Motion planning with hybrid dynamics and temporal goals (original) (raw)
Motion planning with complex goals
2011
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.
Iterative temporal motion planning for hybrid systems in partially unknown environments
Proceedings of the 16th international conference on Hybrid systems: computation and control - HSCC '13, 2013
This paper considers the problem of motion planning for a hybrid robotic system with complex and nonlinear dynamics in a partially unknown environment given a temporal logic specification. We employ a multi-layered synergistic framework that can deal with general robot dynamics and combine it with an iterative planning strategy. Our work allows us to deal with the unknown environmental restrictions only when they are discovered and without the need to repeat the computation that is related to the temporal logic specification. In addition, we define a metric for satisfaction of a specification. We use this metric to plan a trajectory that satisfies the specification as closely as possible in cases in which the discovered constraint in the environment renders the specification unsatisfiable. We demonstrate the efficacy of our framework on a simulation of a hybrid second-order car-like robot moving in an office environment with unknown obstacles. The results show that our framework is successful in generating a trajectory whose satisfaction measure of the specification is optimal. They also show that, when new obstacles are discovered, the reinitialization of our framework is computationally inexpensive.
Sampling-based motion planning with temporal goals
2010
Abstract This paper presents a geometry-based, multi-layered synergistic approach to solve motion planning problems for mobile robots involving temporal goals. The temporal goals are described over subsets of the workspace (called propositions) using temporal logic. A multi-layered synergistic framework has been proposed recently for solving planning problems involving significant discrete structure.
Temporal Logic Motion Planning for Mobile Robots
2006
In this paper, we consider the problem of robot motion planning in order to satisfy formulas expressible in temporal logics. Temporal logics naturally express traditional robot specifications such as reaching a goal or avoiding an obstacle, but also more sophisticated specifications such as sequencing, coverage, or temporal ordering of different tasks. In order to provide computational solutions to this problem, we first construct discrete abstractions of robot motion based on some environmental decomposition. We then generate discrete plans satisfying the temporal logic formula using powerful model checking tools, and finally translate the discrete plans to continuous trajectories using hybrid control. Critical to our approach is providing formal guarantees ensuring that if the discrete plan satisfies the temporal logic formula, then the continuous motion also satisfies the exact same formula.
Temporal logic motion planning for dynamic robots
Automatica, 2009
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.
Motion planning with temporal-logic specifications: Progress and challenges
AI Communications, 2015
Integrating task and motion planning is becoming increasingly important due to the recognition that a growing number of robotics applications in navigation, search-and-rescue missions, manipulation, and medicine involve reasoning with both discrete abstractions and continuous motions. The problem poses unique computational challenges: a vast hybrid discrete/continuous space must be searched while accounting for complex geometries, motion dynamics, collision avoidance, and temporal goals. This paper takes the position that continued progress relies on integrative approaches that bring together techniques from robotics and AI. In this context, the paper examines robot motion planning with temporal-logic specifications and discusses open challenges and directions for future research. The paper aims to promote a continuing dialog between robotics and AI communities.
Hybrid Controllers for Path Planning: A Temporal Logic Approach
Proceedings of the 44th IEEE Conference on Decision and Control, 2005
Robot motion planning algorithms have focused on low-level reachability goals taking into account robot kinematics, or on high level task planning while ignoring low-level dynamics. In this paper, we present an integrated approach to the design of closed-loop hybrid controllers that guarantee by construction that the resulting continuous robot trajectories satisfy sophisticated specifications expressed in the so-called Linear Temporal Logic. In addition, our framework ensures that the temporal logic specification is satisfied even in the presence of an adversary that may instantaneously reposition the robot within the environment a finite number of times. This is achieved by obtaining a Büchi automaton realization of the temporal logic specification, which supervises a finite family of continuous feedback controllers, ensuring consistency between the discrete plan and the continuous execution.
Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics
Cornell University - arXiv, 2018
Noisy observations coupled with nonlinear dynamics pose one of the biggest challenges in robot motion planning. By decomposing nonlinear dynamics into a discrete set of local dynamics models, hybrid dynamics provide a natural way to model nonlinear dynamics, especially in systems with sudden discontinuities in dynamics due to factors such as contacts. We propose a hierarchical POMDP planner that develops cost-optimized motion plans for hybrid dynamics models. The hierarchical planner first develops a high-level motion plan to sequence the local dynamics models to be visited and then converts it into a detailed continuous state plan. This hierarchical planning approach results in a decomposition of the POMDP planning problem into smaller sub-parts that can be solved with significantly lower computational costs. The ability to sequence the visitation of local dynamics models also provides a powerful way to leverage the hybrid dynamics to reduce state uncertainty. We evaluate the proposed planner on a navigation task in the simulated domain and on an assembly task with a robotic manipulator, showing that our approach can solve tasks having high observation noise and nonlinear dynamics effectively with significantly lower computational costs compared to direct planning approaches.
Motion planning with dynamics by a synergistic combination of layers of planning
2010
Abstract To efficiently solve challenges related to motion-planning problems with dynamics, this paper proposes treating motion planning not just as a search problem in a continuous space but as a search problem in a hybrid space consisting of discrete and continuous components. A multilayered framework is presented which combines discrete search and sampling-based motion planning. This framework is called synergistic combination of layers of planning (SyCLoP) hereafter.
A motion planner for a hybrid robotic system with kinodynamic constraints
2007
Abstract The rapidly increasing complexity of tasks robotic systems are expected to carry out underscores the need for the development of motion planners that can take into account discrete changes in the continuous motions of the system. Completion of tasks such as exploration of unknown or hazardous environments often requires discrete changes in the controls and motions of the robot in order to adapt to different terrains or maintain operability during partial failures or other mishaps.
Iterative Temporal Planning in Uncertain Environments With Partial Satisfaction Guarantees
IEEE Transactions on Robotics, 2016
This work introduces a motion-planning framework for a hybrid system with general continuous dynamics to satisfy a temporal logic specification consisting of co-safety and safety components in a partially unknown environment. The framework employs a multi-layered synergistic planner to generate trajectories that satisfy the specification and adopts an iterative replanning strategy to deal with unknown obstacles. When the discovery of an obstacle renders the specification unsatisfiable, a division between the constraints in the specification is considered. The co-safety component of the specification is treated as a soft constraint, whose partial satisfaction is allowed, while the safety component is viewed as a hard constraint, whose violation is forbidden. To partially satisfy the co-safety component, inspirations
T* : A Heuristic Search Based Algorithm for Motion Planning with Temporal Goals
ArXiv, 2018
Motion planning is the core problem to solve for developing any application involving an autonomous mobile robot. The fundamental motion planning problem involves generating a trajectory for a robot for point-to-point navigation while avoiding obstacles. Heuristic-based search algorithms like A* have been shown to be extremely efficient in solving such planning problems. Recently, there has been an increased interest in specifying complex motion plans using temporal logic. In the state-of-the-art algorithm, the temporal logic motion planning problem is reduced to a graph search problem and Dijkstra's shortest path algorithm is used to compute the optimal trajectory satisfying the specification. The A* algorithm when used with a proper heuristic for the distance from the destination can generate an optimal path in a graph efficiently. The primary challenge for using A* algorithm in temporal logic path planning is that there is no notion of a single destination state for the robot...
Temporal Logic based Motion Planning in Unstructured Environments
In this paper we describe the problem of motion planning for mul- tiple goals in unstructured environments by using temporal logic for speci- cation of the goals. We solve the problem of mission planning for mobile robots, wherein the goal of the robot can be a complex mission speci cation rather than a classical "Go from A to B" problem. To achieve this problem we take a two dimensional map of the real environment and identify the re- gions of interest, navigable areas and non-navigable areas, which are tagged to make the nal map. We also assume that during the motion of the robot the current position is known and the environment is stationary. We describe the high level planing algorithm which gives a trajectory catering to the tem- poral logic based goal speci cation. We use optimal graph search to search for an optimal solution, while at each step checking for the validity of the temporal logic. Simulation results to various practical scenarios are provided making a robot act over high level speci cations while yielding optimal plans.
A hybrid approach for complete motion planning
IEEE International Conference on Intelligent Robots and Systems, 2007
We present an efficient algorithm for complete motion planning that combines approximate cell decomposition (ACD) with probabilistic roadmaps (PRM). Our approach uses ACD to subdivide the configuration space into cells and computes localized roadmaps by generating samples within these cells. We augment the connectivity graph for adjacent cells in ACD with pseudo-free edges that are computed based on localized roadmaps. These roadmaps are used to capture the connectivity of free space and guide the adaptive subdivision algorithm. At the same time, we use cell decomposition to check for path non-existence and generate samples in narrow passages. Overall, our hybrid algorithm combines the efficiency of PRM methods with the completeness of ACD-based algorithms. We have implemented our algorithm on 3-DOF and 4-DOF robots. We demonstrate its performance on planning scenarios with narrow passages or no collision-free paths. In practice, we observe up to 10 times improvement in performance over prior complete motion planning algorithms.
Temporal-Logic-Based Reactive Mission and Motion Planning
IEEE Transactions on Robotics, 2000
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.
2019 International Conference on Robotics and Automation (ICRA), 2019
This work addresses the problem of robot navigation under timed temporal specifications in workspaces cluttered with obstacles. We propose a hybrid control strategy that guarantees the accomplishment of a high-level specification expressed as a timed temporal logic formula, while preserving safety (i.e., obstacle avoidance) of the system. In particular, we utilize a motion controller that achieves safe navigation inside the workspace in predetermined time, thus allowing us to abstract the motion of the agent as a finite timed transition system among certain regions of interest. Next, we employ standard formal verification and convex optimization techniques to derive high-level timed plans that satisfy the agent's specifications. A simulation study illustrates and clarifies the proposed scheme.
Vehicle Motion Planning with Time-Varying Constraints
Journal of Guidance, Control, and Dynamics, 2004
With the growing emphasis on vehicle autonomy, the problem of planning a trajectory in an environment with obstacles has become increasingly important. This task has been of particular interest to roboticists and computer scientists, whose primary focus is on kinematic motion planning . Typical kinematic planning methods fall into two main categories, roadmap methods and incremental search methods, both of which find collision-free paths in the state space. Roadmap methods generate and traverse a graph of collision-free connecting paths spanning the state space, while incremental search methods, including dynamic programming [2] and potential field methods [3], perform an iterative search to connect the initial and goal states. For the purely geometric path planning problem, deterministic algorithms have been created that are complete, i.e., they will find a solution if and only if one exists. Unfortunately, these suffer from high computational costs which are exponential in system degrees of freedom. This cost has motivated the development of iterative randomized path planning algorithms that are probabilistically complete, i.e., if a feasible path exists, the probability of finding a path from the initial to final conditions converges to one as the number of iterations goes to infinity. The introduction of the Rapidly-exploring Random Trees (RRTs) of LaValle and Kuffner [4] allowed both for computationally efficient exploration of a complicated space as well as incorporation of system dynamics. The RRT grows a tree of feasible trajectories from the initial condition, or root node. Each node, or waypoint, on the tree represents a system state and has possible trajectories branching from it. Through use of an embedded planning routine, the tree incrementally builds itself in random direc-Associate Fellow AIAA 1 of 12 tions, node by node, until the final conditions are met (within accuracy bounds). Frazzoli demonstrated that a hybrid systems representation of vehicle dynamics, when coupled with the RRT, could be used to address moving obstacles and time-invariant final conditions in a real-time environment. This paper presents a similar approach which provides probabilistic completeness in the presence of both time-varying obstacles and final conditions while using a simpler algorithmic procedure. In addition, a novel approach to provide error mitigation of the embedded planner in a hybrid system-based RRT is presented. An example is then given in which the proposed algorithm is applied to the landing of a spacecraft on an idealized asteroid.
Time-Informed Exploration For Robot Motion Planning
2020
Anytime sampling-based methods are an attractive technique for solving kino-dynamic motion planning problems. These algorithms scale well to higher dimensions and can efficiently handle state and control constraints. However, an intelligent exploration strategy is required to accelerate their convergence and avoid redundant computations. This work defines a "Time Informed Set", using ideas from reachability analysis, that focuses the search for time-optimal kino-dynamic planning after an initial solution is found. Such a Time Informed Set includes all trajectories that can potentially improve the current best solution. Exploration outside this set is hence redundant. Benchmarking experiments show that an exploration strategy based on the Time Informed Set can accelerate the convergence of sampling-based kino-dynamic motion planners.
Reactive motion planning in a dynamic world
Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments, 1991
| This paper deals with the problem of planning and controlling the motion of a car like vehicle moving in a dynamic and roadway like environment. The contribution presented here is a motion controller which executes in a reactive way a given nominal motion plan. Such a plan is made up of a smooth trajectory C and of time constraints of the type \reach location l at time t l ". Data concerning the actual environment of the vehicle considered are assumed to be obtained through perception. In order to get the required reactivity, we have developed a motion controller with two main components: the pilot which analyses the current situation and adapts the nominal plan accordingly, and the executor which generates the required motion commands. The pilot operates at a symbolic level using a set of behavioural rules. The executor makes use of a potential eld approach to generate the motion commands.
A hybrid mobile robot architecture with integrated planning and control
Proceedings of the first international joint conference on Autonomous agents and multiagent systems part 1 - AAMAS '02, 2002
Research in the planning and control of mobile robots has received much attention in the past two decades. Two basic approaches have emerged from these research efforts: deliberative vs. reactive. These two approaches can be distinguished by their different usage of sensed data and global knowledge, speed of response, reasoning capability, and complexity of computation. Their strengths are complementary and their weaknesses can be mitigated by combining the two approaches in a hybrid architecture. This paper describes a method for goal-directed, collision-free navigation in unpredictable environments that employs a behavior-based hybrid architecture with asynchronously operating behavioral modules. It differs from existing hybrid architectures in two important ways: (1) the planning module produces a sequence of checkpoints instead of a conventional complete path, and (2) in addition to obstacle avoidance, the reactive module also performs target reaching under the control of a self-organizing neural network. The neural network is trained to perform fine, smooth motor control that moves the robot through the checkpoints. These two aspects facilitate a tight integration between high-level planning and low-level control, which permits real-time performance and easy path modification even when the robot is en route to the goal position.