Volume 35 #2

Contents

1. Neurophysiological support of motor control in “Hybrid” positions. Literature review
2. Functional investigations of the primary auditory cortex in the cat
3. Analysis of individual strategies in categorical visual search
4. Effect of auditory LTP-like stimulation on the processing of sounds
5. Gradient algorithm for raindrop detection on a sequence of images
6. Local path finding in autonomous car state lattice based on multi-criteria optimization

In this work algorithm of local path planning for the ground autonomous vehicle with car-type non-holonomic kinematics is proposed. The constructed path is represented by a path in a graph of possible maneuvers that minimizes predefined quality measures. The maneuvers graph is constructed by coping along the global path of precalculated in curvilinear coordinate system maneuvers' templates which are kinematically feasible. The use of a pre-calculated template speeds up runtime algorithm execution. The weight of each graph edge is a weighted sum of several criteria. The search algorithm minimizes maneuvring while keeping the maximum possible distance from obstacles. The obstacles can be dynamically detected as well as extract from static occupancy grid map. The path search in the weighted graph is executed with Dijkstra’s algorithm. The proposed planning algorithm was tested on generated road scenes. Each scene is a static road situation with obstacles in which a safe path has to be found. The safe local path was found in which scenes where it was feasible. The generated local paths are on average just 1.3% longer than the predefined shortest global path, which does not account for obstacles.

Key words: path planning, local path planning, state lattice, multi-objective optimization, collision avoidance, autonomous vehicle, nonholonomic kinematics

DOI: 10.31857/S0235009221020062

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