Particle Swarm Optimization (PSO) has demonstrated efficacy in addressing static path planning problems. Nevertheless, such application on dynamic scenarios has been severely precluded by PSO's low computational efficiency and premature convergence downsides. To address these limitations, we proposed a Tensor Operation Form (TOF) that converts particle-wise manipulations to tensor operations, thereby enhancing computational efficiency. Harnessing the computational advantage of TOF, a variant of PSO, designated as Self-Evolving Particle Swarm Optimization (SEPSO) was developed. The SEPSO is underpinned by a novel Hierarchical Self-Evolving Framework (HSEF) that enables autonomous optimization of its own hyper-parameters to evade premature convergence. Additionally, a Priori Initialization (PI) mechanism and an Auto Truncation (AT) mechanism that substantially elevates the real-time performance of SEPSO on dynamic path planning problems were introduced. Comprehensive experiments on four widely used benchmark optimization functions have been initially conducted to corroborate the validity of SEPSO. Following this, a dynamic simulation environment that encompasses moving start/target points and dynamic/static obstacles was employed to assess the effectiveness of SEPSO on the dynamic path planning problem. Simulation results exhibit that the proposed SEPSO is capable of generating superior paths with considerably better real-time performance (67 path planning computations per second in a regular desktop computer) in contrast to alternative methods. The code and video of this paper can be accessed here.

We consider the multi-robot task allocation (MRTA) problem in an initially unknown environment. The objective of the MRTA problem is to find a schedule or sequence of tasks that should be performed by a set of robots so that the cost or energy expended by the robots is minimized. Existing solutions for the MRTA problem mainly concentrate on finding an efficient task allocation among robots, without directly incorporating changes to tasks' costs originating from changes in robots' paths due to dynamically detected obstacles while moving between tasks. Dynamically updating path costs is an important aspect as changing path costs can alter the task sequence for robots that corresponds to the minimum cost. In this paper, we attempt to address this problem by developing an algorithm called MRTA-RTPP (MRTA with Real-time Path Planning) by integrating a greedy MRTA algorithm for task planning with a Field D*-based path planning algorithm. Our technique is capable of handling dynamic changes in a robot's path costs due to static as well as mobile obstacles and computes a new task schedule if the original schedule is no longer optimal due to the robots' replanned paths. We have verified our proposed technique on physical Corobot robots that perform surveillance-like tasks by visiting a set of locations. Our experimental results show that that our MRTA technique is able to handle dynamic path changes while reducing the cost of the schedule to the robots