Cooperative Path Planning for Object Transportation with Fault Management

Enhancing the serviceability of mobile robots is an important factor for improving regular work to a great extent. This approach has been implemented in areas such as industry, healthcare, and military. To ensure the successful implementation of the proposed work, it is important to have an impeccable collision-free path for mobile robots. This goal has been accomplished by developing an intelligent fault management system. The proposed work produces an efficient path through the use of a hybrid algorithm that combines the benefits of the sine cosine algorithm (SCA) and particle swarm optimization (PSO) algorithms. The proposed work reports on the object transportation by a pair or group of robots from source to destination, and the mentioned task can be proficiently completed in three steps: fault identification, fault resolution using robot replacement, and computation of a collision-free path. The proposed work was successfully implemented in a C language environment to showcase its competence in terms of execution time, path traveled, and path deviated. The presented comparative analysis of the proposed algorithm demonstrates the effectiveness of the approach in terms of several metrics, such as path planning, cooperation, and fault management. The proposed approach achieved path optimality by reducing the traveled path by approximately 9.6% compared to QCOV-R and 8.4% compared to the ABCO algorithm in an environment with a minimum of eight obstacles.