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Ramkumar Natarajan

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5 papers

1 author row

IROS Conference 2024 Conference Paper

PINSAT: Parallelized Interleaving of Graph Search and Trajectory Optimization for Kinodynamic Motion Planning

Ramkumar Natarajan
Shohin Mukherjee
Howie Choset
Maxim Likhachev

Trajectory optimization is a widely used technique in robot motion planning for letting the dynamics of the system shape and synthesize complex behaviors. Several previous works have shown its benefits in high-dimensional continuous state spaces and under differential constraints. However, long time horizons and planning around obstacles in non-convex spaces pose challenges in guaranteeing convergence or finding optimal solutions. As a result, discrete graph search planners and sampling-based planers are preferred when facing obstacle-cluttered environments. A recently developed algorithm called INSAT effectively combines graph search in the low-dimensional subspace and trajectory optimization in the full-dimensional space for global kinodynamic planning over long horizons. Although INSAT successfully reasoned about and solved complex planning problems, the numerous expensive calls to an optimizer resulted in large planning times, thereby limiting its practical use. Inspired by the recent work on edge-based parallel graph search, we present PINSAT, which introduces systematic parallelization in INSAT to achieve lower planning times and higher success rates, while maintaining significantly lower costs over relevant baselines. We demonstrate PINSAT by evaluating it on 6 DoF kinodynamic manipulation planning with obstacles. We demonstrate PINSAT by evaluating it on two kinodynamic manipulation planning scenarios: (i) a single ball blocking task among obstacles using a 6 DoF ABB arm, and (ii) a multi-ball blocking task where the balls are separated by short time intervals using a 7 DoF KUKA LBR iiwa arm with obstacles.

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ICRA Conference 2024 Conference Paper

Preprocessing-based Kinodynamic Motion Planning Framework for Intercepting Projectiles using a Robot Manipulator

Ramkumar Natarajan
Hanlan Yang
Qintong Xie
Yash Oza
Manash Pratim Das
Fahad Islam 0002
Muhammad Suhail Saleem
Howie Choset

We are interested in studying sports with robots and starting with the problem of intercepting a projectile moving toward a robot manipulator equipped with a shield. To successfully perform this task, the robot needs to (i) detect the incoming projectile, (ii) predict the projectile’s future motion, (iii) plan a minimum-time rapid trajectory that can evade obstacles and intercept the projectile, and (iv) execute the planned trajectory. These four steps must be performed under the manipulator’s dynamic limits and extreme time constraints (≤ 350ms in our setting) to successfully intercept the projectile. In addition, we want these trajectories to be smooth to reduce the robot’s joint torques and the impulse on the platform on which it is mounted. To this end, we propose a kinodynamic motion planning framework that preprocesses smooth trajectories offline to allow real-time collision-free executions online. We present an end-to-end pipeline along with our planning framework, including perception, prediction, and execution modules. We evaluate our framework experimentally in simulation and show that it has a higher blocking success rate than the baselines. Further, we deploy our pipeline on a robotic system comprising an industrial arm (ABB IRB-1600) and an onboard stereo camera (ZED 2i), which achieves a 78% success rate in projectile interceptions.

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ICRA Conference 2023 Conference Paper

Torque-Limited Manipulation Planning through Contact by Interleaving Graph Search and Trajectory Optimization

Ramkumar Natarajan
Garrison L. H. Johnston
Nabil Simaan
Maxim Likhachev
Howie Choset

Robots often have to perform manipulation tasks in close proximity to people (Fig 1). As such, it is desirable to use a robot arm that has limited joint torques so as to not injure the nearby person. Unfortunately, these limited torques then limit the payload capability of the arm. By using contact with the environment, robots can expand their reachable workspace that, otherwise, would be inaccessible due to exceeding actuator torque limits. We adapt our recently developed INSAT algorithm [1] to tackle the problem of torque-limited whole arm manipulation planning through contact. INSAT requires no prior over contact mode sequence and no initial template or seed for trajectory optimization. INSAT achieves this by interleaving graph search to explore the manipulator joint configuration space with incremental trajectory optimizations seeded by neighborhood solutions to find a dynamically feasible trajectory through contact. We demonstrate our results on a variety of manipulators and scenarios in simulation. We also experimentally show our planner exploiting robot-environment contact for the pick and place of a payload using a Kinova Gen3 robot. In comparison to the same trajectory running in free space, we experimentally show that the utilization of bracing contacts reduces the overall torque required to execute the trajectory.

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SoCS Conference 2019 Conference Paper

A-MHA*: Anytime Multi-Heuristic A

Ramkumar Natarajan
Muhammad Suhail Saleem
Sandip Aine
Maxim Likhachev
Howie Choset

Designing good heuristic functions for graph search requires adequate domain knowledge. It is often easy to design heuristics that perform well and correlate with the underlying true cost-to-go values in certain parts of the search space but these may not be admissible throughout the domain thereby affecting the optimality guarantees of the search. Bounded suboptimal search using several of such partially good but inadmissible heuristics was developed in Multi-Heuristic A* (MHA*). Although MHA* leverages multiple inadmissible heuristics to potentially generate a faster suboptimal solution, the original version does not improve the solution over time. It is an one shot algorithm that requires careful setting of inflation factors to obtain a desired one time solution. In this work, we tackle this issue by extending MHA* to an anytime version that finds a feasible suboptimal solution quickly and continually improve it until time runs out. Our work is inspired from the Anytime Repairing A* (ARA*) algorithm. We prove that our precise adaptation of ARA* concepts in the MHA* framework preserves the original suboptimal and completeness guarantees and enhances MHA* to perform in an anytime fashion. Furthermore, we report the performance of A-MHA* in 3-D path planning domain and sliding tiles puzzle and compare against MHA* and other anytime algorithms.

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IROS Conference 2017 Conference Paper

Towards planning and control of hybrid systems with limit cycle using LQR trees

Siddharthan Rajasekaran
Ramkumar Natarajan
Jonathan D. Taylor

We present a multi-query recovery policy for a hybrid system with goal limit cycle. The sample trajectories and the hybrid limit cycle of the dynamical system are stabilized using locally valid Time Varying LQR controller policies which probabilistically cover a bounded region of state space. The original LQR Tree algorithm builds such trees for non-linear static and non-hybrid systems like a pendulum or a cart-pole. We leverage the idea of LQR trees to plan with a continuous control set, unlike methods that rely on discretization like dynamic programming to plan for hybrid dynamical systems where it is hard to capture the exact event of discrete transition. We test the algorithm on a compass gait model by stabilizing a dynamic walking hybrid limit cycle with point foot contact from random initial conditions. We show results from the simulation where the system comes back to a stable behavior with initial position or velocity perturbation and noise.

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