Abstract — What would it be like if we could give our robot high level commands and it would automatically execute them in a verifiably correct fashion in dynamically changing environments? This work demonstrates a method for generating continuous feedback control inputs that satisfy high-level specifications. Using a collection of continuous local feedback control policies in concert with a synthesized discrete automaton, this paper demonstrates the approach on an Ackermann-steered vehicle that satisfies the command “drive around until you find an empty parking space, then park. ” The system reacts to changing environmental conditions using only local information, while guaranteeing the correct high level behavior. The local policies consider the vehicle body shape as well as bounds on drive and steering velocities. The discrete automaton that invokes the local policies guarantees executions that satisfy the high-level specification based only on information about the current availability of the nearest parking space. This paper also demonstrates coordination of two vehicles using the approach. I.

Sampling-based algorithms have dramatically improved the state of the art in robotic motion planning. However, they make restrictive assumptions that limit their applicability to manipulators operating in uncontrolled and partially unknown environments. This work describes how one of these assumptions—that the world is perfectly known—can be removed. We propose a utility-guided roadmap planner that incorporates uncertainty directly into the planning process. This enables the planner to identify configuration space paths that minimize uncertainty and, when necessary, efficiently pursue further exploration through utility-guided sensing of the workspace. Experimental results indicate that our utilityguided approach results in a robust planner even in the presence of significant error in its perception of the workspace. Furthermore, we show how the planner is able to reduce the amount of required sensing to compute a successful plan.

Abstract — Given a robot model and a class of admissible environments, this paper provides a framework for automatically and verifiably composing controllers that satisfy high level task specifications expressed in suitable temporal logics. The desired task specifications can express complex robot behaviors such as search and rescue, coverage, and collision avoidance. In addition, our framework explicitly captures sensor specifications that depend on the environment with which the robot is interacting, resulting in a novel paradigm for sensor-based temporal logic motion planning. As one robot is part of the environment of another robot, our sensor-based framework very naturally captures multi-robot specifications. Our computational approach is based on first creating discrete controllers satisfying so-called General Reactivity(1) formulas. If feasible, the discrete controller is then used in order to guide the sensor-based composition of continuous controllers resulting in a hybrid controller satisfying the high level specification, but only if the environment is admissible. Index Terms — Motion planning, temporal logics, sensorbased planning, controller synthesis, hybrid control.

Abstract—This paper provides a framework to automatically generate a hybrid controller that guarantees that the robot can achieve its task when a robot model, a class of admissible environments, and a high-level task or behavior for the robot are provided. The desired task specifications, which are expressed in a fragment of linear temporal logic (LTL), can capture complex robot behaviors such as search and rescue, coverage, and collision avoidance. In addition, our framework explicitly captures sensor specifications that depend on the environment with which the robot is interacting, which results in a novel paradigm for sensor-based temporal-logicmotion planning. As one robot is part of the environment of another robot, our sensor-based framework very naturally captures multirobot specifications in a decentralized manner. Our computational approach is based on first creating discrete controllers satisfying specific LTL formulas. If feasible, the discrete controller is then used to guide the sensor-based composition of continuous controllers, which results in a hybrid controller satisfying the highlevel specification but only if the environment is admissible. Index Terms—Controller synthesis, hybrid control, motion planning, sensor-based planning, temporal logic. I.

In this paper, we address the temporal logic motion planning problem for point 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. In order to solve this problem, we follow a hierarchical approach that enables the control of the second order system by designing control laws for a fully actuated kinematic model. 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.

Amajor 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 singlerobot 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. This article uses the approach presented in [1], in which

Abstract — This paper presents a hybrid control strategy for navigation of shape-accelerated underactuated balancing systems with dynamic constraints. It extends the concept of sequential composition to perform discrete state-based switching between asymptotically convergent control policies to produce a globally asymptotically convergent feedback policy. The individual control policies consists of an external trajectory planner, a shape trajectory planner, an external trajectory tracking controller and a balancing controller. The paper also presents an integrated planning and control procedure, wherein standard graph-search algorithms are used to plan for the sequence of control policies that will help the system achieve a navigation goal. Simulation results of the 3D ballbot system navigating an environment with static obstacles to reach the goal position are also presented. I.