Integrated planning and control for graceful navigation of shape-accelerated underactuated balancing mobile robots

Nagarajan, Umashankar; Kantor, George; Hollis, R.L.

doi:10.1109/icra.2012.6224970

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…As such, the task of fetching the mail is then interpreted as a sequential composition of controllers: walk to the mail room, grasp the mail, walk up the stairs, and finally drop the mail on the office. The simplicity of the idea has been explored in a number of robotics applications [3]- [6]. Consider a dynamical systeṁ…”

Section: Sequential Compositionmentioning

confidence: 99%

“…By composing each continuous-time controller such that the goal set of one controller lies in the domain of attraction of another controller, results in rich behaviors. Sequential composition has been successfully implemented in several robotic systems [3]- [6] and further enhanced in [7]- [9]. From a robotics point of view, sequential composition offers a very natural framework for control design since it decomposes complex tasks into smaller problems, each solved in a traditional control systems manner, taking advantage of all the available tools (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Reinforcement learning for sequential composition control

Najafi

Lopes

Babuška

2013

52nd IEEE Conference on Decision and Control

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Sequential composition is an effective strategy for addressing complex control specifications and complex dynamical systems by partitioning the problem in time and space. Traditionally, sequential composition controllers are synthesized offline given a control task and a static environment with possible constraints. Dynamical environments may require redesigning the entire sequential composition controller, which may be time costly and inefficient. In this paper we introduce a learning strategy to augment online a pre-designed sequential composition controller based on reinforcement learning. By interpreting the sequential composition controller as an automaton, we add and delete nodes in the graph online, based on newly acquired knowledge via learning. We present simulation and experimental results for a nonlinear motion-control system.

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Section: Sequential Compositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reinforcement learning for sequential composition control

Najafi

Lopes

Babuška

2013

52nd IEEE Conference on Decision and Control

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“…This control architecture of using an inner-loop balancing controller that stabilizes the shape dynamics and an outer-loop position tracking controller that achieves desired position space motions has been experimentally verified to be robust as shown in (Nagarajan et al 2009a,b,c). The ability of this control architecture to successfully track desired motions in position space has also been experimentally verified on the ballbot as shown in (Nagarajan et al 2012a In general, the time-varying domain D i (t) of the feedback control law φ i (t) is defined to be the largest invariant domain along the feasible state trajectory σ i (t) in the state space of the system. Lyapunov functions can be used to determine these invariant domains, and recent advancements in algebraic verification tools like sumsof-squares (SOS) tools (Tedrake et al 2010;Tobenkin et al 2011) make such computations a possibility.…”

Section: B Motion Policiesmentioning

confidence: 99%

“…A motion planner plans in the space of these gracefully composable collision-free motion policies to achieve desired navigation tasks. This paper is an extended and significantly improved version of the paper presented in (Nagarajan et al 2012a). …”

Section: E Approach Towards Graceful Navigationmentioning

confidence: 99%

Integrated motion planning and control for graceful balancing mobile robots

Nagarajan

Kantor

Hollis

2013

The International Journal of Robotics Research

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Abstract-This paper presents an integrated motion planning and control framework that enables balancing mobile robots to gracefully navigate human environments. A palette of controllers called motion policies is designed such that balancing mobile robots can achieve fast, graceful motions in small, collision-free domains of the position space. The domains determine the validity of a motion policy at any point in the robot's position state space. An automatic instantiation procedure that generates a motion policy library by deploying motion policies from a palette on a map of the environment is presented. A gracefully prepares relationship that guarantees valid compositions of motion policies to produce overall graceful motion is introduced. A directed graph called the gracefully prepares graph is used to represent all valid compositions of motion policies in the motion policy library. The navigation tasks are achieved by planning in the space of these gracefully composable motion policies. In this work, Dijsktra's algorithm is used to generate a single-goal optimal motion policy tree, and its variant is used to rapidly replan the optimal motion policy tree in the presence of dynamic obstacles. A hybrid controller is used as a supervisory controller to ensure successful execution of motion policies and also successful switching between them.The integrated motion planning and control framework presented in this paper was experimentally tested on the ballbot, a human-sized dynamically stable mobile robot that balances on a single ball. The results of successful experimental testing of two navigation tasks, namely, pointpoint and surveillance motions are presented. Additional experimental results that validate the framework's capability to handle disturbances and rapidly replan in the presence of dynamic obstacles are also presented.

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“…The control of the Segway, wheeled inverted pendulums and ballbots, which are all similar platforms, has been investigated in the literature (Grasser et al, 2002;Nagarajan et al, 2012). It has been shown that although the Segway is a nonlinear underactuated system, it can be controlled by linear controllers (Pathak et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Balancing a Legged Robot Using State-Dependent Riccati Equation Control

Najafi

Lopes

Babuška

2014

IFAC Proceedings Volumes

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In this paper, we propose a nonlinear control approach for balancing underactuated legged robots. For the balancing task, the robot is modeled as a generalized version of a Segway. The control design is based on the State-Dependent Riccati Equation (SDRE) approach. The domain of attraction of the SDRE controller is compared to the domain of attraction of a linear quadratic controller. Using a simulation example of a four-legged robot balancing on its hind legs, we show that the SDRE controller gives a reasonably large domain of attraction, even with realistic level constraints on the control input, while the linear quadratic controller is unable to stabilize the system.

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Integrated planning and control for graceful navigation of shape-accelerated underactuated balancing mobile robots

Cited by 10 publications

References 18 publications

Reinforcement learning for sequential composition control

Reinforcement learning for sequential composition control

Integrated motion planning and control for graceful balancing mobile robots

Balancing a Legged Robot Using State-Dependent Riccati Equation Control

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