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Papers by Tirthankar Bandyopadhyay
Frontiers in Robotics and AI
Firm foot contact is the top priority of climbing robots to avoid catastrophic events, especially... more Firm foot contact is the top priority of climbing robots to avoid catastrophic events, especially when working at height. This study proposes a robust planning and control framework for climbing robots that provides robustness to slippage in unknown environments. The framework includes 1) a center of mass (CoM) trajectory optimization under the estimated contact condition, 2) Kalman filter–like approach for uncertain environment parameter estimation and subsequent CoM trajectory re-planing, and 3) an online weight adaptation approach for whole-body control (WBC) framework that can adjust the ground reaction force (GRF) distribution in real time. Though the friction and adhesion characteristics are often assumed to be known, the presence of several factors that lead to a reduction in adhesion may cause critical problems for climbing robots. To address this issue safely and effectively, this study suggests estimating unknown contact parameters in real time and using the evaluated cont...
IEEE Transactions on Intelligent Transportation Systems, 2022
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020
Understanding the kinematics of a ground robot is essential for efficient navigation. Based on th... more Understanding the kinematics of a ground robot is essential for efficient navigation. Based on the kinematic model of a robot, its full motion capabilities can be represented by theoretical motion primitives. However, depending on the environment and/or human preferences, not all of those theoretical motion primitives are desirable and/or achievable. This work presents a method to identify effective motion primitives (eMP) from continuous trajectories for autonomous ground robots. The pipeline efficiently performs segmentation, representation and reconstruction of the motion primitives, using initial human-driving behaviour as a guide to create a motion primitive library. Hence, this strategy incorporates how the environment affects the robot operation regarding accelerations, speed, braking, and steering behaviours.The method is thoroughly tested on an autonomous car-like electric vehicle, and the results show excellent generalisation of the theoretical motion primitive distribution to real vehicle. The experiments are carried out on large site with very diverse characteristics, illustrating the applicability of the method.
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020
Directionality in path planning is essential for efficient autonomous navigation in a number of r... more Directionality in path planning is essential for efficient autonomous navigation in a number of real-world environments. In many map-based navigation scenarios, the viable path from a given point A to point B is not the same as the viable path from B to A. We present a method that automatically incorporates preferred navigation directionality into a path planning costmap. This ‘preference’ is represented by coloured paths in the costmap. The colourisation is obtained based on an analysis of the driving trajectory generated by the robot as it navigates through the environment. Hence, our method augments this driving trajectory by intelligently colouring it according to the orientation of the robot during the run. Creating an analogy between the vehicle orientation angle and the hue angle in the Hue-Saturation-Value colour space, the method uses the hue, saturation and value components to encode the direction, directionality and scalar cost, respectively, into a costmap image. We describe a costing function to be used by the A* algorithm to incorporate this information to plan direction-aware vehicle paths. Our experiments with LiDAR-based localisation and autonomous driving in real environments illustrate the applicability of the method.
2019 International Conference on Robotics and Automation (ICRA), 2019
Springer Proceedings in Advanced Robotics, 2020
This paper introduces the N-ey[any] Wheel system, a set of modular wheels that enable bespoke pla... more This paper introduces the N-ey[any] Wheel system, a set of modular wheels that enable bespoke platform development for rapid field deployments. The N-ey Wheel is inuenced by existing modular and inspection robots but provides a simple-to-operate solution to exploring various environments. Each wheel provides two degrees of freedom allowing any continuous orientation to be achieved within a plane. Onboard computing and a wificonnection enables the N-ey Wheels to operate individually or collaboratively. Heterogeneous robot platforms can be created as required through the use of adaptors. With robots of differing shapes, sizes and configurations able to be created at run time as demonstrated within the laboratory and in the field. The dynamic nature of the system model dictates the control characteristics providing differential, Ackerman and nonholonomic omnidirectional control options to the user.
Motion planning under uncertainty is critical for robust autonomy. The Partially Observable Marko... more Motion planning under uncertainty is critical for robust autonomy. The Partially Observable Markov Decision Process (POMDP) is a principled and general framework for solving such problems. Although solving a POMDP problem exactly is computationally intractable, in the past decade, many practical methods have been proposed to approximate solution of POMDPs that represent motion planning under uncertainty problems. However, the problem remains relatively open when it involves robots with complex non-linear dynamics. Recently, linearization-based methods that are derived from the Linear Quadratic Gaussian (LQG) controller have been shown to perform well in some planning under uncertainty problems with non-linear robot dynamics. However, it is not clear what the effect of linearization to motion planning under uncertainty is. The control and estimation results have clearly indicated that linearization performs well only when the non-linear dynamics is “weak”. These results will definite...
Advances in Astronautics Science and Technology, 2020
Robotics: Science and Systems XVII, 2021
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018
Experimental Robotics, 2015
2014 IEEE International Conference on Robotics and Automation (ICRA), 2014
This paper proposes a semantic mapping method based on pedestrian activity in the urban road envi... more This paper proposes a semantic mapping method based on pedestrian activity in the urban road environment. Pedestrian activity patterns are learned from pedestrian tracks collected by a mobile platform. With the learned knowledge of pedestrian activity, semantic mapping is performed using Bayesian classification techniques. The proposed method is tested in real experiments, and shows promising results in recognizing four activity-related semantic properties of the urban road environment: pedestrian path, entrance/exit, pedestrian crossing and sidewalk.
TENCON 2012 IEEE Region 10 Conference, 2012
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012
ABSTRACT We present an autonomous vehicle providing mobility-on-demand service in a crowded urban... more ABSTRACT We present an autonomous vehicle providing mobility-on-demand service in a crowded urban environment. The focus in developing the vehicle has been to attain autonomous driving with minimal sensing and low cost, off-the-shelf sensors to ensure the system's economic viability. The autonomous vehicle has successfully completed over 50 km handling numerous mobility requests during the course of multiple demonstrations. The video provides an overview of our approach, with special comments on our localization and perception modules showcasing one such request being serviced.
2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS), 2011
2017 IEEE International Conference on Robotics and Automation (ICRA)
Journal of Field Robotics
Frontiers in Robotics and AI
Firm foot contact is the top priority of climbing robots to avoid catastrophic events, especially... more Firm foot contact is the top priority of climbing robots to avoid catastrophic events, especially when working at height. This study proposes a robust planning and control framework for climbing robots that provides robustness to slippage in unknown environments. The framework includes 1) a center of mass (CoM) trajectory optimization under the estimated contact condition, 2) Kalman filter–like approach for uncertain environment parameter estimation and subsequent CoM trajectory re-planing, and 3) an online weight adaptation approach for whole-body control (WBC) framework that can adjust the ground reaction force (GRF) distribution in real time. Though the friction and adhesion characteristics are often assumed to be known, the presence of several factors that lead to a reduction in adhesion may cause critical problems for climbing robots. To address this issue safely and effectively, this study suggests estimating unknown contact parameters in real time and using the evaluated cont...
IEEE Transactions on Intelligent Transportation Systems, 2022
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020
Understanding the kinematics of a ground robot is essential for efficient navigation. Based on th... more Understanding the kinematics of a ground robot is essential for efficient navigation. Based on the kinematic model of a robot, its full motion capabilities can be represented by theoretical motion primitives. However, depending on the environment and/or human preferences, not all of those theoretical motion primitives are desirable and/or achievable. This work presents a method to identify effective motion primitives (eMP) from continuous trajectories for autonomous ground robots. The pipeline efficiently performs segmentation, representation and reconstruction of the motion primitives, using initial human-driving behaviour as a guide to create a motion primitive library. Hence, this strategy incorporates how the environment affects the robot operation regarding accelerations, speed, braking, and steering behaviours.The method is thoroughly tested on an autonomous car-like electric vehicle, and the results show excellent generalisation of the theoretical motion primitive distribution to real vehicle. The experiments are carried out on large site with very diverse characteristics, illustrating the applicability of the method.
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020
Directionality in path planning is essential for efficient autonomous navigation in a number of r... more Directionality in path planning is essential for efficient autonomous navigation in a number of real-world environments. In many map-based navigation scenarios, the viable path from a given point A to point B is not the same as the viable path from B to A. We present a method that automatically incorporates preferred navigation directionality into a path planning costmap. This ‘preference’ is represented by coloured paths in the costmap. The colourisation is obtained based on an analysis of the driving trajectory generated by the robot as it navigates through the environment. Hence, our method augments this driving trajectory by intelligently colouring it according to the orientation of the robot during the run. Creating an analogy between the vehicle orientation angle and the hue angle in the Hue-Saturation-Value colour space, the method uses the hue, saturation and value components to encode the direction, directionality and scalar cost, respectively, into a costmap image. We describe a costing function to be used by the A* algorithm to incorporate this information to plan direction-aware vehicle paths. Our experiments with LiDAR-based localisation and autonomous driving in real environments illustrate the applicability of the method.
2019 International Conference on Robotics and Automation (ICRA), 2019
Springer Proceedings in Advanced Robotics, 2020
This paper introduces the N-ey[any] Wheel system, a set of modular wheels that enable bespoke pla... more This paper introduces the N-ey[any] Wheel system, a set of modular wheels that enable bespoke platform development for rapid field deployments. The N-ey Wheel is inuenced by existing modular and inspection robots but provides a simple-to-operate solution to exploring various environments. Each wheel provides two degrees of freedom allowing any continuous orientation to be achieved within a plane. Onboard computing and a wificonnection enables the N-ey Wheels to operate individually or collaboratively. Heterogeneous robot platforms can be created as required through the use of adaptors. With robots of differing shapes, sizes and configurations able to be created at run time as demonstrated within the laboratory and in the field. The dynamic nature of the system model dictates the control characteristics providing differential, Ackerman and nonholonomic omnidirectional control options to the user.
Motion planning under uncertainty is critical for robust autonomy. The Partially Observable Marko... more Motion planning under uncertainty is critical for robust autonomy. The Partially Observable Markov Decision Process (POMDP) is a principled and general framework for solving such problems. Although solving a POMDP problem exactly is computationally intractable, in the past decade, many practical methods have been proposed to approximate solution of POMDPs that represent motion planning under uncertainty problems. However, the problem remains relatively open when it involves robots with complex non-linear dynamics. Recently, linearization-based methods that are derived from the Linear Quadratic Gaussian (LQG) controller have been shown to perform well in some planning under uncertainty problems with non-linear robot dynamics. However, it is not clear what the effect of linearization to motion planning under uncertainty is. The control and estimation results have clearly indicated that linearization performs well only when the non-linear dynamics is “weak”. These results will definite...
Advances in Astronautics Science and Technology, 2020
Robotics: Science and Systems XVII, 2021
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018
Experimental Robotics, 2015
2014 IEEE International Conference on Robotics and Automation (ICRA), 2014
This paper proposes a semantic mapping method based on pedestrian activity in the urban road envi... more This paper proposes a semantic mapping method based on pedestrian activity in the urban road environment. Pedestrian activity patterns are learned from pedestrian tracks collected by a mobile platform. With the learned knowledge of pedestrian activity, semantic mapping is performed using Bayesian classification techniques. The proposed method is tested in real experiments, and shows promising results in recognizing four activity-related semantic properties of the urban road environment: pedestrian path, entrance/exit, pedestrian crossing and sidewalk.
TENCON 2012 IEEE Region 10 Conference, 2012
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012
ABSTRACT We present an autonomous vehicle providing mobility-on-demand service in a crowded urban... more ABSTRACT We present an autonomous vehicle providing mobility-on-demand service in a crowded urban environment. The focus in developing the vehicle has been to attain autonomous driving with minimal sensing and low cost, off-the-shelf sensors to ensure the system's economic viability. The autonomous vehicle has successfully completed over 50 km handling numerous mobility requests during the course of multiple demonstrations. The video provides an overview of our approach, with special comments on our localization and perception modules showcasing one such request being serviced.
2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS), 2011
2017 IEEE International Conference on Robotics and Automation (ICRA)
Journal of Field Robotics