This paper describes a new hybrid algorithm extracted from honey bee mating optimization (HBMO) algorithm (for robot travelling distance minimization) and tabu list technique (for obstacle avoidance) for team robot system. This algorithm was implemented in a C++ programming language on a Pentium computer and simulated on simple cylindrical robots in a simulation software. The environment in this simulation was dynamic with moving obstacles and goals. The results of simulation have shown validity and reliability of new algorithm. The outcomes of simulation have shown better performance than ACO and PSO algorithm (society, nature algorithms) with respect to two well-known metrics included, ATPD (average total path deviation) and AUTD (average uncovered target distance).
One of the most important issues in using robots is working automatically without human intervention. Autonomous robots are robots that self-control itself without human intervention [
Robot motion planning refers to process of robot task breakdown in the format of separated and discrete motions [
But the working capacity of a robot is limited and when we use it in a real world’s environment, we need to be using a group of them [
Furthermore, using robot in industrial tasks causes some overall costs such as energy cost [
Because of the following reasons, this paper tries to investigate a new hybrid algorithm for team-robot motion planning that offers a robot motion planning algorithm with an optimized robot path travelling distance and obstacle avoidance in dynamics and unknown environment with moving goals and obstacles according to real world conditions. For doing it, we use honey bee mating optimization algorithm for taking an optimized path according to robot path travelling distance and use tabu lists for obstacle avoidance. The validity and reliability of this algorithm show with regard to simulation results. All simulations has been done in Webots 7.0.1 simulation software and programming of this algorithm implemented on a C++ programming language on a Pentium PC. The simulation results compare to two other algorithms including ant colony optimization algorithm (ACO) and particle swarm (PSO) algorithm. The outcomes of simulation have shown better performance than ACO and PSO algorithm (society, nature algorithms) with respect to two well-known metrics included, ATPD (average total path deviation) and AUTD (average uncovered target distance).
In the following part, we distinguish the research methodology. Section
In the first step of managing this research, we describe the methodology of environmental modeling. Then we identify the problem definition and the offered algorithm structure. Ultimately, we describe the simulation methods and ways of making resolutions.
In this research, the environment is dynamic and unknown. Dynamic environments are the environments with moving obstacles and goals. For the modeling of dynamic in obstacles and goals, we must apply a physical modeling. The physical modeling is based on the dynamic equations in physics science. According to physics science in one step of time (from
The motion of obstacles and goals in dynamic environment.
According to physics science, a solid object makes a motion with a velocity vector from its position at time
For the modeling of obstacle and goals in the simulation software, we use a supervised plan for making motion in time according to random position of obstacle and goals and equations (
This dynamic in the environment is unknown for the robot. Robots did not have any previous information about robots, obstacles, and goals. Robots must get information from their sensors and cameras.
The simulation software is working with virtual reality modeling language. So, we must use a supervisor node for making these changes in the environment.
The conceptualization speculates the evaluation of the next position of the robot in its workspace thereby avoiding collision with other robots and the static hindrances in its runway from the current locality of the robot in the workspace. The following are the presuppositions made to validate the multirobot path planning problem: among a fixed set of actions in motion the robot has to select only one action at a time; the path planning problem, hence, incurs a bit of steps until all the robots arrive at their respective address.
The following principles are used to satisfy the assumptions: the robot first ascertains the next position in order to coordinate itself with the destination and constructs a path to that location.
Figure
The problem definition.
In Figure
According to (
The honey bee is a social insect that can survive only as a member of a community, or a colony. The colony of different drone’s sperm there is in her spermatheca; she can use parts of the honey bee community which consists of three structurally different forms: the queen (reproductive female), the drones (male), and the workers (no reproductive female). These castes are associated with different functions in the colony; each caste possesses its own special instincts geared to the needs of the colony. The HBMO algorithm combines a number of different steps and the main steps of HBMO are depicted in Figure
HBMO algorithm diagram.
By replacing the bees with points that are available around the robot, we can choose the best point with minimum traveling distance for each robot. But this point may be available in the obstacles and robots must choose another point. So, we use tabu list technique for avoiding obstacles. The diagram of this algorithm is shown in Figure
The first step in this algorithm is searching. Searching begins with camera searching in if the camera did not find an object near to robot for if camera found a goal near to robot in if camera found an obstacle near to robot in
After doing the search, the robot runs the HBMO algorithm and chooses a near to optimum path and moves to that point. The searching and moving algorithm diagram is shown in Figure
Searching and moving algorithm diagram.
In this algorithm, there are two tabu lists. One of these is global and the other is local. Each robot has a local tabu list and there is a global list for all algorithms. The local tabu list is for robot obstacle avoidance and the global tabu list is for avoiding robot loss. The main pseudocode is shown in Pseudocode
Initial position ( Initial position ( Initial position ( Real List_1
Repeat Search with Camera If Camera see No things begin End Else if see an obstacle begin Calculate Calculate List_1 End Else if see an goal Begin Calculate Calculate end
The algorithm was implemented in a C++ programming language and on a Pentium PC. All of the simulations are done in Webots 7.0.1. The results of simulations are evaluated according to two known metrics: average total path deviation (ATPD) and average uncovered target distance (AUTD). An example of simulation is shown in Figure
An example of simulation.
Let
Given a goal position
Figure
AUTD versus number of steps with velocity as variable for number of obstacles = 5 (constant).
AUTD versus number of steps with number of robots as variables for number of obstacles = 8 (constant).
Figure
The fall-off in AUTD over program steps for a given
We note from Figure
ATPD versus number of robots with number of obstacles as variables for velocity = 16 units (constant).
ATPD versus number of obstacles with number of robots as variables for velocity = 16 units (constant).
The fall-off in AUTD over time for a given
AUTD versus time with number of robots as variables for velocity = 16 units (constant).
AUTD versus time with number of robots as obstacles for velocity = 16 units (constant).
The simulation results of PSO and ACO algorithm show that the average performance of new algorithm is better than them (you can see the results in Table
Comparison between new algorithm versus ACO and PSO.
Problem | Number of Obs. | Number of Robots | ATPD | AUTD | ||||
---|---|---|---|---|---|---|---|---|
New algorithm | ACO | PSO | New algorithm | ACO | PSO | |||
1 | 10 | 5 | 23.87 | 24.29 | 24.44 | 38.56 | 39.06 | 47.26 |
2 | 20 | 5 | 26.42 | 26.89 | 27.05 | 45.5 | 46.09 | 55.77 |
3 | 30 | 5 | 28.54 | 29.05 | 29.22 | 53.69 | 54.39 | 65.81 |
4 | 40 | 5 | 29.3 | 29.82 | 30.0 | 63.69 | 64.18 | 77.66 |
5 | 50 | 5 | 29.86 | 30.39 | 30.57 | 74.76 | 75.73 | 91.63 |
6 | 10 | 10 | 30.05 | 30.59 | 30.77 | 88.22 | 89.36 | 108.13 |
7 | 20 | 10 | 31.23 | 31.79 | 31.98 | 104.1 | 105.45 | 127.59 |
8 | 30 | 10 | 31.56 | 32.12 | 32.32 | 122.83 | 124.43 | 150.56 |
9 | 40 | 10 | 32.12 | 32.69 | 32.89 | 144.94 | 146.83 | 177.66 |
10 | 50 | 10 | 32.86 | 33.45 | 33.65 | 171.04 | 173.26 | 209.64 |
11 | 10 | 15 | 33.87 | 34.48 | 34.69 | 201.82 | 204.45 | 247.38 |
12 | 20 | 15 | 52.57 | 53.51 | 53.84 | 238.15 | 241.25 | 291.91 |
13 | 30 | 15 | 73.7 | 75.02 | 75.47 | 281.02 | 284.67 | 344.46 |
14 | 40 | 15 | 97.57 | 99.33 | 99.99 | 331.61 | 335.92 | 406.42 |
15 | 50 | 15 | 124.55 | 126.79 | 127.56 | 391.3 | 396.38 | 472.62 |
16 | 10 | 20 | 155.54 | 157.83 | 158.78 | 461.73 | 467.73 | 565.96 |
17 | 20 | 20 | 189.49 | 192.9 | 194.06 | 554.84 | 551.92 | 667.83 |
18 | 30 | 20 | 228.42 | 232.53 | 223.93 | 642.91 | 651.27 | 788.54 |
19 | 40 | 20 | 272.41 | 277.31 | 278.98 | 758.64 | 768.5 | 929.89 |
20 | 50 | 20 | 322.12 | 327.91 | 329.88 | 895.19 | 906.83 | 1097.27 |
21 | 10 | 25 | 378.29 | 385.1 | 387.41 | 1056.33 | 1070.06 | 1294.78 |
22 | 20 | 25 | 441.76 | 449.73 | 452.41 | 1246.47 | 1262.68 | 1527.84 |
23 | 30 | 25 | 513.48 | 605.23 | 525.86 | 1470.84 | 1489.96 | 1802.85 |
24 | 40 | 25 | 594.53 | 615.44 | 608.86 | 1735.59 | 1758.15 | 2127.36 |
25 | 50 | 25 | 686.12 | 698.47 | 702.66 | 2048 | 2024.62 | 2510.29 |
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Average: | 178.4 | 185.3 | 182.3 | 528.46 | 535.33 | 647.55 |
The paper introduced a new technique for team robot motion planning in dynamics and unknown environment with moving goals and obstacles to select the shortest path length of all the robots without hitting any obstacles in the world map with a hybrid algorithm extracted from HBMO and Tabu lists algorithm. Experiments reveal that the proposed scheme outperforms the PSO and ACO robot motion planning scheme at least with respect to two well-known metrics: ATPD and AUTD. These results confirm the validity and reliability of algorithm.
The authors declare that they have no conflict of interests regarding the publication of this paper.