How can LiDAR data be communicated between robots in a swarm?

Swarm robots utilise LiDAR data for spatial awareness, enabling them to navigate complex environments by detecting obstacles and other robots. This is crucial for coordinated movement and task execution, like in precision agriculture, where robots work together to monitor and treat crops, ensuring they avoid collisions and optimise coverage, thereby enhancing efficiency and productivity. LiDAR’s precision in mapping surroundings makes it indispensable for these autonomous systems.

LiDar is a sensing device. Communication is usually Duplex or Bi directional way as you need handshake to ensure no error in data communication. I cant see how LiDar can do such communication

LiDAR data, in and of itself, is woefully inadequate to achieve FOV sufficiency. Unless FOV sufficiency is achieved, any swarm will be unsafe and/or erratic in any dynamic environment. This question reveals that the questioner does not completely understand the physics and mathematics of what is required to achieve FOV sufficiency. The question exploits the common reality that most AI software devs, especially in all levels of mobile autonomy, are simply ignorant of the need for FOV sufficiency and human quick WCET. Not theory, Fact.

Swarm robots will not beaing able to become swarm robot if they didn't attached and get LIDAR data , there for swarm robot must equipped with LIDAR that predict and calculate real time motions according to the data gets from LIDAR , actually that is the real coordinate world observed t9 the swarm robot and one of the basics of teaching the robot our world also can be called the eyes of the robot but its allot more since it can handle wide range of detections such as obstacles and locations both time using an algorithm that so called AI the swarm robot can be functional in our world

LiDAR data communication among swarm robots poses challenges such as bandwidth limitations, which can hinder real-time data sharing, and signal interference, particularly in environments with multiple robots operating closely. Additionally, maintaining data integrity and synchronisation across the swarm is complex, as is ensuring robustness against environmental factors like dust or moisture that can degrade signal quality. For instance, in search and rescue operations within dense forests, these challenges can impact the swarm’s efficiency in mapping and navigating the terrain, potentially delaying critical missions.

Solutions include deploying mesh networks to enhance connectivity and resilience, and using data compression techniques to manage bandwidth more efficiently. Implementing robust error-correction algorithms ensures data integrity, even in adverse conditions. For instance, in a precision farming scenario, these approaches can ensure that each robot receives accurate data about the location of crops and obstacles, facilitating effective and synchronised task execution across the swarm, thus optimising agricultural processes.

Centralized Communication: to have a central controller that collects LiDAR data from each robot and makes decisions based on the aggregated information. This central entity can then distribute commands or information to individual robots. it simplifies communication but may introduce a single point of failure. Decentralized Communication: Peer-to-Peer Communication: robots communicate directly with each other. They share LiDAR data and collaborate to make local decisions. This reduces dependence on a central controller and increases the system's robustness. Mesh Networking: Implementing a mesh network allows robots to communicate with nearby units, forming a net of nodes. LiDAR data r shared among neighboring units (+collaboration)

Data Compression and Representation: Feature Extraction: Rather than transmitting the entire raw point cloud, robots can extract and share relevant features or information from the LiDAR data. This reduces the amount of data that needs to be communicated while preserving essential details. Octree-based Compression: Representing the point cloud data using octrees can help in efficiently transmitting and reconstructing the 3D environment. Octrees allow for hierarchical organization of the data, reducing the overall amount of information exchanged.

I would like to add that in a swarm of robots, LiDAR data can be communicated through a networked system where each unit broadcasts its sensory readings to others in real-time or near-real-time. This involves using standardized communication protocols to ensure interoperability and efficient data exchange. Data compression and selective sharing techniques are often applied to minimize bandwidth usage and latency, allowing only relevant LiDAR data to be transmitted. This approach enables the swarm to collectively process and utilize the information for coordinated navigation, obstacle avoidance, and task execution, enhancing the overall efficiency and effectiveness of the group's operations.