# Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging Robots
## 3 Results
### Experiments with Actual Robots  
In order to verify the effectiveness of the self-organized task allocation method based on DRTM, the e-puck robot was used to perform the foraging task of swarm robots. The e-puck robots are open tools, which have been used extensively for swarm experiments. With a diameter of 6.8 cm and a height 5.3 cm, each e-puck robot is equipped with (i) eight infrared proximity sensors for detecting obstacles, (ii) one CMOS camera to look for objects, (iii) one three-dimensional accelerometer, (iv) three microphones, and (v) one loudspeaker. Each e-puck robot is equipped with a 3D-printed component, as shown in Figure 7a, which can realize the passive grasping of food and transport the food back to the nest. The food to be searched for by the robot is cylindrical, with a diameter of 3.5 cm and a height of 4.2 cm. As shown in Figure 7b, the upper half of the food is red and the lower half is white. There is a color camera on the front of the robot with a resolution of 640 \(\times\) 480. When the robot searches in the environment, it judges whether it has found food by observing the color of the object. The environment was designed as a rectangular area of 1.2 m \(\times\) 1.5 m, and each robot moved at a speed of \(v\) = 0.05 m/s. Figure 6: Number of obstacle avoidance events (OAEs) (\(N_{f}\) = 20, \(E_{d}\) = 10, \(N_{r}\) = 20, \(L\) = 4), in DRTM: \(M_{T}\) = 925.96, \(M_{v}\) = 46.30, in ARTM: \(M_{T}\) = 1148.44, \(M_{v}\) = 57.42. Figure 7: E-puck robot for foraging tasks. (**a**) e-puck with a 3D-printed component (**b**) e-puck and the food. In this experiment, seven e-puck robots were used to complete swarm foraging, as shown in Figure 8. In order to reduce the error and enable the robot to return to the nest effectively, a static robot is placed in the center of the nest as a beacon, and the robot can continuously send out signals to assist the foraging robot to return to the nest. The beacon robot is in the center of the nest, and 6 foraging robots are evenly distributed along the perimeter of the nest. In order to verify the effectiveness of the task allocation method and study the factors affecting the task allocation, swarm-robotics foraging experiments were carried out in two different situations. In the first scenario, the initial food quantity in the nest is set to 10, and the expected food quantity is 10. At the beginning of the experiment, the stimulus value \(S(t)=0\), and the robot is in a waiting state in the nest. When part of the food in the nest is consumed by the swarm, the stimulus value \(S(t)\) increases, and the robot decides whether to start foraging by calculating the foraging probability. At this time, the TFD and the average number of obstacle avoidance events are both 0. From Equation (7), it can be seen that the foraging probability of the robot is mainly affected by the stimulus value \(S(t)\). Therefore, as time increases, the robot's foraging probability gradually increases. After the foraging robot successfully forages and returns to the nest, if the amount of food in the nest is greater than the expected amount, the robot stops foraging. During the experiment, the difference between the amount of food in the nest and the expected amount of food is small, so the swarm-robotics system allocates fewer robots for food. According to the experimental observation, when the amount of food in the nest is less than the expected value, the swarm-robotics system always assigns one-three robots to look for food. The entire experiment ran for 10 min, and the swarm robot achieved autonomous task allocation and maintained the amount of food in the nest at the desired level. In the second scenario, the initial food quantity in the nest is 0, and the expected food quantity is 10. At the beginning of the experiment, the stimulus value \(S(t)=10\), the TFD and the average number of obstacle avoidance are both 0, that is, the threshold \(\theta=0\), and the robot has a higher probability of foraging from Equation (7). The system will allocate more robots to start foraging.