SUMMARY
Advancement in the field of autonomous motion planning has enabled the realisation of fully autonomous unmanned vehicles. Sampling based motion planning algorithms have shown promising prospects in generating fast, effective and practical solutions to different motion planning problems in unmanned vehicles for both civilian and military applications. But the goal bias introduced by heuristic probability shaping to generate faster solution may result in local collisions. A simple, real-time method is proposed for goal direction by preferential selection of a state from a sampled pair of random state, based on the distance to goal. This limits the graph motions resulting in smaller data structure, making the algorithm optimised for time and solution length. This would enable unmanned vehicles to take shorter paths and avoid collisions in obstacle rich environment. The approach is analysed on a sampling based algorithm, rapidly-exploring random tree (RRT) which computes motion plans under constrain of time. This paper proposes an algorithm called ’goal directed RRT (GRRT)’ building on the basic RRT algorithm, providing an alternative to probabilistic goal biasing, thereby avoiding local collision. The approach is evaluated by benchmarking it with RRT algorithm for kinematic car, dynamic car and a quadrotor and the results show improvements in length of the motion plans and the time of computing.