Multi-robot task allocation (MRTA) problem has long been a key issue in multi-robot systems. Previous studies usually assumed that the robots must complete all tasks with minimum time cost. However, in many real situations, some tasks can be selectively performed by robots and will not limit the achievement of the goal. Instead, completing these tasks will cause some functional effects, such as decreasing the time cost of completing other tasks. This kind of task can be called “functional task”. This paper studies the multi-robot task allocation in the environment with functional tasks. In the problem, neither allocating all functional tasks nor allocating no functional task is always optimal. Previous algorithms usually allocate all tasks and cannot suitably select the functional tasks. Because of the interaction and sequential influence, the total effects of the functional tasks are too complex to exactly calculate. We fully analyze this problem and then design a heuristic algorithm. The heuristic algorithm scores the functional tasks referring to linear threshold model (used to analyze the sequential influence of a functional task). The simulated experiments demonstrate that the heuristic algorithm can outperform the benchmark algorithms.

As an essential problem in robotics, foraging means that robots collect objects from a given environment and return them to a specified location. On many occasions, robots are required to perform foraging tasks in adversarial environments, such as battlefield rescue, where potential adversaries may damage robots with a certain probability. The longer an individual robot moves through adversarial environments, the higher the probability of being damaged by adversaries. The robot system can gain utility only when the robot brings carried objects back to a predetermined home station. Such a risk of being damaged makes returning home at different locations potentially relevant to the expected utility produced by the robot. Thus, the individual robot faces a dilemma when it responds to the potential risks in adversarial environments: whether to return the carried resources home or continue foraging tasks. In this article, two fundamental environment settings are discussed, homogeneous cases and heterogeneous cases. The former is analyzed as having both the optimal substructure property and the non-aftereffect property. Then, we present a dynamic programming (DP) algorithm that can find an optimal solution with polynomial time complexity. For the latter, it is proven that finding an optimal solution is \( \mathcal {NP} \) -hard. We then propose a heuristic algorithm: A division hierarchical path planning (DHPP) algorithm that is based on the idea of dividing the foraging routes generated initially into a certain number of subroutes to dilute risks. Finally, these algorithms are extensively evaluated in simulations, concluding that in adversarial environments, they can significantly improve the productivity of an individual robot before it is damaged.

Existing studies on the multirobot foraging problem often assume safe settings, in which nothing in an environment hinders the robots’ tasks. In many real-world applications, robots have to collect objects from hazardous environments like earthquake rescue, where possible risks exist, with possibilities of destroying robots. At this stage, there are no targeted algorithms for foraging robots in hazardous environments, which can lead to damage to the robot itself and reduce the final foraging efficiency. A motivating example is a rescue scenario, in which the lack of a suitable solution results in many victims not being rescued after all available robots have been destroyed. Foraging robots face a dilemma after some robots have been destroyed: whether to take over tasks of the destroyed robots or continue executing their remaining foraging tasks. The challenges that arise when attempting such a balance are twofold: (1) the loss of robots adds new constraints to traditional problems, complicating the structure of the solution space, and (2) the task allocation strategy in a multirobot team affects the final expected utility, thereby increasing the dimension of the solution space. In this study, we address these challenges in two fundamental environmental settings: homogeneous and heterogeneous cases. For the former case, a decomposition and grafting mechanism is adopted to split this problem into two weakly coupled problems: the foraging task execution problem and the foraging task allocation problem. We propose an exact foraging task allocation algorithm, and graft it to another exact foraging task execution algorithm to find an optimal solution within the polynomial time. For the latter case, it is proven \( \mathcal {NP} \) -hard to find an optimal solution in polynomial time. The decomposition and grafting mechanism is also adopted here, and our proposed greedy risk-aware foraging algorithm is grafted to our proposed hierarchical agglomerative clustering algorithm to find high-utility solutions with low computational overhead. Finally, these algorithms are extensively evaluated through simulations, demonstrating that compared with various benchmarks, they can significantly increase the utility of objects returned by robots before all the robots have been stopped.

Multiagent pursuit-evasion problems have been widely investigated in a number of related areas. In many situations, the pursuers have energy limitation when pursuing the evader, and the pursuers moving at higher speeds consume energy more quickly. The energy limitation creates two problems for the pursuers. First, constantly moving at the maximum speed is not optimal because the energy will be exhausted quickly. Therefore, it is necessary to control the speed to achieve the optimal energy consumption, but previous studies have not presented a suitable speed-control algorithm. Second, a fraction of pursuers with insufficient energy may fail to complete the sub-tasks, and the evader can vary the escape strategy by considering the energy limitation of the pursuers. Therefore, the pursuers with sufficient energy need to help the pursuers with insufficient energy and address the variety of the evader's escape strategy. The existing pursuing strategies are not self-adaptable to satisfy this requirement. In this paper, we present a pursuing strategy consisting of both a speed-control algorithm and a path planning algorithm as sub-algorithms. Additionally, we integrate the subalgorithms into a self-adaptable cooperation strategy based on simulated annealing, which makes the pursuers adaptably help each other and is adaptable to the variety of escape strategies. The experimental results show that our strategy leads to higher capture success ratios than previous strategies when the energy of pursuers is limited. The experimental data also show that our strategy is more adaptable to the variety of the pursuers' maximum speed and the variety of the evader's escape strategies.

In online social network, the personal information dissemination behavior is reported to be affected by the clash of social individuals' biased opinions. In this paper, we present a model to discuss the influence of individual biased opinion on diffusion dynamics. Based on multi-agent simulations, we obtain some conclusions which are helpful for recommender systems and in controlling diffusion. In addition, our study offers potential avenues for the study of diffusion dynamics with personal biases.