We review a range of techniques related to navigation of unmanned vehicles through unknown environments with obstacles, especially those that rigorously ensure collision avoidance (given certain assumptions about the system). This topic continues to be an active area of research, and we highlight some directions in which available approaches may be improved. The paper discusses models of the sensors and vehicle kinematics, assumptions about the environment, and performance criteria. Methods applicable to stationary obstacles, moving obstacles and multiple vehicles scenarios are all reviewed. In preference to global approaches based on full knowledge of the environment, particular attention is given to reactive methods based on local sensory data, with a special focus on recently proposed navigation laws based on model predictive and sliding mode control.

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Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

We discuss an implementation of the RRT* optimal motion planning algorithm for the half-car dynamical model to enable autonomous high-speed driving. To develop fast solutions of the associated local steering problem, we observe that the motion of a special point (namely, the front center of oscillation) can be modeled as a double integrator augmented with fictitious inputs. We first map the constraints on tire friction forces to constraints on these augmented inputs, which provides instantaneous, state-dependent bounds on the curvature of geometric paths feasibly traversable by the front center of oscillation. Next, we map the vehicle's actual inputs to the augmented inputs. The local steering problem for the half-car dynamical model can then be transformed to a simpler steering problem for the front center of oscillation, which we solve efficiently by first constructing a curvature-bounded geometric path and then imposing a suitable speed profile on this geometric path. Finally, we demonstrate the efficacy of the proposed motion planner via numerical simulation results.

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Optimal motion planning with the half-car dynamical model for autonomous high-speed driving

This paper addresses the driver–automation shared driving control for lane keeping and obstacle avoidance of automated vehicles in highway traffic. The proposed shared control framework is established from a novel cooperative trajectory planning algorithm and a fuzzy steering controller. Based on polynomial functions, the cooperative trajectory planning is formulated by judiciously exploiting the information on the maneuver decision, the conflict management, and the driver monitoring. As a result, the planned trajectory of the vehicle is continuously adapted according to the driver's actions and intentions. By means of Lyapunov stability arguments, sufficient conditions in terms of linear matrix inequalities are given to design a Takagi–Sugeno fuzzy model-based controller. This robust steering controller provides a necessary assistive torque to track the planned vehicle trajectory. The new shared driving control framework allows reducing effectively the driver–automation conflict issue while offering the driver more freedom to swerve within a predefined lane. The advantages of the proposed approach are evaluated using both objective and subjective results, experimentally obtained from several human drivers and an advanced interactive dynamic driving simulator.

Cooperative Trajectory Planning for Haptic Shared Control Between Driver and Automation in Highway Driving

RRT and RRT* have become popular planning techniques, in particular for high-dimensional systems such as wheeled robots with complex nonholonomic constraints. Their planning times, however, can scale poorly for such robots, which has motivated researchers to study hierarchical techniques that grow the RRT trees in more focused ways. Along this line, we introduce Theta*-RRT that hierarchically combines (discrete) any-angle search with (continuous) RRT motion planning for nonholonomic wheeled robots. Theta*-RRT is a variant of RRT that generates a trajectory by expanding a tree of geodesics toward sampled states whose distribution summarizes geometric information of the any-angle path. We show experimentally, for both a differential drive system and a high-dimensional truck-and-trailer system, that Theta*-RRT finds shorter trajectories significantly faster than four baseline planners (RRT, A*-RRT, RRT*, A*-RRT*) without loss of smoothness, while A*-RRT* and RRT* (and thus also Informed RRT*) fail to generate a first trajectory sufficiently fast in environments with complex nonholonomic constraints. We also prove that Theta*-RRT retains the probabilistic completeness of RRT for all small-time controllable systems that use an analytical steer function.

/pdf/rrt-based-nonholonomic-motion-planning-using-any-angle-path-518j5guz9i.pdf

RRT-based nonholonomic motion planning using any-angle path biasing

Motion tracking control design of nonholonomic mobile robot systems considering actuator dynamics is addressed in this paper. A trajectory tracking controller is designed at actuator level, which guarantees that the nonholonomic mobile robot tracks a given trajectory. A numerical example is shown to demonstrate and validate the proposed approach in this paper.

Motion Tracking Control Design for a Class of Nonholonomic Mobile Robot Systems

New requirements of autonomous mobile vehicles necessitate hierarchical motion-planning techniques that not only find a plan to satisfy high-level specifications, but also guarantee that this plan is suitable for execution under vehicle dynamical constraints. In this context, the H-cost motion-planning technique has been reported in the recent literature. We propose an incremental motion-planning algorithm based on this technique. The proposed algorithm retains the benefits of the original technique, while significantly reducing the associated computational time. In particular, the proposed iterative algorithm presents during intermediate iterations feasible solutions, with the guarantee that the algorithm eventually converges to an optimal solution. The costs of solutions at intermediate iterations are almost always nonincreasing. Therefore, the proposed algorithm is suitable for real-time implementations, where hard bounds on the available computation time are imposed, and where the original H-cost optimization algorithm may not have sufficient time to converge to a solution at all. We illustrate the proposed algorithm with numerical simulation examples.

Incremental path repair in hierarchical motion-planning with dynamical feasibility guarantees for mobile robotic vehicles

We investigate motion-planning for a team of robotic vehicles assigned to a collaborative intelligent task in the form of global linear temporal logic (LTL) specifications. Specifically, we extend recent results from the literature to include nonholonomic kinematic constraints on the robotic vehicles. The problem formulation relies on workspace cell decompositions, where certain regions of interest in the robots' shared workspace are defined. The proposed algorithm involves two graphs: first, the topological graph arising from the workspace cell decomposition, and second, a graph arising from vertex aggregation on the previous graph. The main technical innovation is the application of the so-called method of lifted graphs to determine the feasibility of edge transitions in these graphs. We illustrate the proposed approach with numerical simulation examples.

Motion-planning with global temporal logic specifications for multiple nonholonomic robotic vehicles

We present a new technique for the control of a nonholonomic vehicle kinematic model subject to linear temporal logic (LTL) specifications. The proposed technique is based on partitioning of the vehicle's planar workspace into cells. The main result of this paper is the precise characterization of acceptable sequences of cells, which can be traversed by admissible state trajectory of the vehicle model while satisfying the given LTL specifications. The proposed approach does not require complete controllability of the vehicle model in the presence of workspace constraints, and no linearization of the model is involved. The key technical innovation is the so-called lifted graph. Edge transitions in the lifted graph are associated with certain forward- and backward reachable sets of the vehicle model. We illustrate the proposed technique with numerical simulation examples.

Motion-planning with linear temporal logic specifications for a nonholonomic vehicle kinematic model

A new technique for aircraft route guidance subject to linear temporal logic specifications is presented. The proposed approach is based on workspace partitioning, and it relies on the idea of so-called lifted graphs. Briefly, edges in a lifted graph are successions of adjacent edges in the topological graph associated with the workspace partition. Edges of the lifted graph are associated with certain reachability properties of the aircraft model. The main result of this paper is the precise characterization of acceptable routes (namely, sequences of cells) that are guaranteed to be traversable by admissible state trajectories of the aircraft model while satisfying the given linear temporal logic specifications. The proposed approach incorporates nonholonomic kinematic constraints, does not require complete controllability in the presence of workspace constraints, and does not require linearization of the aircraft model. Numerical methods to implement the proposed route-planning algorithm are discussed. T...

Zetian Zhang

Papers

Incremental path repair in hierarchical motion-planning with dynamical feasibility guarantees for mobile robotic vehicles

Motion-planning with global temporal logic specifications for multiple nonholonomic robotic vehicles

Motion-planning with linear temporal logic specifications for a nonholonomic vehicle kinematic model

Route Guidance for Satisfying Temporal Logic Specifications on Aircraft Motion

Decentralized Route-Planning to Satisfy Global Linear Temporal Logic Specifications on Multiple Aircraft