Swarm Robotics

Full Title or Meme

Swarm robotics is a field of robotics that studies the behavior of large groups of relatively simple robots that work together to accomplish tasks.

Context

The applications of swarm robotics are diverse and include tasks that demand miniaturization, such as distributed sensing tasks in micromachinery or the human body, and search and rescue missions. Swarm robotics can also be used to tackle dangerous tasks to reduce or eliminate the risk for humans, such as cleanup of toxic spills and demining. Other potential applications of swarm robotics include object clustering, assembling and construction, collective search and exploration, coordinated motion, collective transportation, self-deployment, and foraging.

Computing

Swarm robotics is a field of robotics that studies the behavior of large groups of relatively simple robots that work together to accomplish a task. Swarm robots can be used for computation in various ways. For example, they can be used to solve problems that are too complex for a single robot to handle, such as path planning, object recognition, and environmental monitoring. Swarm robots can also be used to perform distributed computing tasks, such as data processing, data storage, and data transmission. In addition, swarm robots can be used to simulate complex systems, such as biological systems, social systems, and ecological systems. Swarm robotics is an exciting and rapidly growing field that has the potential to revolutionize the way we think about computation and robotics.

Swarm robotics can be used for computation in a variety of applications, such as agriculture, environmental monitoring, search and rescue, and military operations. For example, swarm robots can be used to monitor crop growth and detect pests and diseases in agricultural fields. They can also be used to monitor air and water quality in the environment, and to search for and rescue people in disaster zones. In military operations, swarm robots can be used for reconnaissance, surveillance, and target acquisition.

Decision Making

These behaviors allow the robots in a swarm to take a common choice on a given issue.

• Consensus allows the individual robots in the swarm to agree on or converge toward a single common choice from several alternatives.
• Task allocation assigns arising tasks dynamically to the individual robots of the swarm. Its goal is to maximize performance of the entire swarm system. If the robots have heterogeneous capabilities, the tasks can be distributed accordingly to further increase the system's performance.
• Collective fault detection within the swarm of robots determines deficiencies of individual robots. It allows to determine robots that deviate from the desired behavior of the swarm, e.g., due to hardware failures.
• Collective perception combines the data locally sensed by the robots in the swarm into a big picture. It allows the swarm to make collective decisions in an informed way, e.g., to classify objects reliably, allocate an appropriate fraction of robots to a specific task, or to determine the optimal solution to a global problem. See section 2.2 for more details.
• Synchronization aligns frequency and phase of oscillators of the robots in the swarm. Thereby, the robots have a common understanding of time which allows them to perform actions synchronously. See section 2.2 for more details.
• Group size regulation allows the robots in the swarm to form groups of desired size. If the size of the swarm exceeds the desired group size, it splits into multiple groups.

Solutions

• "Smarticle" Robot Swarms Turn Random Behavior into Collective Intelligence: This article describes how simple robots called “smarticles” can work together as a group by flapping their arms and responding to light and sound signals. The researchers use algorithms to control the swarm behavior and mimic natural phenomena like ant colonies or DNA molecules.
• Categories, Quantum Computing, and Swarm Robotics: A Case Study: This paper presents a theoretical framework and a quantitative example of how to model and program swarm robotics using category theory and quantum computing. The authors use a matrix representation to relate local and global behaviors in a swarm, and simulate a toy model with a 4-qubit system. (2022-01-25)
  • Motivation: The paper aims to build a general model for swarm robotics, inspired by the complex behaviors of natural swarms of animals. The authors want to explore the potentialities of quantum computing in this field, as it offers computational efficiency and parallelism.
  • Methodology: The paper uses category theory to sketch a diagrammatic classification of swarms, relating ideal swarms to existing implementations. The paper uses Matrix Calculation to describe the local and global behaviors of a swarm, with diagonal sub-matrices for individual features and off-diagonal sub-matrices for pairwise interactions. The paper then shapes the structure of the interaction term using quantum computing, and simulates a toy model with a 4-qubit system.
  • Results: The paper shows that the matrix representation can capture the essential features of a swarm, such as self-organization, communication, and coordination. The paper also shows that the quantum computing simulation can reproduce the expected behaviors of the toy model, such as alignment, cohesion, and separation.
  • Implications: The paper suggests that the proposed framework and example can shed light on the potentialities of quantum computing in the realm of swarm robotics, and open new avenues for further research and development. The paper also discusses some limitations and challenges, such as scalability, noise, and decoherence.
• Trajectory Planning in Robot Joint Space Based on Improved Quantum Particle Swarm Optimization Algorithm Trajectory planning is a crucial step in controlling robot motion. The efficiency and accuracy of trajectory planning directly impact the real-time control and accuracy of robot motion. The robot’s trajectory is mapped to the joint space, and a mathematical model of trajectory planning is established to meet physical constraints during motion and avoid joint coupling problems. To enhance convergence speed and avoid local optima, an improved quantum particle swarm optimization algorithm is proposed and applied to solve the mathematical model of robot trajectory planning. The trajectory planning in robot joint space is then tested based on the improved quantum particle swarm optimization algorithm. The results demonstrate that this method can replace the traditional trajectory planning algorithms and offers higher accuracy and efficiency.
• Modeling and designing a robotic swarm: A quantum computing approach (2023-06) from query Use robot swarm to emulate quantum computer

• Birds flock. Locusts swarm. Fish school. Within assemblies of organisms that seem as though they could get chaotic, order somehow emerges. The collective behaviors of animals differ in their details from one species to another, but they largely adhere to principles of collective motion that physicists have worked out over centuries. Now, using technologies that only recently became available, researchers have been able to study these patterns of behavior more closely than ever before.

References

Other Material

• See wiki page on Self-organization
• See wiki page on Emergent Behavior