QRDPSO: A New Optimization Method for Swarm Robot Searching and Obstacle Avoidance in Dynamic Environments

Reducing Interrupts Among Robots in Quantum-Behaved Swarm Exploration With MR-Leach

“…The multi-hop routes are established and used between the nodes to maintain the network's full connection [13]. This way, the communication link's quality can be measured, helpful in restricting the robot's movement.…”

“…This section presents the robot team development for unstructured scenarios, describing one robot's communication and deployment to another robot within a multi-hop network path. The complete conversion of the QRDPSO algorithm from its predecessor, the Robotics Darwinian PSO (RDPSO) [1], can be found in [13]. The development of the QRDPSO revolves around parameters 𝑖𝑠 𝑖𝑗 (𝑡) and 𝑖𝑚 𝑖𝑗 (𝑡) into the term (𝑋 𝑖,𝑛 𝑗 − 𝐶 𝑛 𝑗 ); thus, we obtain the QRDPSO as follows:…”

“…Each component's parameters are random vectors, which are mentioned as zeros and ones. 𝑋 𝑖,𝑛 𝑗 represents the best position for the local, obstacle, and MR-Leach components [13].…”

“…The connectivity component 𝑋 2 is represented by the position where there is connectivity with its neighbour, connectivity function 𝑖𝑚 𝑖 (𝑡). The obstacle component 𝑋 3 which is represented by each robot's position is optimized monotonically by increasing or by decreasing obstacle sensing function (𝑠(𝑥 𝑖 (𝑡))) [13].…”

“…Long transmission distance and multiple relays contribute to heavy communication traffic and do not encourage efficiency. The communication schema must follow a missionrelated design, i.e., based on the behaviour that one should expect from the swarm robotics, to enhance the communication of the Quantum Robotic Darwinian Particle Swarm Optimization (QRDPSO) [13].…”