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Gautham P. Das

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4 papers

1 author row

ICRA Conference 2020 Conference Paper

Context Dependant Iterative Parameter Optimisation for Robust Robot Navigation

Adam Binch
Gautham P. Das
Jaime Pulido Fentanes
Marc Hanheide

Progress in autonomous mobile robotics has seen significant advances in the development of many algorithms for motion control and path planning. However, robust performance from these algorithms can often only be expected if the parameters controlling them are tuned specifically for the respective robot model, and optimised for specific scenarios in the environment the robot is working in. Such parameter tuning can, depending on the underlying algorithm, amount to a substantial combinatorial challenge, often rendering extensive manual tuning of these parameters intractable. In this paper, we present a framework that permits the use of different navigation actions and/or parameters depending on the spatial context of the navigation task. We consider the respective navigation algorithms themselves mostly as a "black box", and find suitable parameters by means of an iterative optimisation, improving for performance metrics in simulated environments. We present a genetic algorithm incorporated into the framework, and empirically show that the resulting parameter sets lead to substantial performance improvements in both simulated and real-world environments in the domain of agricultural robots.

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IROS Conference 2020 Conference Paper

Incorporating Spatial Constraints into a Bayesian Tracking Framework for Improved Localisation in Agricultural Environments

Muhammad Waqas Khan
Gautham P. Das
Marc Hanheide
Grzegorz Cielniak

Global navigation satellite system (GNSS) has been considered as a panacea for positioning and tracking since the last decade. However, it suffers from severe limitations in terms of accuracy, particularly in highly cluttered and indoor environments. Though real-time kinematics (RTK) supported GNSS promises extremely accurate localisation, employing such services are expensive, fail in occluded environments and are unavailable in areas where cellular base stations are not accessible. It is, therefore, necessary that the GNSS data is to be filtered if high accuracy is required. Thus, this article presents a GNSS-based particle filter that exploits the spatial constraints imposed by the environment. In the proposed setup, the state prediction of the sample set follows a restricted motion according to the topological map of the environment. This results in the transition of the samples getting confined between specific discrete points, called the topological nodes, defined by a topological map. This is followed by a refinement stage where the full set of predicted samples goes through weighting and resampling, where the weight is proportional to the predicted particle's proximity with the GNSS measurement. Thus, a discrete space continuous-time Bayesian filter is proposed, called the Topological Particle Filter (TPF). The proposed TPF is put to test by localising and tracking fruit pickers inside polytunnels. Fruit pickers inside polytunnels can only follow specific paths according to the topology of the tunnel. These paths are defined in the topological map of the polytunnels and are fed to TPF to tracks fruit pickers. Extensive datasets are collected to demonstrate the improved discrete tracking of strawberry pickers inside polytunnels thanks to the exploitation of the environmental constraints.

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IROS Conference 2019 Conference Paper

Improving Local Trajectory Optimisation using Probabilistic Movement Primitives

R. B. Ashith Shyam
Peter Lightbody
Gautham P. Das
Pengcheng Liu 0005
Sebastián Gómez-González
Gerhard Neumann

Local trajectory optimisation techniques are a powerful tool for motion planning. However, they often get stuck in local optima depending on the quality of the initial solution and consequently, often do not find a valid (i. e. collision free) trajectory. Moreover, they often require fine tuning of a cost function to obtain the desired motions. In this paper, we address both problems by combining local trajectory optimisation with learning from demonstrations. The human expert demonstrates how to reach different target end-effector locations in different ways. From these demonstrations, we estimate a trajectory distribution, represented by a Probabilistic Movement Primitive (ProMP). For a new target location, we sample different trajectories from the ProMP and use these trajectories as initial solutions for the local optimisation. As the ProMP generates versatile initial solutions for the optimisation, the chance of finding poor local minima is significantly reduced. Moreover, the learned trajectory distribution is used to specify the smoothness costs for the optimisation, resulting in solutions of similar shape as the demonstrations. We demonstrate the effectiveness of our approach in several complex obstacle avoidance scenarios.

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IROS Conference 2011 Conference Paper

A fast distributed auction and consensus process using parallel task allocation and execution

Gautham P. Das
T. Martin McGinnity
Sonya Coleman
Laxmidhar Behera

In a multi-robot system, the coordination and cooperation among the robots determine the effectiveness of task execution. Different centralised and distributed task allocation algorithms have been proposed by researchers. Recently consensus based task allocation has been extensively researched because of its robustness in handling large teams of robots. We propose a new auction and consensus based algorithm for fast task allocation in parallel with task execution. The performance of the proposed algorithm under different conditions is analyzed and compared with other distributed consensus algorithms.

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