficiency of
the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in …

In this paper, a deep reinforcement learning-based path planning method for kiwifruit picking
robot coverage is proposed. Compared with existing approaches, the novelty of this paper is …

Planning the energy-efficient and collision-free paths for reconfigurable robots in complex
environments is more challenging than conventional fixed-shaped robots due to their …

The prevalence of maritime transportation and operations is increasing, leading to a gradual
increase in drowning accidents at sea. In the context of maritime search and rescue (SAR), it …

The use of unmanned aerial vehicles (UAVs) to carry out remote aerial surveys has become
prominent in recent years. The UAV-based survey faces several operational issues, such as …

One of the essential attributes of a cleaning robot is to achieve complete area coverage.
Current commercial indoor cleaning robots have fixed morphology and are restricted to …

The traveling salesman problem (TSP) is one of the best-known combinatorial optimization
problems. Many methods derived from TSP have been applied to study autonomous vehicle …

In mobile robotics research, the exploration of unknown environments has always been an
important topic due to its practical uses in consumer and military applications. One specific …

In recent years, more attention has been paid to water surface environment protection.
Current water surface waste cleaning mainly relies on manual operations, which are low …