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Differentially Constrained Mobile Robot Motion Planning in State Lattices
, 2008
"... We present an approach to the problem of differentially constrained mobile robot motion planning in arbitrary cost fields. The approach is based on deterministic search in a specially discretized state space. We compute a set of elementary motions that connects each discrete state value to a set of ..."
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Cited by 39 (4 self)
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We present an approach to the problem of differentially constrained mobile robot motion planning in arbitrary cost fields. The approach is based on deterministic search in a specially discretized state space. We compute a set of elementary motions that connects each discrete state value to a set of its reachable neighbors via feasible motions. Thus, this set of motions induces a connected search graph. The motions are carefully designed to terminate at discrete states, whose dimensions include relevant state variables (e.g., position, heading, curvature, and velocity). The discrete states, and thus the motions, repeat at regular intervals, forming a lattice. We ensure that all paths in the graph encode feasible motions via the imposition of continuity constraints on state variables at graph vertices and compliance of the graph edges with a differential equation comprising the vehicle model. The resulting state lattice permits fast full configuration space cost evaluation and collision detection. Experimental results with research prototype rovers demonstrate that the planner allows us to exploit the entire envelope of vehicle maneuverability in rough terrain, while featuring real-time performance. C ○ 2009 Wiley Periodicals, Inc. 1.
Empirical sampling of path sets for local area motion planning
- IN INTL. SYMPOSIUM OF EXPERIMENTAL ROBOTICS
, 2008
"... Summary. We consider the problem of online planning for a mobile robot among obstacles, where it is impractical to test all possible future paths. One approach is for the runtime task to test some subset of the possible paths and select a path that does not collide with obstacles while advancing the ..."
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Cited by 13 (4 self)
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Summary. We consider the problem of online planning for a mobile robot among obstacles, where it is impractical to test all possible future paths. One approach is for the runtime task to test some subset of the possible paths and select a path that does not collide with obstacles while advancing the robot toward its goal. Performance depends on the choice of path set. In this paper we assume the path set is fixed and chosen offline. By randomly sampling the space of path sets we discover effective path sets—comparable or superior to the best previously suggested approaches. In addition, testing large numbers of randomly generated path sets yields some insights on the relation of robot performance to the choice of path set. 1
Path diversity is only part of the problem
- IN PROC. IEEE INTL. CONF. ON ROBOTICS AND AUTOMATION, KOBE
, 2009
"... Abstract — The goal of motion planning is to find a feasible path that connects two positions and is free from collision with obstacles. Path sets are a robust approach to this problem in the face of real-world complexity and uncertainty. A path set is a collection of feasible paths and their corres ..."
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Cited by 13 (5 self)
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Abstract — The goal of motion planning is to find a feasible path that connects two positions and is free from collision with obstacles. Path sets are a robust approach to this problem in the face of real-world complexity and uncertainty. A path set is a collection of feasible paths and their corresponding control sequences. A path-set-based planner navigates by repeatedly testing each of these robot-fixed paths for collision with obstacles. A heuristic function selects which of the surviving paths to follow next. At each step, the robot follows a small piece of each path selected while simultaneously planning the subsequent trajectory. A path set possesses high path diversity if it performs well at obstacle-avoidance and goal-seeking behaviors. Previous work in path diversity has tacitly assumed that a correlation exists between this dynamic planning problem and a simpler, static path diversity problem: a robot placed randomly into an obstacle field evaluates its path set for collision a single time before following the chosen path in entirety. Although these problems might intuitively appear to be linked, this paper shows that static and dynamic path diversity are two distinct properties. After empirically demonstrating this fact, we discuss some of the factors that differentiate the two problems. A. Path Sets I.
Adaptive model-predictive motion planning for navigation in complex environments
, 2009
"... Outdoor mobile robot motion planning and navigation is a challenging problem in artificial intelligence. The search space density and dimensionality, system dynamics and environmental interaction complexity, and the perceptual horizon limitation all contribute to the difficultly of this problem. It ..."
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Cited by 11 (0 self)
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Outdoor mobile robot motion planning and navigation is a challenging problem in artificial intelligence. The search space density and dimensionality, system dynamics and environmental interaction complexity, and the perceptual horizon limitation all contribute to the difficultly of this problem. It is hard to generate a motion plan between arbitrary boundary states that considers sophisticated vehicle dynamics and all feasible actions for nontrivial mobile robot systems. Accomplishing these goals in real time is even more challenging because of dynamic environments and updating perception information. This thesis develops effective search spaces for mobile robot trajectory generation, motion planning, and navigation in complex environments. Complex environments are defined as worlds where locally optimal motion plans are numerous and where the sensitivity of the cost function is highly dependent on state and motion model fidelity. Examples include domains where obstacles are prevalent, terrain shape is varied, and the consideration of terramechanical effects is important. Three specific contributions are accomplished. First, a model-predictive trajectory generation technique is developed that numerically linearizes and inverts general predictive motion models to determine parameterized actions that satisfy the two-point boundary value problem. Applications on a number of mobile robot platforms (including skidsteered field robots, planetary rovers with actively articulating chassis, mobile manipulators, and autonomous automobiles)
Receding Horizon Model-Predictive Control for Mobile Robot Navigation of Intricate Paths
"... Abstract As mobile robots venture into more complex environments, more arbitrary feasible state-space trajectories and paths are required to move safely and efficiently. The capacity to effectively navigate such paths in the face of disturbances and changes in mobility can mean the difference betwee ..."
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Cited by 6 (1 self)
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Abstract As mobile robots venture into more complex environments, more arbitrary feasible state-space trajectories and paths are required to move safely and efficiently. The capacity to effectively navigate such paths in the face of disturbances and changes in mobility can mean the difference between mission failure and success. This paper describes a technique for model predictive control of a mobile robot that utilizes the structure of a regional motion plan to effectively search the local continuum for an improved solution. The contribution, the receding horizon model-predictive control algorithm, specifically addresses the problem of path following and obstacle avoidance through cusps, turn-in-place, and multi-point turn maneuvers in environments where terrain shape and vehicle mobility effects are non-negligible. The technique is formulated as an optimal controller that utilizes a model-predictive trajectory generator to determine parameterized control inputs that navigate general mobile robots safely through the environment. Experimental results are presented for a a six-wheeled skid-steered field robot in natural terrain. 1
Field Experiments in Rover Navigation via Model-Based Trajectory Generation and Nonholonomic Motion Planning in State Lattices
"... This paper presents field experiments of two novel approaches to local and regional motion planning applied to planetary rover navigation. The first approach solves the two-point boundary value problem using a model-based trajectory optimization technique that inverts an arbitrary dynamics model to ..."
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Cited by 3 (2 self)
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This paper presents field experiments of two novel approaches to local and regional motion planning applied to planetary rover navigation. The first approach solves the two-point boundary value problem using a model-based trajectory optimization technique that inverts an arbitrary dynamics model to generate a feasible motion plan. The second approach utilizes this result to build a special discretization of the state space that allows employing standard search algorithms for solving the motion planning problem. These approaches enable robot autonomy by considering the robot’s dynamics, efficiently searching a finely discretized state space, and allowing the reuse of previous planning computation to improve runtime. We present results from the experiments on the Rocky 8 and FIDO planetary rover prototypes in the NASA/JPL Mars Yard. 1.
Motion Planning for Car-like Robots DIPLOMARBEIT zur Erlangung des akademischen Grades Diplom-Ingenieur
"... ”Hiermit erkläre ich, dass ich diese Arbeit selbstständig verfasst habe, dass ich die verwendeten ..."
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”Hiermit erkläre ich, dass ich diese Arbeit selbstständig verfasst habe, dass ich die verwendeten
Mobile Robot Navigation of Intricate Paths
"... Abstract As mobile robots venture into more difficult environments, more complex state-space paths are required to move safely and efficiently. The difference between mission success and failure can be determined by a mobile robot’s capacity to effectively navigate such paths in the presence of dist ..."
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Abstract As mobile robots venture into more difficult environments, more complex state-space paths are required to move safely and efficiently. The difference between mission success and failure can be determined by a mobile robot’s capacity to effectively navigate such paths in the presence of disturbances. This paper describes a technique for mobile robot model predictive control that utilizes the structure of a regional motion plan to effectively search the local continuum for an improved solution. The contribution, the receding horizon model-predictive control (RHMPC) technique, specifically addresses the problem of path following and obstacle avoidance through geometric singularities and discontinuities such as cusps, turn-in-place, and multi-point turn maneuvers in environments where terrain shape and vehicle mobility effects are non-negligible. The technique is formulated as an optimal controller that utilizes a model-predictive trajectory generator to relax parameterized control inputs initialized from a regional motion planner to navigate safely through the environment. Experimental results are presented for a six-wheeled skid-steered field robot in natural terrain. 1
unknown title
"... the Green-Kelly approximate-area metric; (c) path set generated with the Hausdorff metric; (d) path set generated using a mutual-collision metric, which estimates the probability of two paths colliding with the same obstacle. static case, we place a stationary robot arbitrarily in a space cluttered ..."
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the Green-Kelly approximate-area metric; (c) path set generated with the Hausdorff metric; (d) path set generated using a mutual-collision metric, which estimates the probability of two paths colliding with the same obstacle. static case, we place a stationary robot arbitrarily in a space cluttered with obstacles and test whether at least one of the paths in the path set is collision-free. In this simple paradigm, a single planning step precedes execution of the full plan. This formulation of the static paradigm is designed to obey the assumptions made by many other works on path diversity [9], [3], [6]. In the dynamic case, the robot simultaneously drives the beginning of each path plan while computing the subsequent replan step. In most implementations of this planning ap-proach, new plans, which look ahead five seconds or more,
