Hybrid intelligent path planning for articulated rovers in rough terrain (original) (raw)
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Fuzzy terrain-based path planning for planetary rovers
This paper presents a fuzzy terrain-based path planning method for planetary rovers operating on rough natural terrain. The focus of this approach is on planning an optimally safe path of minimum traversal cost, which is calculated from linguistic descriptors of terrain traversability. The method incorporates the Traversability Map, a fuzzy map representation of traversal difficulty of the terrain, into the path planning logic. The search methodology uses a traversal cost function that is derived directly from this Traversability Map. The path planning method is developed in detail and experimental results are presented.
h i g h l i g h t s • Proposed a new potential field method for rough terrain path planning for a rover. • A gradient function is introduced in the conventional potential field method. • The gradient function depends on the roll, pitch and yaw angles of the rover. • Weights of potential field function are optimized by using GA. • Results prove that the new method is superior to conventional potential field method. a b s t r a c t Motion planning of rovers in rough terrains involves two parts of finding a safe path from an initial point to a goal point and also satisfying the path constraints (velocity, wheel torques, etc.) of the rover for traversing the path. In this paper, we propose a new motion planning algorithm on rough terrain for a 6 wheel rover with 10 DOF (degrees of freedom), by introducing a gradient function in the conventional potential field method. The new potential field function proposed consists of an attractive force, repulsive force, tangential force and a gradient force. The gradient force is a function of the roll, pitch and yaw angles of the rover at a particular location on the terrain. The roll, pitch and yaw angles are derived from the kinematic model of the rover. This additional force component ensures that the rover does not go over very high gradients and results in a safe path. Weights are assigned to the various components of the potential field function and the weights are optimized using genetic algorithms to get an optimal path that satisfies the path constraints via a cost function. The kinematic model of the rover is also derived that gives the wheel velocity ratio as it traverses different gradients. Quasi static force analysis ensures stability of the rover and prevents wheel slip. In order to compare different paths, four different objective functions are evaluated each considering energy, wheel slip, traction and length of the path. A comparison is also made between the conventional 2D potential field method and the newly proposed 3D potential field method. Simulation and experimental results show the usefulness of the new method for generating paths in rough terrains.
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This paper proposes a path planning method for a wheeled mobile robot operating in rough terrain dynamic environments using a combination of A* search algorithm and potential field method. In this method, the mobile robot uses the structured light system to extract real terrain data as a discrete points to generate a b-spline surface. The terrain is classified based on the slope and elevation using a fuzzy logic controller and a user defined cost function is generated. A combination of A* and potential field method has been introduced to find the path from the start location to goal location according to the cost function. The A* algorithm determines the path that globally optimizes terrain roughness, curvature and length of the path, and the potential field method has been used as a local planner which performs an on-line planning to avoid the newly detected obstacles by the sensory information. The developed potential function is found to be able to avoid local minima in the work space.The results shows the effectiveness of the proposed algorithm.
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