Authors: Rajat Kumar Jenamani, Rahul Kumar, Parth Mall, Kushal Kedia
Abstract: Sampling based methods are widely used for robotic motion planning. Traditionally, these samples are drawn from probabilistic ( or deterministic ) distributions to cover the state space uniformly. Despite being probabilistically complete, they fail to find a feasible path in a reasonable amount of time in constrained environments where it is essential to go through narrow passages (bottleneck regions). Current state of the art techniques train a learning model (learner) to predict samples selectively on these bottleneck regions. However, these algorithms depend completely on samples generated by this learner to navigate through the bottleneck regions. As the complexity of the planning problem increases, the amount of data and time required to make this learner robust to fine variations in the structure of the workspace becomes computationally intractable. In this work, we present (1) an efficient and robust method to use a learner to locate the bottleneck regions and (2) two algorithms that use local sampling methods to leverage the location of these bottleneck regions for efficient motion planning while maintaining probabilistic completeness.

Authors: Snehal Reddy Koukuntla, Manjunath Bhat, Shamin Aggarwal, Rajat Kumar Jenamani, Jayanta Mukhopadhyay
Abstract: Randomised sampling-based algorithms such as RRT and RRT* have widespread use in path planning, but they tend to take a considerable amount of time and space to converge towards the destination. RRT* with artificial potential field (RRT*-APF) is a novel solution to pilot the RRT* sampling towards the destination and away from the obstacles, thus leading to faster convergence. But the ideal potential function varies from one configuration space to another and different sections within a single configuration space as well. Finding the potential function for each section for every configuration space is a grueling task. In this paper, we divide the 2 dimensional configuration space into multiple regions and propose a deep learning based approach in the form of a custom feedforward neural network to tune the sensitive parameters , upon which the potential function depends. These parameters act as a heuristic and pilots the tree towards the destination, which has a substantial effect on both the rate of convergence and path length. Our algorithm, DL-P-RRT* has shown the ability to learn and emulate the shortest path and converges much faster than the current random sampling algorithms as well as deterministic path planning algorithms. So, this algorithm can be used effectively in environments where the path planner is called multiple times, which is typical to events such as Robo-Soccer.

Authors: Saurabh Agarwal, Ashish Kumar Gaurav, Mehul Kumar Nirala, Sayan Sinha
Abstract: Path planning is an extremely important step in every robotics related activity today. In this paper, we present an approach to a real-time path planner which makes use of concepts from the random sampling of the Rapidly-exploring random tree and potential fields. It revises the cost function to incorporate the dynamics of the obstacles in the environment. Not only the path generated is significantly different but also it is much more optimal and rigid to breakdowns and features faster replanning. This variant of the Real-Time RRT* incorporates artificial potential field with a revised cost function.