applied sciences Article  
Information Rich Voxel Grid for Use in Heterogeneous Multi-Agent Robotics [PERSON] *, [PERSON], [PERSON] + and [PERSON] *(c)  
School of Computer Science, Faculty of Science and Engineering, University of Hull, Hull HU6 7 RX, UK Correspondence: [EMAIL_ADDRESS] (S.B. ); [EMAIL_ADDRESS] (J.I.) Footnote 28: [[https://www.mdpi.com/](https://www.mdpi.com/)]([https://www.mdpi.com/](https://www.mdpi.com/))A similar narrative can be found in humanitarian efforts. Imagine a dynamic, ever-changing environment too dangerous for humans to navigate or assess. This scenario could arise from various natural disasters, such as an earthquake resulting in a collapsed building. In such situations, a robotic agent could be employed to map the area, identify objects or crucial points within, monitor changes over time, and, ultimately, interact with the environment to offer assistance. However, can we realistically expect a single agent to be specifically designed for such a rare occurrence? Moreover, even if multiple agents are deployed, the environment may not remain static or offer easily identifiable reference points. Additionally, in this critical scenario, should we assume the presence of a pre-established centralised coordinator or server infrastructure? It becomes evident that these impractical expectations in a complex environment are neither feasible nor realistic. Thus, the challenge lies in advancing collaborative robotics to capitalise on the significant progress already achieved in the field. In this paper, an approach is presented that utilises a voxel representation of the environment, and multiple agents utilising that voxel grid in order to better estimate the agent's pose and understand the environment. The main contribution of this paper is the design of a voxel grid that incorporates environmental information, making it suitable for multi-agent systems. The key aspects of the design are as follows:  
1. _Voxel grid that shows persistent probability over time_--this is a 3D representation of an environment where each voxel corresponds to a small volume in space. The grid is used to model the occupancy probability of each voxel over time, indicating the likelihood of a particular voxel being occupied by an object or obstacle. The persistent probability over time refers to the ability of the grid to maintain the occupancy probabilities of voxels over a period, even as new observations or data are added. This means that the grid can be updated with new information, allowing it to adapt to changes in the environment. 2. _Voxel grid that preserves information over time and contains detailed information_--this is a grid that preserves detailed information about the environment and tracks changes in that environment over time. The voxel grid can store information such as occupancy probabilities, surface normals, color, and texture, allowing for a more accurate representation of the environment. Additionally, the voxel grid can be updated with new information over time, allowing it to adapt to changes in the environment. 3. _A voxel grid that can be integrated into a multiagent swarm_--this is a 3D representation of the environment that can be used by multiple agents in a coordinated manner. The ability to integrate a voxel grid into a multiagent swarm is useful in a wide range of applications, such as in robotics, where multiple agents may be required to work together to accomplish a task. 4. _A voxel grid that can accommodate multiple sensor types_--this is a 3D representation of the environment that can be constructed by integrating data from multiple sensors. The key benefit of this approach is that by utilising a voxel grid, the information can be represented in a uniform manner regardless of the sensor type. In this paper, a solution to these issues is proposed through a framework to utilise a decentralised robotic colony for environment mapping.