This paper addresses the problem of motion planning (MP) in dynamic environments. It is first argued that dynamic environments impose a real-time constraint upon MP: it has a limited time only to compute a motion, the time available being a function of the dynamicity of the environment. Now, given the intrinsic complexity of MP, computing a complete motion to the goal within the time available is impossible to achieve in most real situations. Partial Motion Planning (PMP) is the answer to this problem proposed in this paper. PMP is a motion planning scheme with an anytime flavor: when the time available is over, PMP returns the best partial motion to the goal computed so far. Like reactive navigation scheme, PMP faces a safety issue: what guarantee is there that the system will never end up in a critical situation yielding an inevitable collision? The answer proposed in this paper to this safety issue relies upon the concept of Inevitable Collision States (ICS). ICS takes into account the dynamics of both the system and the moving obstacles. By computing ICS-free partial motion, the system safety can be guaranteed. Application of PMP to the case of a car-like system in a dynamic environment is presented.

Abstract — We present an efficient, anytime method for path planning in dynamic environments. Current approaches to planning in such domains either assume that the environment is static and replan when changes are observed, or assume that the dynamics of the environment are perfectly known a priori. Our approach takes into account all prior information about both the static and dynamic elements of the environment, and efficiently updates the solution when changes to either are observed. As a result, it is well suited to robotic path planning in known or unknown environments in which there are mobile objects, agents or adversaries. I.

Classical search algorithms such as A* or IDA* are useful for computing optimal solutions in a single pass, which can then be executed. But in many domains agents either do not have the time to compute complete plans before acting, or should not spend the time to do so, due to the dynamic nature of the environment. Extensions to A* such as LRTA* address this problem by gradually learning an exact heuristic function, but the learning process is quite slow. In this paper we introduce Partial–Refinement A* (PRA*), which can fully interleave planning and acting through path abstraction and refinement. We demonstrate the effectiveness of PRA* in the domain of real–time strategy (RTS) games. In maps taken from popular RTS games, we show that PRA* is not only able to cleanly interleave planning and execution, but it

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Heuristic search methods promise to find shortest paths for path-planning problems faster than uninformed search methods. Incremental search methods, on the other hand, promise to find shortest paths for series of similar path-planning problems faster than is possible by solving each path-planning problem from scratch. In this article, we develop Lifelong Planning A * (LPA*), an incremental version of A * that combines ideas from the artificial intelligence and the algorithms literature. It repeatedly finds shortest paths from a given start vertex to a given goal vertex while the edge costs of a graph change or vertices are added or deleted. Its first search is the same as that of a version of A * that breaks ties in favor of vertices with smaller g-values but many of the subsequent searches are potentially faster because it reuses those parts of the previous search tree that are identical to the new one. We present analytical results that demonstrate its similarity to A * and experimental results that demonstrate its potential advantage in two different domains if the path-planning problems change only slightly and the changes are close to the goal.

Abstract — In this paper, we present an algorithm for generating complex dynamically-feasible maneuvers for autonomous vehicles traveling at high speeds over large distances. Our approach is based on performing anytime incremental search on a multiresolution, dynamically-feasible lattice state space. The resulting planner provides real-time performance and guarantees on and control of the suboptimality of its solution. We provide theoretical properties and experimental results from an implementation on an autonomous passenger vehicle that competed in, and won, the Urban Challenge competition. I.

Abstract: This paper presents our work of integration during the last years within the context of sensor‐based robot navigation systems. In our motion system, as in many others, there are functionalities involved such as modeling, planning or motion control, which have to be integrated within an architecture. This paper addresses this problematic. Furthermore, we also discuss the lessons learned while: (i) designing, testing and validating techniques that implement the functionalities of the navigation system, (ii) building the architecture of integration, and (iii) using the system on several robots equipped with different sensors in different laboratories. Keywords: Mobile robots, Sensor‐Based Robot Navigation, Robot Architectures and Integration. 1.

Abstract. We present an interpolation-based planning and replanning algorithm for generating smooth paths through non-uniform cost grids. Most grid-based path planners use discrete state transitions that artificially constrain an agent’s motion to a small set of possible headings (e.g. 0, π π, , etc). As a result, even the ‘optimal ’ grid planners produce unnatural, suboptimal 4 2 paths. Our approach uses linear interpolation during planning to calculate accurate path cost estimates for arbitrary positions within each grid cell and to produce paths with a continuous range of headings. Consequently, it is particularly well suited to planning smooth, least-cost trajectories for mobile robots. In this paper, we present a number of applications and results, a comparison to related algorithms, and several implementations on real robotic systems. 1

Mobile robots often operate in domains that are only incompletely known, for example, when they have to move from given start coordinates to given goal coordinates in unknown terrain. In this case, they need to be able to replan quickly as their knowledge of the terrain changes. Stentz’ Focussed Dynamic A * (D*) is a heuristic search method that repeatedly determines a shortest path from the current robot coordinates to the goal coordinates while the robot moves along the path. It is able to replan faster than planning from scratch since it modifies its previous search results locally. Consequently, it has been extensively used in mobile robotics. In this article, we introduce an alternative to D * that determines the same paths and thus moves the robot in the same way but is algorithmically different. D * Lite is simple, can be rigorously analyzed, extendible in multiple ways, and is at least as efficient as D*. We believe that our results will make D*-like replanning methods even more popular and enable robotics researchers to adapt them to additional applications.

This paper describes the design and implementation of a path planning system for an autonomous ground vehicle. The system is designed to be flexible, allowing any planning algorithm to be used and any topology of data to be planned over. It employs a hierarchical separation of two planning modules in conjunction with a vehicle model, to achieve continued vehicle motion while planning and the ability to act as either a deliberative or reactive planner, or a hybrid of both types. Results from both simulation and field trials are presented, and demonstrate the effectiveness of this architecture on a large outdoor ground vehicle. The contributions of this paper are twofold: a flexible planning system capable of large scale missions for autonomous vehicles; and the use of a vehicle model to determine the requirements for safe operation without slowing the vehicle, and the conditions under which this cannot be achieved.