This paper addresses the reactive collision avoidance for vehicles that move in very dense, cluttered, and complex scenarios. First, we describe the design of a reactive navigation method that uses a "divide and conquer" strategy based on situations to simplify the difficulty of the navigation. Many techniques could be used to implement this design (since it is described at symbolic level), leading to new reactive methods that must be able to navigate in arduous environments (as the difficulty of the navigation is simplified). We also propose a geometry-based implementation of our design called the nearness diagram navigation. The advantage of this reactive method is to successfully move robots in troublesome scenarios, where other methods present a high degree of difficulty in navigating. We show experimental results on a real vehicle to validate this research, and a discussion about the advantages and limitations of this new approach.

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 — An inevitable collision state for a robotic system can be defined as a state for which, no matter what the future trajectory followed by the system is, a collision with an obstacle eventually occurs. An inevitable collision state takes into account both the dynamics of the system and the obstacles, fixed or moving. The main contribution of this paper is to lay down and explore this novel concept (and the companion concept of inevitable collision obstacle). Formal definitions of the inevitable collision states and obstacles are given. Properties fundamental for their characterisation are established. This concept is very general and can be useful both for navigation and motion planning purposes (for its own safety, a robotic system should never find itself in an inevitable collision state!). The interest of this concept is illustrated by a safe motion planning example.

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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.

The execution of robotic tasks in dynamic, unstructured environments requires the generation of motion plans that respect global constraints imposed by the task while avoiding collisions with stationary, moving, and unforeseen obstacles. This paper presents the elastic strip framework, which addresses this problem by integrating global motion planning methods with a reactive motion execution approach. To maintain a collision-free trajectory, a given motion plan is incrementally modied to reect changes in the environment. This modication can be performed without suspending task behavior. The elastic strip framework is computationally efficient and can be applied to robots with many degrees of freedom. The paper also presents experimental results obtained by the implementation of this framework on the the Stanford Mobile Manipulator.

The dynamic window approach is a well known navigation scheme developed in Fox et al. (1997) and extended in Brock and Khatib (1999). It is safe by construction and has been shown to perform very e#ciently in experimental setups. However, one can construct examples where the proposed scheme fails to attain the goal configuration. What has been lacking is a theoretical treatment of the algorithm's convergence properties. Here we present such a treatment.

The recent development of a new kind of public transportation system relies on a particular double-steering kinematic structure enhancing manoeuvrability in cluttered environments such as downtown areas. We call bi-steerable car a vehicle showing this kind of kinematics. Endowed with autonomy capacities, the bi-steerable car ought to combine suitably and safely a set of abilities: simultaneous localisation and environment modelling, motion planning and motion execution amidst moderately dynamic obstacles. In this paper we address the integration of these four essential autonomy abilities into a single application. Specifically, we aim at reactive execution of planned motion. We address the fusion of controls issued from the control law and the obstacle avoidance module using probabilistic techniques. © 2004 Elsevier B.V. All rights reserved.

Abstract — In this paper we propose a new approach for multiobjective control using a cascade of filters that progressively removes candidate commands which do not satisfy task constraints. The approach is motivated by other control methods that prevent destructive control interactions through null space projections. We apply this approach to a practical leader/follower task in which a mobile robot must address the conflicting objectives of moving to a goal position while avoiding obstacles and keeping a region of the workspace within the field of view of a fixed camera mounted on the platform. We experimentally verify our approach using the Segway Robotic Mobility Platform (RMP), a dynamically stable, differential drive mobile robot. I.