Yifan Cai

An improved PSO-based approach with dynamic parameter tuning for cooperative multi-robot target searching in complex unknown environments

Target searching in complex unknown environments is a challenging aspect of multi-robot cooperation. In this paper, an improved particle swarm optimisation (PSO) based approach is proposed for a team of mobile robots to cooperatively search for targets in complex unknown environments. The improved cooperation rules for a multi-robot system are applied in the potential field function, which acts as the fitness function of the PSO. The main improvements are the district-difference degree and dynamic parameter tuning. In the simulation studies, various complex situations are investigated and compared to the previous research results. The results demonstrate that the proposed approach can enable the multi-robot system to accomplish the target searching tasks in complex unknown environments.

A combined hierarchical reinforcement learning based approach for multi-robot cooperative target searching in complex unknown environments

Effective cooperation of multi-robots in unknown environments is essential in many robotic applications, such as environment exploration and target searching. In this paper, a combined hierarchical reinforcement learning approach, together with a designed cooperation strategy, is proposed for the real-time cooperation of multi-robots in completely unknown environments. Unlike other algorithms that need an explicit environment model or select parameters by trial and error, the proposed cooperation method obtains all the required parameters automatically through learning. By integrating segmental options with the traditional MAXQ algorithm, the cooperation hierarchy is built. In new tasks, the designed cooperation method can control the multi-robot system to complete the task effectively. The simulation results demonstrate that the proposed scheme is able to effectively and efficiently lead a team of robots to cooperatively accomplish target searching tasks in completely unknown environments.

Fuzzy logic-based multi-robot cooperation for object-pushing

Multi-robot cooperation is an important issue in robotics. Collaboration can improve the productivity and complete some complex tasks effectively. In this paper, a two-stage fuzzy logic-based control scheme is proposed for a team of robots to cooperatively push an object to a target location. A fuzzy logic-based multi-robot cooperation is proposed to effectively generate collision-free paths for the robots, where the number of robots and the environment are uncertain. The simulation results demonstrate the feasibility of the proposed approach. In comparison to other methods, the proposed fuzzy logic-based control is easier to implement and is more effective at resolving problems with uncertainties.

A PSO-Based Approach with Fuzzy Obstacle Avoidance for Cooperative Multi-Robots in Unknown Environments

Cooperative exploration in unknown environments is fundamentally important in robotics, where the realtime path planning and proper task allocation strategies are desirable for multi-robot cooperation. In this paper, a PSO-based approach, combined with a fuzzy obstacle avoidance module, is proposed for cooperative robots to accomplish the target searching tasks in unknown environments. The proposed cooperation strategy for a multi-robot system makes use of the potential field function as the fitness function of PSO, while the proposed fuzzy obstacle-avoidance method can improve the robot trajectory smoothness. In the simulation studies, various scenarios are investigated and compared to the method without fuzzy rules. The effectiveness of the proposed approach and the robot trajectory smoothness improvement are demonstrated by simulation and comparison studies.

A PSO-based approach to cooperative foraging multi-robots in unknown environments

Cooperative foraging tasks in unknown environments are fundamentally important in robotics, where the real-time path planning and proper task allocation strategies are desirable for multi-robot cooperation. In this paper, an improved potential field-based (IPPSO) approach is proposed for cooperative robots to accomplish the foraging tasks in unknown environments. The proposed cooperation strategy for a multi-robot system makes use of the potential field function as the fitness function of PSO. In the simulation studies, various scenarios are investigated. The effectiveness of the proposed approach is demonstrated by simulation studies.

A Hierarchical Reinforcement Learning Based Approach for Multi-robot Cooperation in Unknown Environments

Reinforcement learning is a good method for multi-robot systems to handle tasks in unknown environments or with obscure models. MAXQ is a hierarchical reinforcement learning algorithm, which is limited by some inherent problems. In addition, much research has focused on the completion of the task, rather than the ability to deal with new tasks. In this paper, an improved MAXQ approach is adopted to tune the parameters of the cooperation rules. The proposed scheme is applied to target searching tasks by multi-robots. The simulation results demonstrate the effectiveness and efficiency of the proposed scheme.

An improved PSO-based approach with dynamic parameter tuning for cooperative target searching of multi-robots

Multi-robot cooperation for target searching in completely unknown environments is a challenging topic that receives increasing attentions. In this paper, a novel potential field-based particle swarm optimization (PPSO) approach is applied for a team of mobile robots to cooperatively search for and reach targets in completely unknown environments. The target locations are unknown, where the robots explore the area and find the targets in a reasonable and effective way. The potential field function is the fitness function of the PSO, which is used to evaluate the exploration priority of the unknown area. The cooperation rules are defined in the proposed approach to lead the multi-robot system to explore the unknown environment. In addition, the district-difference degree and dynamic parameter tuning is added in the improved PPSO approach (IPPSO) to help the multi-robot system to complete complex tasks. The parameter setting is discussed in the simulation studies, and the effects of the parameter tuning is demonstrated by the experiment results.

A PSO-based approach to cooperative foraging tasks of multi-robots in completely unknown environments

Cooperative foraging tasks in unknown environments are fundamentally important in robotics, where the real-time path planning and proper task allocation strategies are desirable for multi-robot cooperation. In this paper, an improved potential field-based PSO (IPPSO) approach is applied to accomplish the cooperative foraging tasks in completely unknown environments, compared to the cases using the PPSO approach. The proposed cooperation strategy for a multi-robot system makes use of the potential field function as the fitness function of PSO, while the added dynamic parameter tuning and district-difference degree can increase the work efficiency, and help the multi-robot system to complete the tasks in complex environments. In the simulation studies, various scenarios are investigated. The effectiveness of the proposed approach is demonstrated by the experiment results.

A potential field-based PSO approach for cooperative target searching of multi-robots

Multi-robot cooperation receives increasing attention. Collaboration among the robots can improve the efficiency and effectiveness for some complex tasks. Target searching in completely unknown environments is a challenging topic for multi-robot cooperation. In this paper, a novel potential field-based particle swarm optimization (PPSO) approach is proposed for a team of mobile robots to cooperatively search targets in unknown environments. The potential field function is the fitness function of the PSO, which is used to evaluate the exploration priority of the unknown area. The proper cooperation rules for the multi-robot system are defined in the proposed PPSO approach. In the simulation studies, various situations are investigated to test the flexibility and applicability of the proposed approach. In addition, the results are compared to the ones with other commonly used methods to demonstrate the advantage of the proposed method in exploration efficiency.