This article focuses on the control of a group of nonholonomic mobile robots. A leader-follower coordinated control scheme is developed to achieve formation maneuvers of such a multi-robot system. The scheme adopts the methodology of integral sliding mode control to form up and maintain the robots in predefined trajectories. The dynamic equations of the scheme are subject to mismatched uncertainties. The mismatched uncertainties challenge formation stabilization because they cannot be suppressed by the invariance of integral sliding mode control. In light of Lyapunov’s direct method, a sufficient condition is drawn to guarantee the reachability condition of integral sliding mode control in the presence of the mismatched uncertainties. To verify the feasibility and effectiveness of the proposed strategy, simulation results are illustrated by an uncertain multi-robot system composed of three nonholonomic mobile robots.

Multi-robot exploration is a search of uncertainty in restricted space seeking to build a finite map by a group of robots. It has the main task to distribute the search assignments among robots in real time. In this paper, we proposed a stochastic optimization for multi-robot exploration that mimics the coordinated predatory behavior of grey wolves via simulation. Here, the robot movement is computed by the combined deterministic and metaheuristic techniques. It uses the Coordinated Multi-Robot Exploration and Grey Wolf Optimizer algorithms as a new method called the hybrid stochastic exploration. Initially, the deterministic cost and utility determine the precedence of adjacent cells around a robot. Then, the stochastic optimization improves the overall solution. It implies that the robots evaluate the environment by the deterministic approach and move on using the metaheuristic algorithm. The proposed hybrid method was implemented on simple and complex maps and compared with the Coordinated Multi-Robot Exploration algorithm. The simulation results show that the stochastic optimization enhances the deterministic approach to completely explore and map out the areas.

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In this paper, we used multi-objective optimization in the exploration of unknown space. Exploration is the process of generating models of environments from sensor data. The goal of the exploration is to create a finite map of indoor space. It is common practice in mobile robotics to consider the exploration as a single-objective problem, which is to maximize a search of uncertainty. In this study, we proposed a new methodology of exploration with two conflicting objectives: to search for a new place and to enhance map accuracy. The proposed multiple-objective exploration uses the Multi-Objective Grey Wolf Optimizer algorithm. It begins with the initialization of the grey wolf population, which are waypoints in our multi-robot exploration. Once the waypoint positions are set in the beginning, they stay unchanged through all iterations. The role of updating the position belongs to the robots, which select the non-dominated waypoints among them. The waypoint selection results from two objective functions. The performance of the multi-objective exploration is presented. The trade-off among objective functions is unveiled by the Pareto-optimal solutions. A comparison with other algorithms is implemented in the end.

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