Motion path planning of soccer training auxiliary robot based on genetic algorithm in fixed-point rotation environment

Abstract

Soccer training assisted robot system is a combination of robotics and artificial intelligence. Motion path planning is an important part of soccer training assisted robot decision system. Path planning aims to find an optimal path, complete dynamic and static obstacle avoidance, and thus can timely and quickly carry out the path planning of the soccer training assisted robot cooperation strategy. Aiming at the problem of soccer training auxiliary robot motion path planning, this paper proposes a global path planning method based on genetic algorithm, which avoids the problem of unsmooth path and large computation. Under the premise of ensuring the completeness of search, this method transforms the soccer training assisted robot path planning problem into the path search problem of the soccer training assisted robot system center. Under the premise of using the fixed-point rotation method on a large scale, the genetic algorithm is adopted for the case of some special narrow-channel environments, and the action area is determined by simple search. Finally, the planning path of each soccer training-assisted robot in the system is obtained. Finally, the simulation results verify the effectiveness of the algorithm.

Research on Static/Dynamic Global Path Planning Based on Improved A ∗ Algorithm for Mobile Robots

“…Input: S, G Output: the optimal path (1) initialize openList and closedList (2) set Pn: the least costly node in the openList (3) while true do (4) fnd the 8 neighborhood nodes of Pn (5) whether the neighborhood node is in the openList (6) if in the openList then (7) compute g(m), h(m) and f(m) (8) else add to the openList ( 9) end (10) repeat search the least costly node in the openList (11) if G in the closedList then (12) break ( 13) end (14) path-smoothing with B-spline curve (15) return the optimal path ALGORITHM 1: A * pseudocode. 8…”

“…Te proposed QAPF learning algorithm can increase the efciency of learning using the combination of Q-learning and the APF method. Intelligent bionic algorithms include ant colony optimization [10], genetic [11], particle swarm optimization [12]. Orozco-Rosas et al [13] proposed a hybrid path planning algorithm based on membrane pseudobacterial potential feld (MemPBPF) to improve the efectiveness of obstacle avoidance and smoothness.…”

A novel improved elephant herding optimization for path planning of a mobile robot

“…Algorithms based on heuristics, including fuzzy logic [1], [2], neural networks [2], the neurofuzzy technique [3], [4], probabilistic neuro-fuzzy [3], and the Q-learning [5], [6], which is known in the planning of trajectories of mobile robots, by its capacity of self-learning without requiring an a priori model of the environment. Nature-inspired algorithms, used to optimize the length of the path, including, genetic algorithm (GA) [7], [8], particle swarm optimization (PSO) [9]- [11], ant colony optimization (ACO) [12],…”

A Directional Heuristics Pulse Algorithm for a Two Resources Constrained Shortest Path Problem with Reinitialization

“…In public transport, the total travel time is usually limited (such as the route to the airport or railway station). Other applications appear in aircraft management system [19,11,17,7], railway management [9,20,15], the optimal path planning for robots [12,25]. In network communication, the equipment can be placed in the network to supplement the signal [4,34].…”