## 4 Experiments
### First Experiment  
The first simulation environment was considered trivial because after the initial phase, only about 30% of the unknown environment remained, as illustrated in Figure 4. On the left, a simulation environment is displayed, where red zones represent overlaid places that the camera cannot capture at the initial phase. On the right is the state of the environment after the initial phase, which contains about 30% of the unknown environment. The cubes of an octree (with a color scale by height) depict a combination of unknown and occupied space. The trajectory planning algorithm considers this octal tree as a collision object. Bygradually scanning the environment, the octal tree is reduced and at last, only occupied and unreachable cells should remain. The elements of the fitness function are decomposed so that the influence of individual elements can be clearly seen. In three separate measurements, the maximum weight 1 for the chosen element was set, while the weights of the others were 0. The fourth measurement consisted of equally set weights. The experiment was thus be carried out in four measurements:  
* \(\quad\)IG1_T0_R0--Information gain weight 1, others 0;
* \(\quad\)IG0_T1_R0--Weight of the distance of the candidate from the current camera position 1, others 0;
* \(\quad\)IG0_T0_R1--Weight of the rotation difference between candidate and current camera position 1, others 0;
* \(\quad\)IG1_T1_R1--All weights equal to 1. The total decrease in the value of the fitness function with respect to the selected candidates is shown in the graph in Figure 5. The value of IG1_T0_R0 is significantly lower than in the case of other experiments. Since the workspace of the robot is limited (depending on the robot used) and the range of the scanner is up to 5 m, it is impossible to achieve maximum information gain. The highest information gain value is only around 0.3; however, the distance fitness function can have a significantly greater impact, as the worst distance can be twice the robot's, but usually, the positions of the candidates are significantly closer. Therefore, in the IG1_T1_R1 measurement, information gain was less preferred. It is advisable to compensate for this imbalance by unevenly distributing the weights. Figure 6 illustrates the NBV algorithm's progress across iterations. The results indicate that IG1_T0_R0 achieves the fastest convergence to the total scanned space. Figure 4: (**a**) The first experiment setup; (**b**) the occupied and unknown space after the initial phase. Figure 5: The fitness value with respect to the selected candidate. Although IG1_T0_R0 searched the space in fewer iterations, this does not guarantee that the total distance traveled is the shortest, which can significantly impact the total duration of exploration. The total length of the trajectory is evaluated using criteria listed below:  
* Joint Distance (JD): This metric calculates the sum of the absolute angular distance traveled by each of the robot's joints throughout the entire trajectory. A lower JD signifies less overall joint movement. * Control Pseudo Cost (CPC): This is a weighted version of JD, where the movement of larger joints (closer to the robot base) is penalized more heavily than the movement of smaller joints. It provides a more realistic estimate of energy consumption. * Cartesian Distance (CD): This metric measures the total distance traveled by the robot's end-effector in 3D space. * Orientation Change (OC): This calculates the sum of the absolute angular changes in the end-effector's orientation between consecutive points in the trajectory. It is a key indicator of rotational smoothness; a lower OC suggests fewer and less abrupt reorientations of the tool. * Robot Displacement Metric (RDM): The RDM is the sum of the Cartesian distances traveled by the center of mass of each of the robot's links. It provides the most comprehensive measure of the total motion of the entire robot structure, not just the end-effector.