Communication-aware distributed PSO for dynamic robotic search

Abstract

The use of swarm robotics in search tasks is an active area of research. A variety of algorithms have been developed that effectively direct robots toward a desired target by leveraging their collaborative sensing capabilities. Unfortunately, these algorithms often neglect the task of communicating possible task solutions outside of the swarm. Many scenarios require a monitoring station that must receive updates from robots within the swarm. This task is trivial in constrained locations, but becomes difficult as the search area increases and communication between nodes is not always possible. A second shortcoming of existing algorithms is the inability to find and track mobile targets. We propose an extension to the distributed Particle Swarm Optimization algorithm that is both communicationaware and capable of tracking mobile targets within a search space. Simulated experiments show that our algorithm returns more accurate solutions to a monitoring station than existing algorithms, especially in scenarios, where the target value or location changes over time.

Method for the Optimal Sensor Deployment of WSNs in 3D Terrain Based on the DPSOVF Algorithm

“…An extension of the DPSO algorithm considering a CL was studied in [36]. All particles transmitted their newly found global best position to a server one after another as time went on.…”

“…Figs. 8, 9 and 10 show the comparison of coverage ratio when using the original PSO algorithm, DPSO algorithm [34], CPSO algorithm [36], PSOVF algorithm, IDPSOVF algorithm [6] and proposed DPSOVF algorithm in different environmental cases. Fig.…”

A Novel Approach for Swarm Robotic Target Searches Based on the DPSO Algorithm

“…In practice, the communication limit of the robots is a problem that cannot be ignored. To overcome this problem, a communication aware DPSO (CDPSO) algorithm was presented in [41]. Although the CDPSO algorithm had some effect on alleviating the communication limit, its disadvantage was that the communication in a CDPSO could not always be achieved.…”

“…Considering that energy is vital for the robots to continue to work, the energy consumption of the robots should be minimized. In [41], all of the robots needed to communicate with a server, which led to high CEC. In [9], substantial amounts of communication between the robots were needed, which would consume too much energy.…”

“…The CDPSO algorithm took the communication limit of the robots into consideration and was proposed to control the robots in searching for the targets [41]. A communication term was added to the velocity update equation of each particle in the DPSO algorithm:…”