2 research outputs found
Learning to Herd Agents Amongst Obstacles: Training Robust Shepherding Behaviors using Deep Reinforcement Learning
Robotic shepherding problem considers the control and navigation of a group
of coherent agents (e.g., a flock of bird or a fleet of drones) through the
motion of an external robot, called shepherd. Machine learning based methods
have successfully solved this problem in an empty environment with no
obstacles. Rule-based methods, on the other hand, can handle more complex
scenarios in which environments are cluttered with obstacles and allow multiple
shepherds to work collaboratively. However, these rule-based methods are
fragile due to the difficulty in defining a comprehensive set of rules that can
handle all possible cases. To overcome these limitations, we propose the first
known learning-based method that can herd agents amongst obstacles. By using
deep reinforcement learning techniques combined with the probabilistic
roadmaps, we train a shepherding model using noisy but controlled environmental
and behavioral parameters. Our experimental results show that the proposed
method is robust, namely, it is insensitive to the uncertainties originated
from both environmental and behavioral models. Consequently, the proposed
method has a higher success rate, shorter completion time and path length than
the rule-based behavioral methods have. These advantages are particularly
prominent in more challenging scenarios involving more difficult groups and
strenuous passages
A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach
The simultaneous control of multiple coordinated robotic agents represents an
elaborate problem. If solved, however, the interaction between the agents can
lead to solutions to sophisticated problems. The concept of swarming, inspired
by nature, can be described as the emergence of complex system-level behaviors
from the interactions of relatively elementary agents. Due to the effectiveness
of solutions found in nature, bio-inspired swarming-based control techniques
are receiving a lot of attention in robotics. One method, known as swarm
shepherding, is founded on the sheep herding behavior exhibited by sheepdogs,
where a swarm of relatively simple agents are governed by a shepherd (or
shepherds) which is responsible for high-level guidance and planning. Many
studies have been conducted on shepherding as a control technique, ranging from
the replication of sheep herding via simulation, to the control of uninhabited
vehicles and robots for a variety of applications. We present a comprehensive
review of the literature on swarm shepherding to reveal the advantages and
potential of the approach to be applied to a plethora of robotic systems in the
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