Investigating an A-star Algorithm-based Fitness Function for Mobile Robot Evolution

Prabhu, Shanker G. R.; Kyberd, Peter; Wetherall, Jodie

doi:10.1109/icstcc.2018.8540734

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…In the generated configuration space dynamic cell map, the searching region of improved dynamic A* algorithm is in the form of sliding cube (see Figure 3) moving toward the collisionfree trajectory. 21 Since the configuration space has five dimensions, when the improved dynamic A* algorithm is searching for the optimal path, the five orthogonal axes (10 directions) provide the basic motion of the manipulator's 10 actions are as follows Another key part in the improved dynamic A* algorithm is evaluation of the manipulator's 10 actions. The evaluation function f k ð Þ is introduced to evaluate the 10 actions' extended node when the improved dynamic A* algorithm is taking the next action from the current node.…”

Section: The Target State Prediction Methods In the Configuration Space Mapmentioning

confidence: 99%

Capturing the dynamic target by the robot manipulator in high-dimensional configuration space map

Zhang

et al. 2020

International Journal of Advanced Robotic Systems

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In robotics, the tough problem about the dynamic target capturing consists of tracking the target by the robot manipulator and grasping the target by the robot finger. For the sake of space, this article deals with only the first problem, tracking the dynamic target by the robot manipulator. The traditional approaches of capturing the dynamic target may work well when they are employed in low-dimensional space by reinforcement learning or physical modeling. However, they fail to work well in high-dimensional space. The traditional approaches have four limitations with respect to Cartesian space, configuration space, reinforcement learning, and physical modeling. To overcome these limitations, this article implements improved dynamic A* algorithm in high-dimensional configuration space map to capture the target. First, a space injection model injects the collision detection and target position from the Cartesian space into the configuration space to construct a high-dimensional map. Then, the target capturing method including the improved dynamic A* algorithm is applied on the map to track and capture the target. Finally, the experiment performed in time-varying environment and the dynamic target achieves a reliable result. This article has proposed an approach that makes the robot manipulator motion planning more accurate in high-dimensional dynamic configuration space. This approach enables the multi-joint manipulator to avoid the obstacle while tracking the target in high-dimensional configuration space. It takes the advantages of heuristic algorithms in the process of target capturing method designing. It adds precision and speed to target tracking. The success of the approach may apply to any industrial robot tracking target, surgical operation, and space probes. And, it may lay a solid foundation for dynamics control with a scope for future investigations.

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Section: The Target State Prediction Methods In the Configuration Space Mapmentioning

confidence: 99%

Capturing the dynamic target by the robot manipulator in high-dimensional configuration space map

Zhang

et al. 2020

International Journal of Advanced Robotic Systems

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show abstract

“…Depending on the flexibility of heuristic function, A* can modify its own heuristic functions to adapt to different tasks. Adaptive A* algorithms are used in autonomous surface vessel navigation [23] and evolutionary robotics [24]. Since A* is limited by the heuristic function, the generated path always follows the edges of danger zones, which means that even the edges of danger zones are also dangerous.…”

Section: Introductionmentioning

confidence: 99%

Trajectory planning for UAV navigation in dynamic environments with matrix alignment Dijkstra

Wang

et al. 2022

Soft Comput

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The trajectory planning for UAVs in the dynamic environments is a challenging task. Many restrictions should be taken into consideration, including dynamic terrain collision, no-fly zone criteria, power and fuel criteria and so on. However, some methods treat dynamic restrictions as static in order to reduce cost and obtain efficient and acceptable paths. To achieve optimal, efficient and acceptable paths, we first use a high-dimension matrix, Extended Hierarchical Graph (EHG), to model the unexplored dynamic grid environment and to convert weather conditions to passable coefficient. The original shortest path planning task is then translated to plan the safest path. Therefore, we propose a new forward-exploration and backward-navigation algorithm, Matrix Alignment Dijkstra (MAD), to pilot UAVs. We use matrix alignment operation to simulate the parallel exploration process of all cells from time t to t+1. This exploration process can be accelerated on a GPU. Finally, we recall the optimal path according to the navigator matrix. In addition, we can achieve UAV path planning from one source cell to multiple target cells within one single run. We validate our method on a real dynamic weather dataset and get competitive results in both efficiency and accuracy. We analyse the performance of MAD on a grid-based benchmark dataset and artificial maze data. Simulated results show MAD is especially helpful when the grid-based environment is large-scale and dynamic.

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Does Lifelong Learning Affect Mobile Robot Evolution?

Prabhu

Kyberd

Melis

et al. 2021

Recent Advances in Soft Computing and Cybernetics

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Investigating an A-star Algorithm-based Fitness Function for Mobile Robot Evolution

Cited by 6 publications

References 12 publications

Capturing the dynamic target by the robot manipulator in high-dimensional configuration space map

Capturing the dynamic target by the robot manipulator in high-dimensional configuration space map

Trajectory planning for UAV navigation in dynamic environments with matrix alignment Dijkstra

Does Lifelong Learning Affect Mobile Robot Evolution?

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