2020 # 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 p…

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“…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…”

confidence: 99%

“…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…”

confidence: 99%

“…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.…”

confidence: 99%

“…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],…”

confidence: 99%

“…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].…”

mentioning

confidence: 99%