Abstract
This paper introduces a strategy for the path planning problem for platforms with limited sensor and processing capabilities. The proposed algorithm does not require any prior information but assumes that a mapping algorithm is used. If enough information is available, a global path planner finds sub-optimal collision-free paths within the known map. For the real time obstacle avoidance task, a simple and cost-efficient local planner is used, making the algorithm a hybrid global and local planning solution. The strategy was tested in a real, cluttered environment experiment using the Pioneer P3-DX and the Xbox 360 kinect sensor, to validate and evaluate the algorithm efficiency.
Highlights
Mobile robotics is one of the major study fields within robotics
It is the case of the famous artificial potential fields method (APF) [8], which creates artificial forces of repulsion and attraction that drives the robot to its destination
Complexity for any map given, not requiring a high computation effort. Heuristic methods such as genetic algorithms (GA) [21], neural-networks (NN) [22], ant colony optimization (ACO) [23] and particle swarm optimization (PSO) [24] usually show good results in terms of path planning for uncertain environments, but they can be very computationally expensive to converge to an optimal solution [25]
Summary
Mobile robotics is one of the major study fields within robotics. To achieve autonomous navigation, the agent must be able to plan a strategy that will efficiently, wihtin a feasible amount of time, lead to its destination point. Some strategies deal with the planning problem while the robot is moving (online), based on real time and local sensor measurements, which is called reactive navigation It is the case of the famous artificial potential fields method (APF) [8], which creates artificial forces of repulsion and attraction that drives the robot to its destination. A* algorithm and VFH to select sectors which will lead to better paths through the histogram grid Another local obstacle avoidance algorithm, the tangential escape [11,12] creates a virtual temporary goal that leads the robot in a path tangent to the obstacle, avoiding the collision. Hybrid strategies, combining other popular global and local planning methods, have been presented in [10,16,17,18]
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