Collision avoidance for multi-vehicle cooperative missions

This thesis focuses on collision avoidance for multi-vehicle coordinated missions. Building upon an existing cooperative control framework, we propose collision-avoidance methods that rely on practicably available obstacle information and allow safe operation without compromising on the mission objectives. Several applications of multi-vehicle coordinated missions require the vehicles to satisfy relative temporal constraints, such as maintaining formation throughout the mission or reaching their respective destinations at the same time. With such applications in focus, two different methodologies for collision avoidance are explored. We first consider a speed-adjustment based approach that can be used to avoid moving obstacles. Using obstacle information which may be available in real world applications such as air traffic management and highway driving, the proposed algorithm allows collision avoidance without requiring any vehicle to deviate from its path or lose coordination with other vehicles. Next, trajectory replanning approach for obstacle avoidance is considered. Applicable to both static and moving obstacles, this method may require the vehicle to steer away from its originally intended path. The deviations in position, velocity and acceleration caused by the avoidance maneuver, however, are small and respect bounds that can be computed offline. These bounds can be used during the mission-planning phase to guarantee satisfaction of vehicle dynamic constraints and inter-vehicle safety distance even during collision avoidance maneuver. Through novel use of Bézier curves and surfaces for representing uncertain trajectories, these algorithms make use of partial information on obstacle trajectory and are computationally efficient

Collision-free Multiple Unmanned Combat Aerial Vehicles Cooperative Trajectory Planning for Time-critical Missions using Differential Flatness Approach

This paper investigates the cooperative trajectory planning for multiple unmanned combat aerial vehicles in performing autonomous cooperative air-to-ground target attack missions. Firstly, the collision-free cooperative trajectory planning problem for time-critical missions is formulated as a cooperative trajectory optimal control problem (CTP-OCP), which is based on an approximate allowable attack region model, several constraints model, and a multi-criteria objective function. Next, a planning algorithm based on the differential flatness, B-spline curves and nonlinear programming is designed to solve the CTP-OCP. In particular, the notion of the virtual time is introduced to deal with the temporal constraints. Finally, the proposed approach is validated by two typical scenarios and the simulation results show the feasibility and effectiveness of the proposed planning approach.Defence Science Journal, Vol. 64, No. 1, January 2014, DOI:10.14429/dsj.64.299

Cooperative Path-Planning for Multi-Vehicle Systems

In this paper, we propose a collision avoidance algorithm for multi-vehicle systems, which is a common problem in many areas, including navigation and robotics. In dynamic environments, vehicles may become involved in potential collisions with each other, particularly when the vehicle density is high and the direction of travel is unrestricted. Cooperatively planning vehicle movement can effectively reduce and fairly distribute the detour inconvenience before subsequently returning vehicles to their intended paths. We present a novel method of cooperative path planning for multi-vehicle systems based on reinforcement learning to address this problem as a decision process. A dynamic system is described as a multi-dimensional space formed by vectors as states to represent all participating vehicles’ position and orientation, whilst considering the kinematic constraints of the vehicles. Actions are defined for the system to transit from one state to another. In order to select appropriate actions whilst satisfying the constraints of path smoothness, constant speed and complying with a minimum distance between vehicles, an approximate value function is iteratively developed to indicate the desirability of every state-action pair from the continuous state space and action space. The proposed scheme comprises two phases. The convergence of the value function takes place in the former learning phase, and it is then used as a path planning guideline in the subsequent action phase. This paper summarizes the concept and methodologies used to implement this online cooperative collision avoidance algorithm and presents results and analysis regarding how this cooperative scheme improves upon two baseline schemes where vehicles make movement decisions independently

A Pseudospectral Optimal Motion Planner for Autonomous Unmanned Vehicles

2010 American Control Conference, Marriott Waterfront, Baltimore, MD, USA, June 30-July 02, 2010This paper presents a pseudospectral (PS) optimal control algorithm for the autonomous motion planning of a fleet of unmanned ground vehicles (UGVs). The UGVs must traverse an obstacle-cluttered environment while maintaining robustness against possible collisions. The generality of the algorithm comes from a binary logic that modifies the cost function for various motion planning modes. Typical scenarios including path following and multi-vehicle pursuit are demonstrated. The proposed framework enables the availability of real-time information to be exploited by real-time reformulation of the optimal control problem combined with real-time computation. This allows the each vehicle to accommodate potential changes in the mission/environment and uncertain conditions. Experimental results are presented to substantiate the utility of the approach on a typical planning scenario

Coordinated Path Following of UAVs over Time-Varying Digraphs Connected in an Integral Sense

This paper presents a new connectivity condition on the information flow between UAVs to achieve coordinated path following. The information flow is directional, so that the underlying communication network topology is represented by a time-varying digraph. We assume that this digraph is connected in an integral sense. This is a much more general assumption than the one currently used in the literature. Under this assumption, it is shown that a decentralized coordination controller ensures exponential convergence of the coordination error vector to a neighborhood of zero. The efficacy of the algorithm is confirmed with simulation results