Abstract
Multirobot motion planning is always one of the critical techniques in edge intelligent systems, which involve a variety of algorithms, such as map modeling, path search, and trajectory optimization and smoothing. To overcome the slow running speed and imbalance of energy consumption, a swarm intelligence solution based on parallel computing is proposed to plan motion paths for multirobot with many task nodes in a complex scene that have multiple irregularly‐shaped obstacles, which objective is to find a smooth trajectory under the constraints of the shortest total distance and the energy‐balanced consumption for all robots to travel between nodes. In a practical scenario, the imbalance of task allocation will inevitably lead to some robots stopping on the way. Thus, we firstly model a gridded scene as a weighted MTSP (multitraveling salesman problem) in which the weights are the energies of obstacle constraints and path length. Then, a hybridization of particle swarm and ant colony optimization (GPSO‐AC) based on a platform of Compute Unified Device Architecture (CUDA) is presented to find the optimal path for the weighted MTSPs. Next, we improve the A∗ algorithm to generate a weighted obstacle avoidance path on the gridded map, but there are still many sharp turns on it. Therefore, an improved smooth grid path algorithm is proposed by integrating the dynamic constraints in this paper to optimize the trajectory smoothly, to be more in line with the law of robot motion, which can more realistically simulate the multirobot in a real scene. Finally, experimental comparisons with other methods on the designed platform of GPUs demonstrate the applicability of the proposed algorithm in different scenarios, and our method strikes a good balance between energy consumption and optimality, with significantly faster and better performance than other considered approaches, and the effects of the adjustment coefficient q on the performance of the algorithm are also discussed in the experiments.
Highlights
Robot technology is always a remarkably interdisciplinary topic of study, one that can be applied to various engineering practices as well as emerging industrial fields
When the motion control for multirobot navigation is used in large-scale dynamic scenarios, a personalized route algorithm was presented by introducing the Polychromatic Sets (PS) for users to obtain real-time route that meet their travel preferences [4], all obstacle features can be integrated into a scalar potential field to make decisions [30], an online adaptive replanning strategy for multiple drones flying in an urban environment with a number of dynamic changes [5], and a reliable routing optimization scheme based on the Manhattan mobility model is presented [31] for UAV real-time planning in a vehicular ad hoc networks (VANETs)
According to the proposed algorithm, we find that the energy of path smoothing increases rapidly with the increase of the standard deviation of path length, which demonstrates that the equalization algorithm is effective, and balancing the path length of each robot is the guarantee of balancing energy consumption
Summary
Robot technology is always a remarkably interdisciplinary topic of study, one that can be applied to various engineering practices as well as emerging industrial fields. With decades of year development, there are various studies of motion planner including traditional interpolating and heuristics approaches employed effectively in multirobot navigation over different environmental conditions so far Even though, it is still an open challenging for multirobot regarding uncertain constraints, especially dynamic adaptive, scalable, and online replanning are still open and challenge problems for practical applications in real-world with large-scale nodes [3,4,5], which are far from being completely solved, and many practical engineering problems are still tackled today using learning-based heuristics algorithms, for the purpose of robustness, safety, speed, and efficiency. At the same time, combined with the characteristics of easy parallelization of swarm intelligence algorithm, aiming at the problems of high accuracy but long execution time of hybrid iterative algorithm, considering the energy consumption balance of multiple robots, by developing a GPU based on CUDA platform to accelerate the running speed of the algorithm, a smooth grid path algorithm combined with the minimum snap algorithm is proposed to make the final path globally optimal, safe, and fast, collision-free, energy-balanced and meet the dynamic constraints
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