16 research outputs found

    Ingenuity Mars Helicopter

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    Experiences with the JPL telerobot testbed: Issues and insights

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    The Jet Propulsion Laboratory's (JPL) Telerobot Testbed is an integrated robotic testbed used to develop, implement, and evaluate the performance of advanced concepts in autonomous, tele-autonomous, and tele-operated control of robotic manipulators. Using the Telerobot Testbed, researchers demonstrated several of the capabilities and technological advances in the control and integration of robotic systems which have been under development at JPL for several years. In particular, the Telerobot Testbed was recently employed to perform a near completely automated, end-to-end, satellite grapple and repair sequence. The task of integrating existing as well as new concepts in robot control into the Telerobot Testbed has been a very difficult and timely one. Now that researchers have completed the first major milestone (i.e., the end-to-end demonstration) it is important to reflect back upon experiences and to collect the knowledge that has been gained so that improvements can be made to the existing system. It is also believed that the experiences are of value to the others in the robotics community. Therefore, the primary objective here will be to use the Telerobot Testbed as a case study to identify real problems and technological gaps which exist in the areas of robotics and in particular systems integration. Such problems have surely hindered the development of what could be reasonably called an intelligent robot. In addition to identifying such problems, researchers briefly discuss what approaches have been taken to resolve them or, in several cases, to circumvent them until better approaches can be developed

    Combined EDL-Mobility Planning for Planetary Missions

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    This paper presents an analysis framework for planetary missions that have coupled mobility and EDL (Entry-Descent-Landing) systems. Traditional systems engineering approaches to mobility missions such as MERs (Mars Exploration Rovers) and MSL (Mars Science Laboratory) independently study the EDL system and the mobility system, and does not perform explicit trade-off between them or risk minimization of the overall system. A major challenge is that EDL operation is inherently uncertain and its analysis results such as landing footprint are described using PDF (Probability Density Function). The proposed approach first builds a mobility cost-to-go map that encodes the driving cost of any point on the map to a science target location. The cost could include variety of metrics such as traverse distance, time, wheel rotation on soft soil, and closeness to hazards. It then convolves the mobility cost-to-go map with the landing PDF given by the EDL system, which provides a histogram of driving cost, which can be used to evaluate the overall risk of the mission. By capturing the coupling between EDL and mobility explicitly, this analysis framework enables quantitative tradeoff between EDL and mobility system performance, as well as the characterization of risks in a statistical way. The simulation results are presented with a realistic Mars terrain dat

    Kinematic state estimation for a Mars rover

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    Joint Chance-Constrained Dynamic Programming

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    This paper presents a novel dynamic programming algorithm with a joint chance constraint, which explicitly bounds the risk of failure in order to maintain the state within a specified feasible region. A joint chance constraint cannot be handled by existing constrained dynamic programming approaches since their application is limited to constraints in the same form as the cost function, that is, an expectation over a sum of one-stage costs. We overcome this challenge by reformulating the joint chance constraint into a constraint on an expectation over a sum of indicator functions, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the primal variables can be optimized by a standard dynamic programming, while the dual variable is optimized by a root-finding algorithm that converges exponentially. Error bounds on the primal and dual objective values are rigorously derived. We demonstrate the algorithm on a path planning problem, as well as an optimal control problem for Mars entry, descent and landing. The simulations are conducted using a real terrain data of Mars, with four million discrete states at each time step

    A Risk-Constrained Multi-Stage Decision Making Approach to the Architectural Analysis of Mars Missions

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    This paper presents a novel risk-constrained multi-stage decision making approach to the architectural analysis of planetary rover missions. In particular, focusing on a 2018 Mars rover concept, which was considered as part of a potential Mars Sample Return campaign, we model the entry, descent, and landing (EDL) phase and the rover traverse phase as four sequential decision-making stages. The problem is to find a sequence of divert and driving maneuvers so that the rover drive is minimized and the probability of a mission failure (e.g., due to a failed landing) is below a user specified bound. By solving this problem for several different values of the model parameters (e.g., divert authority), this approach enables rigorous, accurate and systematic trade-offs for the EDL system vs. the mobility system, and, more in general, cross-domain trade-offs for the different phases of a space mission. The overall optimization problem can be seen as a chance-constrained dynamic programming problem, with the additional complexity that 1) in some stages the disturbances do not have any probabilistic characterization, and 2) the state space is extremely large (i.e, hundreds of millions of states for trade-offs with high-resolution Martian maps). To this purpose, we solve the problem by performing an unconventional combination of average and minimax cost analysis and by leveraging high efficient computation tools from the image processing community. Preliminary trade-off results are presented
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