Swarm robots assisted by target position estimatio

Swarm robot search assisted by target position estimation under absolute positioning mechanism

Abstract: in the process of target oriented search and swarm robot coordinated control using swarm intelligence method, the robot's search behavior is jointly guided by its own cognition of the environment and the experience shared within the group, and the group experience plays a great role. Because the group experience belongs to the cognition of an individual in essence, in order to improve the speed of target search, the group decision of estimating the position of the group robot about the target is introduced under the absolute positioning mechanism. Firstly, the extended particle swarm optimization modeling method is used to model the swarm robot system. Then, based on the essence of the guiding effect of group experience on search behavior and the essence of using RSSI method to estimate the target position, the experience essentially belonging to a single robot is combined with collective decision-making for target search control. When the conditions of target location estimation are satisfied, the target location estimation is introduced into the extended particle swarm optimization model; If the location estimation conditions are not met, the original model will be retained. The results of simulation experiments show that compared with the model method based on group experience, this algorithm is superior to the former in terms of target search success rate, search efficiency and energy consumption

key words: swarm robots; Particle swarm optimization algorithm; Target search; Position estimation

1 introduction

swarm robots are special multi autonomous robot systems [1], which are composed of several relatively simple robots. Individuals only have limited environmental perception ability, and their structure and functional roles are isomorphic. Swarm robot system has the following characteristics [2]: robustness; flexibility; Scalability of system scale. Swarm robot research is divided into several benchmark problems [3], such as handling, formation, search, rounding up, etc. The search task includes several essential scientific problems [4], such as positioning, communication, collision avoidance, path planning, etc., and positioning is the basis for robots to identify their position in the environment and work together with other robots. Swarm robot positioning [5] refers to the process that autonomous mobile robots collect the measurement data of odometer and sensor in real time during the movement, and speculate their own position and posture and their time-varying characteristics. On this basis, they can also speculate the running speed and estimate the target position and posture. Swarm robot positioning is mainly divided into two types [6]: absolute positioning and relative positioning. The absolute positioning of swarm robots [7] is to set a reference point in or outside the working environment as the reference datum of all robots, and then each robot calculates the position and posture through its own sensor, and then modifies the calculated position through the reference point to eliminate the accumulated error. The relative positioning mechanism of swarm robots [8] is that each single robot takes its own position as the reference point, takes its own head orientation as the positive direction of the coordinate system to build its own local coordinate system material performance, and takes its own relative position detection of other robots as the posture of other robots. Absolute positioning methods mainly include navigation beacon positioning, active or passive identification positioning, graphic matching positioning, GPS positioning, probability positioning, etc; Relative positioning mainly includes inertial navigation, range measurement, etc. [9]. This paper studies the target search problem of swarm robots under absolute localization mechanism. The existing swarm robot target search research adopts the extended particle swarm optimization algorithm model for system modeling and control. The extended particle swarm optimization model iterates the desired position and speed based on its own experience and group experience. Self experience is the introduction of short-term memory mechanism, which is determined by the target signal strength of the current position and the previous position. Group experience is the target signal strength value monitored by the robot and its time-varying feature group to judge its own group experience. It can be seen that group experience is based on the cognitive "election" of all robots in the time-varying feature group. Whether it is the robot's individual perception or group experience, in essence, what guides the robot's search behavior belongs to the cognition of a single robot. The wireless sensor network is composed of decentralized nodes, each of which has the ability of sensing, computing and communication, and its function is similar to that of swarm robots. There are mainly two kinds of node location technologies in wireless sensor networks, ranging free and ranging required. RSSI algorithm in ranging required location method is a relatively mature method. The essence of swarm robots is wireless sensor networks, and individual robots are nodes of sensor networks with motion attributes. The essence of target location based on wireless sensor networks is the result of group decision-making. Considering the problem of search efficiency, this paper combines target position estimation with swarm robot search. When the target position estimation condition of RSSI method is satisfied, the target position is estimated through the detection of the target signal by the robot sensor, and the model is modified with the target position estimation value, so as to assist the swarm robots in target search

this paper is arranged as follows: Section 2 describes the method of system modeling and coordinated control using extended particle swarm optimization algorithm for swarm robot target search, and analyzes the essence of guiding individual robot loading speed and force time curve search behavior based on this method; In Section 3, the RSSI method is introduced, and the essence of its positioning is analyzed in combination with the autonomous mobile robot; Section 4 describes the swarm robot search method for target position estimation, and gives its algorithm; In Section 5, simulation experiments are carried out and the results are discussed. For comparison, the target search experiment of extended particle swarm optimization model method without considering the estimated value of target position is carried out at the same time. Section 6 draws relevant conclusions and concludes with future research prospects

2 modeling and coordination control of swarm robots for target search

the target search task of swarm robots occupies a special important position in the application of swarm robots. Compared with other tasks, target search is the basis. The extended particle swarm optimization model is used as the control tool in the coordinated control of swarm robot target search task. The extended particle swarm optimization model [10] is that the robot is randomly placed in a circle with a radius less than R at the beginning of the search target task, and the center of the circle is far from the target position, so as to improve the search difficulty. The initial speed and position of the robot are random values, and the initial speed is a random value between [0,1] and the maximum speed. The robot monitors the target signal for the first time, and the target signal intensity value [11] is Gaussian white noise with normal distribution from

. The robot broadcasts the measured signal strength value and its position coordinates, and monitors the signal sent by the robot in its time-varying feature group. The robot takes the initial position as its optimal position, compares the target signal strength value between the robot and the neighbor robot in the group, and takes the maximum position of the robot as its optimal position in the group. According to the monitored signal, if no robot in the group detects the target signal, the spiral divergent roaming state is carried out, and the iterative formula is

if at least one robot in the group detects the target intensity signal, the intelligent search state is carried out, and the iterative formula is Formula 1. The robot calculates its desired position and speed according to the initial position, its own optimal position and the group optimal position. At this point, the robot moves one step. The robot monitors the target signal again and calculates the target signal strength value according to equation 2. The robot broadcasts the measured signal strength value and updated position coordinates, and monitors the signals sent by the robots in its time-varying feature group. The robot calculates its optimal position through [12]

, then compares its target signal strength value with the neighbor robots in the group, and takes the robot position with the maximum value as its optimal position in the group. According to the monitored signal, if no robot in the Group monitors the target signal, it will carry out spiral divergent roaming state, and its iterative formula is formula 3. If at least one robot in the Group monitors the target intensity signal, it will carry out intelligent search. Its iterative formula is Formula 1. The robot calculates its desired position and speed according to the current position, its own optimal position and the group optimal position. At this point, the robot moves another step. Repeat until at least one robot in the group is less than a set value from the target, or exceeds the maximum number of iteration steps

self experience is to introduce a short-term memory mechanism, which is determined by the target signal strength of the current position and the previous position. Group experience is to judge its own group experience by the target signal strength value monitored by the robot and its time-varying characteristic robots in the group. It can be seen that group experience is played by one of the cognition of all robots in the time-varying feature group. Whether it is the robot's individual perception or group experience, in essence, what guides the robot's search behavior belongs to the cognition of a single robot, without collective decision-making. The target position estimation is equivalent to collective decision-making, because this method uses the experience of at least three robots involved in the estimation

3 target search with target position estimation

wireless sensor network is composed of decentralized nodes, each node has the ability of sensing, computing and communication, and its function is similar to that of swarm robots. There are mainly two kinds of node location technologies in wireless sensor networks, ranging free and ranging required. RSSI algorithm in ranging required location method is a relatively mature method. According to the similarity between wireless sensor network node location technology and swarm robot absolute location technology, RSSI algorithm is introduced into it. When the robot is searching for a target, when the signal strength of the target detected by at least three robots including itself in the group is not 0, and the three robots are not on the same straight line, the RSSI condition for estimating the target position is met

4 algorithm description

the simulation algorithm process of introducing the new model of target position estimation is as follows:

5 simulation results

for the extended model and the model in this paper, 50 simulations are carried out with 3 to 10 robots respectively, a total of 8000 times. The simulation data are analyzed and compared. The main performance evaluation indexes of the extended particle swarm optimization model and the model in this paper are: the number of robots in this model ranges from 3 to 10, and the success rate of searching targets is compared; In this paper, the number of model robots ranges from 3 to 10, and the expected number of search steps is compared; This paper compares the success rate of target search between the number of robots in this model and the extended particle swarm optimization model from 3 to 10; In this paper, the number of robots searching for the target from 3 to 10 is expected to be compared between the model and the extended particle swarm optimization model; In this paper, the expected step length of robot searching target from 3 to 10 is compared with the extended particle swarm optimization model; When the model satisfies the RSSI method to estimate the target position, the estimated value of the target position of the individual robot is compared with the expectation of the distance from its group optimal position to the target

5.2 simulation data

for the convenience of this model and extended particle swarm model, the style is regular in the data graph and data table. Hereinafter, this model is abbreviated as method 1 and extended particle swarm model is abbreviated as method 2

it can be seen from the table and figure that the success rate of method 1 increases with the increase of the number of robots, indicating that the efficiency of group search increases with the expansion of the scale of the system. When the number of robots in the model is the same, method 1 is also better than method 2 on the whole, which shows that the search efficiency of method 1 is higher than that of method 2 in the same case

it can be seen from the table and figure that the number of search steps of method 1 is gradually decreasing with the increase of the number of robots, indicating that the number of steps required for group search is decreasing, and its search efficiency is increasing. Model