This decade has witnessed a paradigm shift in human-labor based rescue-surveillance operations. Research propositions have explored the possibility of replacing manual intervention for the management of catastrophic situations by a team of robots. However, to implement the concept into practice, the robotics community has faced several challenges. The multi-robotic system has to be duly coordinated efficaciously by controllers to automate the operations thereby saving the lives of the rescuers. Subsequently, the controller/s should be able to ensemble the robots forming a particular shape depending on the varying environmental conditions. Moreover, it would allow the group to switch its current formation so that the system could maneuver towards the target while avoiding static/dynamic obstacles. To address these challenges, we have proposed a hierarchical control strategy so that the robots could maintain a strong inter-agent cohesiveness and simultaneously could switch their formation in the face of the changing situations and could pursue the goal of arriving towards the target. The formation control law has been designed based on the echolocation principle of the bio-inspired bat algorithm. The algorithm, corroborated by the simulation results and the real-time experiments is exceptionally useful for forming the desired pattern, changing the formation adaptively whenever obstructions show up in their trajectories.

在过去的十年中，基于人工的救援监视操作发生了范式转变。研究命题探讨了用机器人团队取代人工干预来管理灾难性情况的可能性。但是，为了将这一概念付诸实践，机器人技术界面临着一些挑战。多机器人系统必须由控制器适当有效地协调，以使操作自动化，从而挽救救援人员的生命。随后，一个或多个控制器应该能够根据变化的环境条件使机器人形成特定的形状。此外，这将允许小组切换其当前的编队，以便系统可以在避免静态/动态障碍的同时向目标机动。为了应对这些挑战，我们提出了一种分层控制策略，以便机器人可以保持强大的智能体之间的内聚力，同时可以在面对变化的情况时切换它们的形式，并可以朝着目标迈进。根据生物启发蝙蝠算法的回波定位原理设计了地层控制律。仿真结果和实时实验证实的算法对于形成所需图案，每当障碍物在其轨迹中出现障碍物时自适应地改变形状都非常有用。根据生物启发蝙蝠算法的回波定位原理设计了地层控制律。仿真结果和实时实验证实的算法对于形成所需图案，每当障碍物在其轨迹中出现障碍物时自适应地改变形状都非常有用。根据生物启发蝙蝠算法的回波定位原理设计了地层控制律。仿真结果和实时实验证实的算法对于形成所需图案，每当障碍物在其轨迹中出现障碍物时自适应地改变形状都非常有用。