Multiple robots are involved in Swarm Robotics and are working together to solve problems by forming advantageous structures and similar behaviors observed in natural systems, such as swarms of bees, birds, or fish.
The AI integration into swarm robotics, which has the potential to greatly enhance the capabilities of these robotic swarms, enables them to adapt to new situations, learn from their experiences, and make intelligent decisions based on the available information.
AI algorithms are designed to take advantage of the collective behavior of robots, allowing them to complete tasks more efficiently and effectively. The use of robot swarms is mainly for coordinating groups in missions in dynamic or unknown environments, such as space exploration, search, and retrieval in disaster situations, or agricultural applications.
The key impacts of AI on Swarm Robotics are:
- Swarm Coordination: It helps in determining the desired collective behavior, such as formation maintenance, exploration, task allocation, or object manipulation. AI techniques like reinforcement learning, evolutionary algorithms, and decentralized control algorithms enable efficient coordination among robots, ensuring the swarm acts as a unified entity.
- Distributed Decision-Making: Swarm robots are enabled by AI to make intelligent decisions in a distributed manner. Each robot collects sensory information, which is locally processed, and makes decisions based on its individual goals and the collective objectives of the swarm. It allows swarm robots to support distributed decision-making to adapt to dynamic environments, respond to changes, and collectively optimise performance.
- Scalability: AI algorithms help address scalability challenges in swarm robotics. With the increase in the number of robots in a swarm, it becomes more challenging to manage the interactions and coordination. AI-based techniques provide scalable solutions for allowing large numbers of robots to operate effectively and efficiently.
- Swarm intelligence: AI has facilitated the emergence of swarm intelligence in robotic systems. By leveraging machine learning and optimisation algorithms, swarm robots collectively learn and adapt their behaviours based on experience, environmental feedback, or task requirements. The collective learning process enables the swarm’s emergent behaviours of exhibition, self-organisation, and problem-solving capabilities to surpass the individual robot’s abilities.
- Robustness and Fault Tolerance: AI supports improving the robustness and fault tolerance of swarm robotics systems. With AI-based techniques, swarm robots can detect and respond to failures or environmental changes in real time. It can autonomously reconfigure its actions, task redistributions, or strategies to ensure the completion of the collective objective, even in the presence of individual robot failures or communication issues.
Specific examples of AI being used in swarm robotics are:
- AI is used in intelligent transportation systems to develop swarms of self-driving cars that communicate and coordinate with each other to improve traffic flow.
- AI is used in Search and rescue operations to develop swarms of robots for survivors in disaster areas.
- AI is used to develop swarms of robots to help with tasks such as crop monitoring and pest control.
- Military applications: AI is used to develop swarms of drones that can be used for surveillance, reconnaissance, and even combat.
AI applications in swarm robotics are vast, and AI continues to develop with more innovative and groundbreaking technology applications.