Abstract
Recent industrial developments in autonomous systems, or agents, which assume that humans and the agents share the same space or even work in close proximity, open for new challenges in robotics, especially in motion planning and control. In these settings, the control system should be able to provide these agents a reliable path following control when they are working in a group or in collaboration with one or several humans in complex and dynamic environments. In such scenarios, these agents are not only moving to reach their goals, i.e., locations, they are also aware of the movements of other entities to find a collision-free path. Thus, this paper proposes a dependable, i.e., safe, reliable and effective, path planning algorithm for a group of agents that share their working space with humans. Firstly, the method employs the Theta* algorithm to initialize the paths from a starting point to a goal for a set of agents. As Theta* algorithm is computationally heavy, it only reruns when there is a significant change of the environment. To deal with the movements of the agents, a static flow field along the configured path is defined. This field is used by the agents to navigate and reach their goals even if the planned trajectories are changed. Secondly, a dipole field is calculated to avoid the collision of agents with other agents and human subjects. In this approach, each agent is assumed to be a source of a magnetic dipole field in which the magnetic moment is aligned with the moving direction of the agent. The magnetic dipole-dipole interactions between these agents generate repulsive forces to help them to avoid collision. The effectiveness of the proposed approach has been evaluated with extensive simulations. The results show that the static flow field is able to drive agents to the goals with a small number of requirements to update the path of agents. Meanwhile, the dipole flow field plays an important role to prevent collisions. The combination of these two fields results in a safe path planning algorithm, with a deterministic outcome, to navigate agents to their desired goals.
Highlights
Until recently, robots have played a critical role in the manufacturing industry where the automatic robots perform repetitive and sometimes heavy tasks
The aim of the static flow field is to convert the path found by Theta* into a navigation field to avoid the needs of running Theta* for every update of the agent position, and to allow a more robust integration of the path planning with obstacle avoidance and velocity controls
This paper has introduced a novel path planning algorithm for agents surrounded by static and multiple moving objects, including other robotic agents as well as humans subjects, all populating a realistic working space
Summary
Robots have played a critical role in the manufacturing industry where the automatic robots perform repetitive and sometimes heavy tasks. The algorithm helps agents avoid collisions with humans and others in the shared environment, in which a group of agents are designed to collaborate with each in order to plan their optimal paths, in real-time. A family of path planning algorithms address the problem of avoiding moving obstacles with field-based approaches. In order to address the above mentioned issues, in this paper, a novel method for path planning of mobile agents, in the shared working environment of human and agents, called as the dipole flow field, is proposed. To the best of our knowledge, most conventional approaches attempt to generate the pushing forces based only on the location of the agents, whereas in this work, it is assumed that, those should be better aligned with both moving directions and velocity magnitudes of different agents.
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