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This article proposes a new, efficient path-planning algorithm for articulated steering vehicles operating in semistructured environments, in which obstacles are detected online by the vehicle’s sensors. The first step of the algorithm is offline

and computes a finite set of feasible motions that connect discrete robot states in order to construct a search space. The motion primitives are parameterized using B´ezier curves and optimized as a nonlinear programming problem (NLP) equivalent to the constrained path planning problem. Applying the A_ searchalgorithm to the search space produces the shortest paths as a sequence of these primitives. The sequence is drivable and suboptimal, but it can cause unnatural swerves. Therefore, online path smoothing, which uses a gradient-based method, is applied in order to solve another NLP. Numerical simulations demonstrate that performance of the proposed algorithm is significantly better than that of existing methods when determining constrained path optimization. Also, field experimental results demonstrate the successful generation of fast, safe trajectories for real-time autonomous driving.

Existing System	Proposed System
In the existing system robot can be operated only in the manual mode. So it is difficult to perform in automatic operations where it doesn’t require humans Drawbacks of existing system: Ø  It cannot be operated automatically Ø  Less operations can be performed using robot Ø  It’s not user friendly Ø  Cannot find the exact location of the robot	In the proposed system robot can be operated in both automatic and the manual mode. So that by using this two mode, vast operations can be performed. Advantages of proposed system:      Ø  It can be operated automatically.     Ø  More operations can be performed using robot      Ø  User friendly robot    Ø  By using gps we can find the exact location of the robot