Continuous Alternation: The Complexity of Pursuit in Continuous Domains

Complexity theory has used a game-theoretic notion, namely alternation, to great advantage in modeling parallelism and in obtaining lower bounds. The usual definition of alternation requires that transitions be made in discrete steps. The study of differential games is a classic area of optimal control, where there is continuous interaction and alternation between the players. Differential games capture many aspects of control theory and optimal control over continuous domains. In this paper, we define a generalization of the notion of alternation which applies to differential games, and which we call "continuous alternation." This approach allows us to obtain the first known complexity-theoretic results for open problems in differential games and optimal control. We concentrate our investigation on an important class of differential games, which we call polyhedral pursuit games. Pursuit games have application to many fundamental problems in autonomous robot control in the presence of an adversary. For example, this problem occurs in manufacturing environments with a single robot moving among a number of autonomous robots with unknown control programs, as well as in automatic automobile control, and collision control among aircraft and boats with unknown or adversary control. We show that in a three-dimensional pursuit game where each player's velocity is bounded (but there is no bound on acceleration), the pursuit game decision problem is hard for exponential time. This lower bound is somewhat surprising due to the sparse nature of the problem: there are only two moving objects (the players), each with only three degrees of freedom. It is also the first provably intractable result for any robotic problem with complete information; previous intractability results have relied on complexity-theoretic assumptions. Fortunately, we can counter our somewhat pessimistic lower bounds with polynomial time upper bounds for obtaining approximate solutions. In particular, we give polynomial time algorithms that approximately solve a very large class of pursuit games with arbitrarily small error. For e > 0, this algorithm finds a winning strategy for the evader provided that there is a winning strategy that always stays at least E distance from the pursuer and all obstacles. If the obstacles are described with n bits, then the algorithm runs in time (n/e)0(1), and applies to several types of pursuit games: either velocity or both acceleration and velocity may be bounded, and the bound may be of either the L2- or L&infin-norm. Our algorithms also generalize to when the obstacles have constant degree algebraic descriptions, and are allowed to have predictable movement

Provably good approximation algorithms for optimal kinodynamic planning for cartesian robots and open chain manipulators

shortest path, kinodynamics, polyhedral obstacles Abstract: We consider the following problem: given a robot system, nd a minimal-time trajectory that goes from a start state to a goal state while avoiding obstacles by a speed-dependent safety-margin and respecting dynamics bounds. In [1] we developed a provably good approximation algorithm for the minimum-time trajectory problem for a robot system with decoupled dynamics bounds (e.g., a point robot in R 3). This algorithm di ers from previous work in three ways. It is possible (1) to bound the goodness of the approximation by an error term �(2) to polynomially bound the computational complexity of our algorithm � and (3) to express the complexity as a polynomial function of the error term. Hence, given the geometric obstacles, dynamics bounds, and the error term, the algorithm returns a solution that is-close to optimal and requires only a polynomial (in ( 1)) amount of time. We extend the results of [1] in two ways. First, we modifyittohalve the exponent inthe polynomial bounds from 6d to 3d, so that that the new algorithm is O c d N 1 3d, where N is the geometric complexity of the obstacles and c is a robot-dependent constant. Second, the new algorithm nds a trajectory that matches the optimal in time with an factor sacri ced in the obstacle-avoidance safety margin. Similar results hold for polyhedral Cartesian manipulators in polyhedral environments. The new results indicate that an implementation of the algorithm could be reasonable, and a preliminary implementation has been done for the planar case

Provably Good Approximation Algorithms for Optimal Kinodynamic Planning for Cartesian Robots and Open Chain Manipulators

We consider the following problem: given a robot system, find a minimal-time trajectory from a start state to a goal state, while avoiding obstacles by a speed-dependent safety-margin and respecting dynamics bounds. In [CDRX] we developed a theoretical, provably good approximation algorithm for the minimum-time trajectory problem for a robot system with decoupled dynamics bounds (e.g. a point robot in). This algorithm differs from previous work in three ways. It is possible (1) to bound the goodness of the approximation by an error term $\epsilon$; (2) to polynomially bound the computational complexity of our algorithm; and (3) to express the complexity as a polynomial function of the error term. Hence, given the geometric obstacles, dynamics bounds, and the error term $\epsilon$, the algorithm returns a solution that is $\epsilon$-close to optimal and requires only a polynomial (in ($\frac{1}{\epsilon}$)) amount of time. We extend the results of [CDRX] in two ways. First, we reanalyze the [CDRX] algorithm for robots with decoupled dynamics bounds. We halve the exponent in the polynomial bounds and prove a better approximation accuracy. These new results indicate that an implementation of the theoretical algorithm could be reasonable. We report on a preliminary implementation of the extended algorithm and experiments. Second, we extend [CDRX] to $d$-link, revolute-joint 3D robots will full rigid body dynamics. Specifically, we first prove a generalized trajectory- tracking lemma for robots with coupled dynamics bounds. Then, using this result we describe polynomial-time approximation algorithms for Cartesian robots obeying $L_{2}$ dynamics bounds and open kinematic chain manipulators with revolute and prismatic joints; the latter class includes most industrial manipulators. We obtain a general $O(n^{2}$(log$n$) ($\frac{1}{\epsilon})^{6d-1})$ algorithm, where $n$ is the geometric complexity

Metastable legged-robot locomotion

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 195-215).A variety of impressive approaches to legged locomotion exist; however, the science of legged robotics is still far from demonstrating a solution which performs with a level of flexibility, reliability and careful foot placement that would enable practical locomotion on the variety of rough and intermittent terrain humans negotiate with ease on a regular basis. In this thesis, we strive toward this particular goal by developing a methodology for designing control algorithms for moving a legged robot across such terrain in a qualitatively satisfying manner, without falling down very often. We feel the definition of a meaningful metric for legged locomotion is a useful goal in and of itself. Specifically, the mean first-passage time (MFPT), also called the mean time to failure (MTTF), is an intuitively practical cost function to optimize for a legged robot, and we present the reader with a systematic, mathematical process for obtaining estimates of this MFPT metric. Of particular significance, our models of walking on stochastically rough terrain generally result in dynamics with a fast mixing time, where initial conditions are largely "forgotten" within 1 to 3 steps. Additionally, we can often find a near-optimal solution for motion planning using only a short time-horizon look-ahead. Although we openly recognize that there are important classes of optimization problems for which long-term planning is required to avoid "running into a dead end" (or off of a cliff!), we demonstrate that many classes of rough terrain can in fact be successfully negotiated with a surprisingly high level of long-term reliability by selecting the short-sighted motion with the greatest probability of success. The methods used throughout have direct relevance to machine learning, providing a physics-based approach to reduce state space dimensionality and mathematical tools to obtain a scalar metric quantifying performance of the resulting reduced-order system.by Katie Byl.Ph.D