Showing papers on "Motion planning published in 2011"

STOMP: Stochastic trajectory optimization for motion planning

Abstract: We present a new approach to motion planning using a stochastic trajectory optimization framework. The approach relies on generating noisy trajectories to explore the space around an initial (possibly infeasible) trajectory, which are then combined to produced an updated trajectory with lower cost. A cost function based on a combination of obstacle and smoothness cost is optimized in each iteration. No gradient information is required for the particular optimization algorithm that we use and so general costs for which derivatives may not be available (e.g. costs corresponding to constraints and motor torques) can be included in the cost function. We demonstrate the approach both in simulation and on a mobile manipulation system for unconstrained and constrained tasks. We experimentally show that the stochastic nature of STOMP allows it to overcome local minima that gradient-based methods like CHOMP can get stuck in.

Rapidly-exploring Random Belief Trees for motion planning under uncertainty

Abstract: In this paper we address the problem of motion planning in the presence of state uncertainty, also known as planning in belief space. The work is motivated by planning domains involving nontrivial dynamics, spatially varying measurement properties, and obstacle constraints. To make the problem tractable, we restrict the motion plan to a nominal trajectory stabilized with a linear estimator and controller. This allows us to predict distributions over future states given a candidate nominal trajectory. Using these distributions to ensure a bounded probability of collision, the algorithm incrementally constructs a graph of trajectories through state space, while efficiently searching over candidate paths through the graph at each iteration. This process results in a search tree in belief space that provably converges to the optimal path. We analyze the algorithm theoretically and also provide simulation results demonstrating its utility for balancing information gathering to reduce uncertainty and finding low cost paths.

Hierarchical task and motion planning in the now

Abstract: In this paper we outline an approach to the integration of task planning and motion planning that has the following key properties: It is aggressively hierarchical; it makes choices and commits to them in a top-down fashion in an attempt to limit the length of plans that need to be constructed, and thereby exponentially decrease the amount of search required It operates on detailed, continuous geometric representations and does not require a-priori discretization of the state or action spaces

On the Complexity of Motion Planning for Multiple Independent Objects; Pspace Hardness of the Warehouseman's Problem

Abstract: Coordinated motion planning for a large number af three-di mensional objects in the presence of obstacles is a computa tional problem whose complexity is important to calibrate. In this paper we show that even the restricted two-dimensional problem for arbitrarily many rectangles in a rectangular region is PSPACE-hard. This result should be viewed as a guide to the difficulty, of the general problem and should lead researchers to consider more tractable restricted classes of motion problems of practical interest.

LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information

Abstract: In this paper we present LQG-MP (linear-quadratic Gaussian motion planning), a new approach to robot motion planning that takes into account the sensors and the controller that will be used during the execution of the robot’s path. LQG-MP is based on the linear-quadratic controller with Gaussian models of uncertainty, and explicitly characterizes in advance (i.e. before execution) the a priori probability distributions of the state of the robot along its path. These distributions can be used to assess the quality of the path, for instance by computing the probability of avoiding collisions. Many methods can be used to generate the required ensemble of candidate paths from which the best path is selected; in this paper we report results using rapidly exploring random trees (RRT). We study the performance of LQG-MP with simulation experiments in three scenarios: (A) a kinodynamic car-like robot, (B) multi-robot planning with differential-drive robots, and (C) a 6-DOF serial manipulator. We also present a method that applies Kalman smoothing to make paths Ck-continuous and apply LQG-MP to precomputed roadmaps using a variant of Dijkstra’s algorithm to efficiently find high-quality paths.

Motion planning for autonomous driving with a conformal spatiotemporal lattice

Abstract: We present a motion planner for autonomous highway driving that adapts the state lattice framework pioneered for planetary rover navigation to the structured environment of public roadways. The main contribution of this paper is a search space representation that allows the search algorithm to systematically and efficiently explore both spatial and temporal dimensions in real time. This allows the low-level trajectory planner to assume greater responsibility in planning to follow a leading vehicle, perform lane changes, and merge between other vehicles. We show that our algorithm can readily be accelerated on a GPU, and demonstrate it on an autonomous passenger vehicle.

Task Space Regions: A framework for pose-constrained manipulation planning

Abstract: We present a manipulation planning framework that allows robots to plan in the presence of constraints on end-effector pose, as well as other common constraints. The framework has three main components: constraint representation, constraint-satisfaction strategies, and a general planning algorithm. These components come together to create an efficient and probabilistically complete manipulation planning algorithm called the Constrained BiDirectional Rapidly-exploring Random Tree (RRT) - CBiRRT2. The underpinning of our framework for pose constraints is our Task Space Regions (TSRs) representation. TSRs are intuitive to specify, can be efficiently sampled, and the distance to a TSR can be evaluated very quickly, making them ideal for sampling-based planning. Most importantly, TSRs are a general representation of pose constraints that can fully describe many practical tasks. For more complex tasks, such as manipulating articulated objects, TSRs can be chained together to create more complex end-effector pose constraints. TSRs can also be intersected, a property that we use to plan with pose uncertainty. We provide a detailed description of our framework, prove probabilistic completeness for our planning approach, and describe several real-world example problems that illustrate the efficiency and versatility of the TSR framework.

Towards autonomous robotic butlers: Lessons learned with the PR2

Abstract: As autonomous personal robots come of age, we expect certain applications to be executed with a high degree of repeatability and robustness. In order to explore these applications and their challenges, we need tools and strategies that allow us to develop them rapidly. Serving drinks (i.e., locating, fetching, and delivering), is one such application with well-defined environments for operation, requirements for human interfacing, and metrics for successful completion. In this paper we present our experiences and results while building an autonomous robotic assistant using the PR21 platform and ROS2. The system integrates several new components that are built on top of the PR2's current capabilities. Perception components include dynamic obstacle identification, mechanisms for identifying the refrigerator, types of drinks, and human faces. Planning components include navigation, arm motion planning with goal and path constraints, and grasping modules. One of the main contributions of this paper is a new task-level executive system, SMACH, based on hierarchical concurrent state machines, which controls the overall behavior of the system. We provide in-depth discussions on the solutions that we found in accomplishing our goal, and the implementation strategies that let us achieve them.

SIPP: Safe interval path planning for dynamic environments

Abstract: Robotic path planning in static environments is a thoroughly studied problem that can typically be solved very efficiently. However, planning in the presence of dynamic obstacles is still computationally challenging because it requires adding time as an additional dimension to the search-space explored by the planner. In order to avoid the increase in the dimensionality of the planning problem, most real-time approaches to path planning treat dynamic obstacles as static and constantly re-plan as dynamic obstacles move. Although gaining efficiency, these approaches sacrifice optimality and even completeness. In this paper, we develop a planner that builds on the observation that while the number of safe timesteps in any configuration may be unbounded, the number of safe time intervals in a configuration is finite and generally very small. A safe interval is a time period for a configuration with no collisions and if it were extended one timestep in either direction, it would then be in collision. The planner exploits this observation and constructs a search-space with states defined by their configuration and safe interval, resulting in a graph that generally only has a few states per configuration. On the theoretical side, we show that our planner can provide the same optimality and completeness guarantees as planning with time as an additional dimension. On the experimental side, in simulation tests with up to 200 dynamic obstacles, we show that our planner is significantly faster, making it feasible to use in real-time on robots operating in large dynamic environments. We also ran several real robot trials on the PR2, a mobile manipulation platform.

Joint stiffness identification of six-revolute industrial serial robots

Abstract: Although robots tend to be as competitive as CNC machines for some operations, they are not yet widely used for machining operations. This may be due to the lack of certain technical information that is required for satisfactory machining operation. For instance, it is very difficult to get information about the stiffness of industrial robots from robot manufacturers. As a consequence, this paper introduces a robust and fast procedure that can be used to identify the joint stiffness values of any six-revolute serial robot. This procedure aims to evaluate joint stiffness values considering both translational and rotational displacements of the robot end-effector for a given applied wrench (force and torque). In this paper, the links of the robot are assumed to be much stiffer than its actuated joints. The robustness of the identification method and the sensitivity of the results to measurement errors and the number of experimental tests are also analyzed. Finally, the actual Cartesian stiffness matrix of the robot is obtained from the joint stiffness values and can be used for motion planning and to optimize machining operations.

Parallel Elite Genetic Algorithm and Its Application to Global Path Planning for Autonomous Robot Navigation

Abstract: This paper presents a parallel elite genetic algorithm (PEGA) and its application to global path planning for autonomous mobile robots navigating in structured environments. This PEGA, consisting of two parallel EGAs along with a migration operator, takes advantages of maintaining better population diversity, inhibiting premature convergence, and keeping parallelism in comparison with conventional GAs. This initial feasible path generated from the PEGA planner is then smoothed using the cubic B-spline technique, in order to construct a near-optimal collision-free continuous path. Both global path planner and smoother are implemented in one field-programmable gate array chip utilizing the system-on-a-programmable-chip technology and the pipelined hardware implementation scheme, thus significantly expediting computation speed. Simulations and experimental results are conducted to show the merit of the proposed PEGA path planner and smoother for global path planning of autonomous mobile robots.

Aerial remote sensing in agriculture: A practical approach to area coverage and path planning for fleets of mini aerial robots

Abstract: In this paper, a system that allows applying precision agriculture techniques is described. The application is based on the deployment of a team of unmanned aerial vehicles that are able to take georeferenced pictures in order to create a full map by applying mosaicking procedures for postprocessing. The main contribution of this work is practical experimentation with an integrated tool. Contributions in different fields are also reported. Among them is a new one-phase automatic task partitioning manager, which is based on negotiation among the aerial vehicles, considering their state and capabilities. Once the individual tasks are assigned, an optimal path planning algorithm is in charge of determining the best path for each vehicle to follow. Also, a robust flight control based on the use of a control law that improves the maneuverability of the quadrotors has been designed. A set of field tests was performed in order to analyze all the capabilities of the system, from task negotiations to final performance. These experiments also allowed testing control robustness under different weather conditions. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

M*: A complete multirobot path planning algorithm with performance bounds

Abstract: Multirobot path planning is difficult because the full configuration space of the system grows exponentially with the number of robots. Planning in the joint configuration space of a set of robots is only necessary if they are strongly coupled, which is often not true if the robots are well separated in the workspace. Therefore, we initially plan for each robot separately, and only couple sets of robots after they have been found to interact, thus minimizing the dimensionality of the search space. We present a general strategy called subdimensional expansion, which dynamically generates low dimensional search spaces embedded in the full configuration space. We also present an implementation of subdimensional expansion for robot configuration spaces that can be represented as a graph, called M*, and show that M* is complete and finds minimal cost paths.

Waalbot II: Adhesion Recovery and Improved Performance of a Climbing Robot using Fibrillar Adhesives

Abstract: This paper presents the design and optimization of a wall-climbing robot along with the incorporation of autonomous adhesion recovery and a motion planning implementation. The result is Waalbot II, an untethered 85 g robot able to climb on smooth vertical surfaces with up to a 100 g payload (117% body mass) or, when unburdened, on planar surfaces of any orientation at speeds up to 5 cm/s. Bio-inspired climbing mechanisms, such as Waalbot II’s gecko-like fibrillar adhesives, passive peeling, and force sensing, improve the overall climbing capabilities compared with initial versions, resulting in the ability to climb on non-smooth surfaces as well as on inverted smooth surfaces. Robot length scale optimization reveals and quantifies trends in the theoretical factor of safety and payload carrying capabilities. Autonomous adhesion recovery behavior provides additional climbing robustness without additional mechanical complexity to mitigate degradation and contamination. An implementation of a motion planner, designed to take into account Waalbot II’s kinematic constraints, results in the ability to navigate to a goal in complex three-dimensional environments while properly planning plane-to-plane transitions and avoiding obstacles. Experiments verified the improved climbing capabilities of Waalbot II as well as its novel semi-autonomous adhesion recovery behavior and motion planning.

Optimal path planning for surveillance with temporal-logic constraints*

Abstract: In this paper we present a method for automatically generating optimal robot paths satisfying high-level mission specifications. The motion of the robot in the environment is modeled as a weighted transition system. The mission is specified by an arbitrary linear temporal-logic (LTL) formula over propositions satisfied at the regions of a partitioned environment. The mission specification contains an optimizing proposition, which must be repeatedly satisfied. The cost function that we seek to minimize is the maximum time between satisfying instances of the optimizing proposition. For every environment model, and for every formula, our method computes a robot path that minimizes the cost function. The problem is motivated by applications in robotic monitoring and data-gathering. In this setting, the optimizing proposition is satisfied at all locations where data can be uploaded, and the LTL formula specifies a complex data-collection mission. Our method utilizes BA¼chi automata to produce an automaton (which can be thought of as a graph) whose runs satisfy the temporal-logic specification. We then present a graph algorithm that computes a run corresponding to the optimal robot path. We present an implementation for a robot performing data collection in a road-network platform.

Reinforcement based mobile robot navigation in dynamic environment

Abstract: In this paper, a new approach is developed for solving the problem of mobile robot path planning in an unknown dynamic environment based on Q-learning. Q-learning algorithms have been used widely for solving real world problems, especially in robotics since it has been proved to give reliable and efficient solutions due to its simple and well developed theory. However, most of the researchers who tried to use Q-learning for solving the mobile robot navigation problem dealt with static environments; they avoided using it for dynamic environments because it is a more complex problem that has infinite number of states. This great number of states makes the training for the intelligent agent very difficult. In this paper, the Q-learning algorithm was applied for solving the mobile robot navigation in dynamic environment problem by limiting the number of states based on a new definition for the states space. This has the effect of reducing the size of the Q-table and hence, increasing the speed of the navigation algorithm. The conducted experimental simulation scenarios indicate the strength of the new proposed approach for mobile robot navigation in dynamic environment. The results show that the new approach has a high Hit rate and that the robot succeeded to reach its target in a collision free path in most cases which is the most desirable feature in any navigation algorithm.

Sampling heuristics for optimal motion planning in high dimensions

Abstract: We present a sampling-based motion planner that improves the performance of the probabilistically optimal RRT* planning algorithm. Experiments demonstrate that our planner finds a fast initial path and decreases the cost of this path iteratively. We identify and address the limitations of RRT* in high-dimensional configuration spaces. We introduce a sampling bias to facilitate and accelerate cost decrease in these spaces and a simple node-rejection criteria to increase efficiency. Finally, we incorporate an existing bi-directional approach to search which decreases the time to find an initial path. We analyze our planner on a simple 2D navigation problem in detail to show its properties and test it on a difficult 7D manipulation problem to show its effectiveness. Our results consistently demonstrate improved performance over RRT*.

Combining high-level causal reasoning with low-level geometric reasoning and motion planning for robotic manipulation

Abstract: We present a formal framework that combines high-level representation and causality-based reasoning with low-level geometric reasoning and motion planning. The frame-work features bilateral interaction between task and motion planning, and embeds geometric reasoning in causal reasoning, thanks to several advantages inherited from its underlying components. In particular, our choice of using a causality-based high-level formalism for describing action domains allows us to represent ramifications and state/transition constraints, and embed in such formal domain descriptions externally defined functions implemented in some programming language (e.g., C++). Moreover, given such a domain description, the causal reasoner based on this formalism (i.e., the Causal Calculator) allows us to compute optimal solutions (e.g., shortest plans) for elaborate planning/prediction problems with temporal constraints. Utilizing these features of high-level representation and reasoning, we can combine causal reasoning, motion planning and geometric planning to find feasible kinematic solutions to task-level problems. In our framework, the causal reasoner guides the motion planner by finding an optimal task-plan; if there is no feasible kinematic solution for that task-plan then the motion planner guides the causal reasoner by modifying the planning problem with new temporal constraints. Furthermore, while computing a task-plan, the causal reasoner takes into account geometric models and kinematic relations by means of external predicates implemented for geometric reasoning (e.g., to check some collisions); in that sense the geometric reasoner guides the causal reasoner to find feasible kinematic solutions. We illustrate an application of this framework to robotic manipulation, with two pantograph robots on a complex assembly task that requires concurrent execution of actions. A short video of this application accompanies the paper.

Randomized multi-modal motion planning for a humanoid robot manipulation task

Abstract: Robots that perform complex manipulation tasks must be able to generate strategies that make and break contact with the object. This requires reasoning in a motion space with a particular multi-modal structure, in which the state contains both a discrete mode (the contact state) and a continuous configuration (the robot and object poses). In this paper we address multi-modal motion planning in the common setting where the state is high-dimensional, and there are a continuous infinity of modes. We present a highly general algorithm, Random-MMP, that repeatedly attempts mode switches sampled at random. A major theoretical result is that Random-MMP is formally reliable and scalable, and its running time depends on certain properties of the multi-modal structure of the problem that are not explicitly dependent on dimensionality. We apply the planner to a manipulation task on the Honda humanoid robot, where the robot is asked to push an object to a desired location on a cluttered table, and the robot is restricted to switch between walking, reaching, and pushing modes. Experiments in simulation and on the real robot demonstrate that Random-MMP solves problem instances that require several carefully chosen pushes in minutes on a PC.

Application of Dijkstra algorithm in robot path-planning

Abstract: In this paper, the robot is a maze robot. Dijkstra algorithm is used in the robot path planning. The shortest path is selected in the process of barrier. Simulation results prove the model valid; it can effectively solve the maze robot path planning.

Bounding on rough terrain with the LittleDog robot

Abstract: A motion planning algorithm is described for bounding over rough terrain with the LittleDog robot. Unlike walking gaits, bounding is highly dynamic and cannot be planned with quasi-steady approximations. LittleDog is modeled as a planar five-link system, with a 16-dimensional state space; computing a plan over rough terrain in this high-dimensional state space that respects the kinodynamic constraints due to underactuation and motor limits is extremely challenging. Rapidly Exploring Random Trees (RRTs) are known for fast kinematic path planning in high-dimensional configuration spaces in the presence of obstacles, but search efficiency degrades rapidly with the addition of challenging dynamics. A computationally tractable planner for bounding was developed by modifying the RRT algorithm by using: (1) motion primitives to reduce the dimensionality of the problem; (2) Reachability Guidance, which dynamically changes the sampling distribution and distance metric to address differential constraints and discontinuous motion primitive dynamics; and (3) sampling with a Voronoi bias in a lower-dimensional “task space” for bounding. Short trajectories were demonstrated to work on the robot, however open-loop bounding is inherently unstable. A feedback controller based on transverse linearization was implemented, and shown in simulation to stabilize perturbations in the presence of noise and time delays.

Push planning for object placement on cluttered table surfaces

Abstract: We present a novel planning algorithm for the problem of placing objects on a cluttered surface such as a table, counter or floor. The planner (1) selects a placement for the target object and (2) constructs a sequence of manipulation actions that create space for the object. When no continuous space is large enough for direct placement, the planner leverages means-end analysis and dynamic simulation to find a sequence of linear pushes that clears the necessary space. Our heuristic for determining candidate placement poses for the target object is used to guide the manipulation search. We show successful results for our algorithm in simulation.

Feasible and Optimal Path Planning in Strong Current Fields

Abstract: This paper addresses the problem of path planning in strong current fields In such situations, existing approaches are subject to incorrectness and incompleteness issues That is, they may return physically infeasible paths or no path at all, even if a feasible path exists That is why we propose here a new approach called the sliding wavefront expansion This algorithm, which combine an appropriate cost function and continuous optimization techniques, guarantees the existence of a path with an arbitrary precision The validity and the global optimality of the path are theoretically proven Simulation results on realistic environments, which is based on actual wind charts, are also provided

Motion Planning with Complex Goals

Abstract: This article describes approach for solving motion planning problems for mobile robots involving temporal goals. Traditional motion planning for mobile robotic systems involves the construction of a motion plan that takes the system from an initial state to a set of goal states while avoiding collisions with obstacles at all times. The motion plan is also required to respect the dynamics of the system that are typically described by a set of differential equations. A wide variety of techniques have been pro posed over the last two decades to solve such problems [1], [2].

Mobile robot navigation and target tracking system

Abstract: This paper presents the framework for the navigation and target tracking system for a mobile robot. Navigation and target tracking are to be performed using a Microsoft Xbox Kinect sensor which provides RGB color and 3D depth imaging data to an x86 based computer onboard the robot running Ubuntu Linux. A fuzzy logic controller to be implemented on the computer is considered for control of the robot in obstacle avoidance and target following. Data collected by the computer is to be sent to a server for processing with learning-based systems utilizing neural networks for pattern recognition, object tracking, long-term path planning and process improvement. An eventual goal of this work is to create a multi-agent robot system that is able to work autonomously in an outdoor environment.

Motion planning under uncertainty for robotic tasks with long time horizons

Abstract: Motion planning with imperfect state information is a crucial capability for autonomous robots to operate reliably in uncertain and dynamic environments. Partially observable Markov decision processes (POMDPs) provide a principled general framework for planning under uncertainty. Using probabilistic sampling, point-based POMDP solvers have drastically improved the speed of POMDP planning, enabling us to handle moderately complex robotic tasks. However, robot motion planning tasks with long time horizons remains a severe obstacle for even the fastest point-based POMDP solvers today. This paper proposes Milestone Guided Sampling (MiGS), a new point-based POMDP solver, which exploits state space information to reduce effective planning horizons. MiGS samples a set of points, called milestones, from a robot’s state space and constructs a simplified representation of the state space from the sampled milestones. It then uses this representation of the state space to guide sampling in the belief space and tries to capture the essential features of the belief space with a small number of sampled points. Preliminary results are very promising. We tested MiGS in simulation on several difficult POMDPs that model distinct robotic tasks with long time horizons in both 2-D and 3-D environments. These POMDPs are impossible to solve with the fastest point-based solvers today, but MiGS solved them in a few minutes.

Path Planning of Data Mules in Sensor Networks

Abstract: We study the problem of planning the motion of “data mules” for collecting the data from stationary sensor nodes in wireless sensor networks Use of data mules significantly reduces energy consumption at sensor nodes compared to commonly used multihop forwarding approaches, but has a drawback in that it increases the latency of data delivery Optimizing the motion of data mules, including path and speed, is critical for improving the data delivery latency and making the data mule approach more useful in practice In this article, we focus on the path selection problem: finding the optimal path of data mules so that the data delivery latency can be minimized We formulate the path selection problem as a graph problem that is capable of expressing the benefit from larger communication range The problem is NP-hard and we present approximation algorithms for both single-data mule case and multiple-data mules case We further consider the case in which we have only partial knowledge of communication range, where we design semionline algorithms that improve the offline plan using online knowledge at runtime Simulation experiments on Matlab and ns2 demonstrate that our offline and semionline algorithms produce significantly shorter path lengths and data delivery latency compared to previously proposed methods, suggesting that controlled mobility can be exploited much more effectively