A genetic algorithm for task completion time minimization for multi-robot sensor-based coverage

Abstract

Minimizing the coverage task time is important for many sensor-based coverage applications. The completion time of a sensor-based coverage task is determined by the maximum time traveled by a robot in a mobile robot group. So the environment needs to be partitioned among robots considering their travel times. Most of the coverage algorithms results in sharp turns which require the robot to slow down, turn and accelerate. So the actual travel time of a mobile robot is depending on the traveled distance and number of turns both. In this study, previously proposed hierarchical oriented genetic algorithm (HOGA) is extended to consider the travel time rather than just the traveled distances. The HOGA consists of two phases. In the first phase, a previously proposed oriented genetic algorithm is used to find a single route with minimum repeated coverage. Then, in the second phase, a directed genetic algorithm is used to partition the route among robots considering actual travel time costs. The algorithms are coded in C++ and simulations are conducted using P3-DX mobile robots in the MobileSim environment.

A pattern-based genetic algorithm for multi-robot coverage path planning minimizing completion time

“…In this subsection, the results of the proposed method are compared with the results of the hierarchical two-stage genetic algorithm presented in Ozkan et al (2009b). For the laboratory environment given in Fig.…”

“…The completion times are 159 and 137 s with about 14% improvement. As mentioned before the method in Ozkan et al (2009b) consists of two stages; (i) find a single time-based route with minimum repeated coverage, (ii) partition the route among robots considering travel time costs. Since the HOGA partitions the initial route, the resulting partitioning is not changed so much depending on initial conditions (compare Figs.…”

“…For a time-efficient coverage, turns should be taken into consideration. In Ozkan et al (2009b), a hierarchical oriented genetic algorithm (HOGA) that firstly proposed in Ozkan et al (2009a) is extended to consider the travel time rather than just the traveled distances. The HOGA consists of two stages.…”

Coefficient of agility and sampling frequency issues in Mobile Agents Collision Detection with Dynamic Obstacles in 3D Space

“…Relevant to this research is robots collaboration [21] and multi-robots networks [22] [23] [24]. There is proposed generic algorithm [25] and reference generation and control schemes [26]. Term behavior is used [27] and VSTR [28] algorithm for collision avoiding is one of the solutions.…”