Monthly Archives: November 2015

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Scheduling of communications between several nodes for better achieving real-time constraints in a distributed control system, and also a very detailed dynamical model of a wheeled vehicle

Naim Bajcinca, Wireless cars: A cyber-physical approach to vehicle dynamics control, Mechatronics, Volume 30, September 2015, Pages 261-274, ISSN 0957-4158, DOI: 10.1016/j.mechatronics.2015.04.016.

A non-conventional drive-by-wireless technology for guidance and control of a redundantly actuated electric car supported by an on-board wireless network of sensors, actuators and control units is proposed in this article. Several optimization-based distributed feedforward control schemes are developed for such powertrain infrastructures. In view of the limitations of the commercial off-the-shelf wireless communication technologies and the harshness of the in-vehicle environments, a pressing design and implementation aspect, in addition to the robustness against information loss, refers to fulfilling the hard real-time computational requirements. In this work, we address such problems by introducing several distributed event-based control schemes in conjunction with adaptive scheduling at the protocol level. Hereby we obtain a simple tuning mechanism to compromise between the outcome accuracy and computation efficiency (i.e., communication traffic intensity). Using simulative evaluations, we demonstrate the viability of the proposed algorithms and illustrate the impact of external interferences in an IEEE 802.15.4 based wireless communication solution.

Study of how a complex motion planning problem solved through RRT can benefit from parallelization

Brian W. Satzinger, Chelsea Lau, Marten Byl, Katie Byl, Tractable locomotion planning for RoboSimian, The International Journal of Robotics Research November 2015 vol. 34 no. 13 1541-1558, DOI: 10.1177/0278364915584947.

This paper investigates practical solutions for low-bandwidth, teleoperated mobility for RoboSimian in complex environments. Locomotion planning for this robot is challenging due to kinematic redundancy. We present an end-to-end planning method that exploits a reduced-dimension rapidly-exploring random tree search, constraining a subset of limbs to an inverse kinematics table. Then, we evaluate the performance of this approach through simulations in randomized environments and in the style of the Defense Advanced Research Projects Agency Robotics Challenges terrain both in simulation and with hardware.
We also illustrate the importance of allowing for significant body motion during swing leg motions on extreme terrain and quantify the trade-offs between computation time and execution time, subject to velocity and acceleration limits of the joints. These results lead us to hypothesize that appropriate statistical “investment” of parallel computing resources between competing formulations or flavors of random planning algorithms can improve motion planning performance significantly. Motivated by the need to improve the speed of limbed mobility for the Defense Advanced Research Projects Agency Robotics Challenge, we introduce one formulation of this resource allocation problem as a toy example and discuss advantages and implications of such trajectory planning for tractable locomotion on complex terrain.

Using MDPs when the transition probability matrix is just partially specified, therefore getting closer to a model-free approach

Karina V. Delgado, Leliane N. de Barros, Daniel B. Dias, Scott Sanner, Real-time dynamic programming for Markov decision processes with imprecise probabilities, Artificial Intelligence, Volume 230, January 2016, Pages 192-223, ISSN 0004-3702, DOI: 10.1016/j.artint.2015.09.005.

Markov Decision Processes have become the standard model for probabilistic planning. However, when applied to many practical problems, the estimates of transition probabilities are inaccurate. This may be due to conflicting elicitations from experts or insufficient state transition information. The Markov Decision Process with Imprecise Transition Probabilities (MDP-IPs) was introduced to obtain a robust policy where there is uncertainty in the transition. Although it has been proposed a symbolic dynamic programming algorithm for MDP-IPs (called SPUDD-IP) that can solve problems up to 22 state variables, in practice, solving MDP-IP problems is time-consuming. In this paper we propose efficient algorithms for a more general class of MDP-IPs, called Stochastic Shortest Path MDP-IPs (SSP MDP-IPs) that use initial state information to solve complex problems by focusing on reachable states. The (L)RTDP-IP algorithm, a (Labeled) Real Time Dynamic Programming algorithm for SSP MDP-IPs, is proposed together with three different methods for sampling the next state. It is shown here that the convergence of (L)RTDP-IP can be obtained by using any of these three methods, although the Bellman backups for this class of problems prescribe a minimax optimization. As far as we are aware, this is the first asynchronous algorithm for SSP MDP-IPs given in terms of a general set of probability constraints that requires non-linear optimization over imprecise probabilities in the Bellman backup. Our results show up to three orders of magnitude speedup for (L)RTDP-IP when compared with the SPUDD-IP algorithm.

See also:

  • Karina Valdivia Delgado, Scott Sanner, Leliane Nunes de Barros, Efficient solutions to factored MDPs with imprecise transition probabilities, Artif. Intell. 175 (9–10) (2011) 1498–1527.
  • Satia, J. K., and Lave Jr., R. E. 1970. MDPs with uncertain transition probabilities. Operations Research 21:728–740
  • White III, C. C., and El-Deib, H. K. 1994. MDPs with Imprecise Transition Probabilities. Operations Research 42(4):739–749