Courteous Cars: Decentralized Multiagent Traffic Coordination

A major goal in robotics is to develop machines that perform useful tasks with minimal supervision. Instead of requiring each small detail to be specified, we would like to describe the task at a high level and have the system autonomously execute in a manner that satisfies that desired task. While the single robot case is difficult enough, moving to a multirobot behavior adds another layer of challenges. Having every robot achieve its specific goals while contributing to a global coordinated task requires each robot to react to information about other robots, for example, to avoid collisions. Furthermore, each robot must incorporate new information into its decision framework to react to environmental changes induced by other robots since this knowledge may effect its behavior

Feedback Motion Prediction for Safe Unicycle Robot Navigation

As a simple and robust mobile robot base, differential drive robots that can be modelled as a kinematic unicycle find significant applications in logistics and service robotics in both industrial and domestic settings. Safe robot navigation around obstacles is an essential skill for such unicycle robots to perform diverse useful tasks in complex cluttered environments, especially around people and other robots. Fast and accurate safety assessment plays a key role in reactive and safe robot motion design. In this paper, as a more accurate and still simple alternative to the standard circular Lyapunov level sets, we introduce novel conic feedback motion prediction methods for bounding the close-loop motion trajectory of the kinematic unicycle robot model under a standard unicycle motion control approach. We present an application of unicycle feedback motion prediction for safe robot navigation around obstacles using reference governors, where the safety of a unicycle robot is continuously monitored based on the predicted future robot motion. We investigate the role of motion prediction on robot behaviour in numerical simulations and conclude that fast and accurate feedback motion prediction is key for fast, reactive, and safe robot navigation around obstacles.Comment: 11 pages, 5 figures, extended version of a paper submitted to a conference publicatio

Exact Robot Navigation Using Power Diagrams

We reconsider the problem of reactive navigation in sphere worlds, i.e., the construction of a vector field over a compact, convex Euclidean subset punctured by Euclidean disks, whose flow brings a Euclidean disk robot from all but a zero measure set of initial conditions to a designated point destination, with the guarantee of no collisions along the way. We use power diagrams, generalized Voronoi diagrams with additive weights, to identify the robot’s collision free convex neighborhood, and to generate the value of our proposed candidate solution vector field at any free configuration via evaluation of an associated convex optimization problem. We prove that this scheme generates a continuous flow with the specified properties. We also propose its practical extension to the nonholonomically constrained kinematics of the standard differential drive vehicle.For more information: Kod*la

Nonholonomic motion planning using the fast marching square method

This research presents two novel approaches to nonholonomic motion planning. The methodologies presented are based on the standard fast marching square path planning method and its application to car-like robots. Under the first method, the environment is considered as a three-dimensional C-space, with the first two dimensions given by the position of the robot and the third dimension by its orientation. This means that we operate over the configuration space instead of the bi-dimensional environment map. Moreover, the trajectory is computed along the C-space taking into account the dimensions of the vehicle, and thus guaranteeing the absence of collisions. The second method uses the standard fast marching square, and takes advantage of the vector field of the velocities computed during the first step of the method in order to adapt the motion plan to the control inputs that a car-like robot is able to execute. Both methods ensure the smoothness and safety of the calculated paths in addition to providing the control actions to perform the trajectory.This work is funded by project number DPI2010-17772, by the Spanish Ministry of Science and Innovation, and also by the RoboCity2030-II-CM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid, and co-funded by the Structural Funds of the EU

Towards Reactive Control of Transitional Legged Robot Maneuvers

We propose the idea of a discrete navigation problem – consisting of controlling the state of a discrete-time control system to reach a goal set while in the interim avoiding a set of obstacle states – to approximate a simplified class of transitional legged robotic tasks such as leaping which have no well established mathematical description that lends itself to synthesis. The control relation given in Theorem 1 is (assuming a task solution exists) necessary and sufficient to solve a discrete navigation problem in a minimum number of steps, and is well suited to computation when a legged system’s continuous-time within-stride controller anchors sufficiently simple stance mechanics. We demonstrate the efficacy of this control technique on a physical hopping robot affixed to a boom to reactively leap over an obstacle with a running start, controlling in continuous time during stance to exhibit a linear stance map

Temporal logic motion planning for dynamic robots

In this paper, we address the temporal logic motion planning problem for mobile robots that are modeled by second order dynamics. Temporal logic specifications can capture the usual control specifications such as reachability and invariance as well as more complex specifications like sequencing and obstacle avoidance. Our approach consists of three basic steps. First, we design a control law that enables the dynamic model to track a simpler kinematic model with a globally bounded error. Second, we built a robust temporal logic specification that takes into account the tracking errors of the first step. Finally, we solve the new robust temporal logic path planning problem for the kinematic model using automata theory and simple local vector fields. The resulting continuous time trajectory is provably guaranteed to satisfy the initial user specification

An Extended Convergence Result for Behaviour Tree Controllers

Behavior trees (BTs) are an optimally modular framework to assemble hierarchical hybrid control policies from a set of low-level control policies using a tree structure. Many robotic tasks are naturally decomposed into a hierarchy of control tasks, and modularity is a well-known tool for handling complexity, therefor behavior trees have garnered widespread usage in the robotics community. In this paper, we study the convergence of BTs, in the sense of reaching a desired part of the state space. Earlier results on BT convergence were often tailored to specific families of BTs, created using different design principles. The results of this paper generalize the earlier results and also include new cases of cyclic switching not covered in the literature.Comment: Submitted to the IEEE Transactions on Robotics (T-RO

Reactive Planning With Legged Robots In Unknown Environments

Unlike the problem of safe task and motion planning in a completely known environment, the setting where the obstacles in a robot\u27s workspace are not initially known and are incrementally revealed online has so far received little theoretical interest, with existing algorithms usually demanding constant deliberative replanning in the presence of unanticipated conditions. Moreover, even though recent advances show that legged platforms are becoming better at traversing rough terrains and environments, legged robots are still mostly used as locomotion research platforms, with applications restricted to domains where interaction with the environment is usually not needed and actively avoided. In order to accomplish challenging tasks with such highly dynamic robots in unexplored environments, this research suggests with formal arguments and empirical demonstration the effectiveness of a hierarchical control structure, that we believe is the first provably correct deliberative/reactive planner to engage an unmodified general purpose mobile manipulator in physical rearrangements of its environment. To this end, we develop the mobile manipulation maneuvers to accomplish each task at hand, successfully anchor the useful kinematic unicycle template to control our legged platforms, and integrate perceptual feedback with low-level control to coordinate each robot\u27s movement. At the same time, this research builds toward a useful abstraction for task planning in unknown environments, and provides an avenue for incorporating partial prior knowledge within a deterministic framework well suited to existing vector field planning methods, by exploiting recent developments in semantic SLAM and object pose and triangular mesh extraction using convolutional neural net architectures. Under specific sufficient conditions, formal results guarantee collision avoidance and convergence to designated (fixed or slowly moving) targets, for both a single robot and a robot gripping and manipulating objects, in previously unexplored workspaces cluttered with non-convex obstacles. We encourage the application of our methods by providing accompanying software with open-source implementations of our algorithms

Model based methods for the control and planning of running robots

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 115-122.The Spring-Loaded Inverted Pendulum (SLIP) model has long been established as an effective and accurate descriptive model for running animals of widely differing sizes and morphologies. Not surprisingly, the ability of such a simple spring-mass model to capture the essence of running motivated several hopping robot designs as well as the use of the SLIP model as a control target for more complex legged robot morphologies. Further research on the SLIP model led to the discovery of several analytic approximations to its normally nonintegrable dynamics. However, these approximations mostly focus on steady-state running with symmetric trajectories due to their linearization of gravitational effects, an assumption that is quickly violated for locomotion on more complex terrain wherein transient, non-symmetric trajectories dominate. In the first part of the thesis , we introduce a novel gravity correction scheme that extends on one of the more recent analytic approximations to the SLIP dynamics and achieves good accuracy even for highly non-symmetric trajectories. Our approach is based on incorporating the total effect of gravity on the angular momentum throughout a single stance phase and allows us to preserve the analytic simplicity of the approximation to support research on reactive footstep planning for dynamiclegged locomotion. We compare the performance of our method with two other existing analytic approximations by simulation and show that it outperforms them for most physically realistic non-symmetric SLIP trajectories while maintaining the same accuracy for symmetric trajectories. Additionally, this part of the thesis continues with analytical approximations for tunable stiffness control of the SLIP model and their motion prediction performance analysis. Similarly, we show performance improvement for the variable stiffness approximation with gravity correction method. Besides this, we illustrate a possible usage of approximate stance maps for the controlling of the SLIP model. Furthermore, the main driving force behind research on legged robots has always been their potential for high performance locomotion on rough terrain and the outdoors. Nevertheless, most existing control algorithms for such robots either make rigid assumptions about their environments (e.g flat ground), or rely on kinematic planning with very low speeds. Moreover, the traditional separation of planning from control often has negative impact on the robustness of the system against model uncertainty and environment noise. In the second part of the thesis, we introduce a new method for dynamic, fully reactive footstep planning for a simplified planar spring-mass hopper, a frequently used dynamic model for running behaviors. Our approach is based on a careful characterization of the model dynamics and an associated deadbeat controller, used within a sequential composition framework. This yields a purely reactive controller with a very large, nearly global domain of attraction that requires no explicit replanning during execution. Finally, we use a simplified hopper in simulation to illustrate the performance of the planner under different rough terrain scenarios and show that it is robust to both model uncertainty and measurement noise.Arslan, ÖmürM.S