Trajectory Re-planning Research Articles

A COLREGs-based obstacle avoidance approach for unmanned surface vehicles

This paper reports the preliminary research results of a novel automatic obstacle avoidance approach based on the COLREGs for unmanned surface vehicles (USVs). The approach presented is essentially a path searching-based algorithm called the local normal distribution-based trajectory, which plans viable avoidance trajectories in the presence of both static and dynamic obstacles. The proposed algorithm can generate a COLREGs-compliant suboptimal trajectory based on the bell-shaped curve of normal distribution and extract waypoints for the navigation controller to steer USVs safely. In addition, we discuss three key parameters and present a trajectory replanning strategy to improve the safety and flexibility of our approach. The common overtaking, crossing and head-on collision scenarios are each simulated in experiments. It is shown through simulations that the proposed approach considers multiple factors and can plan paths to avoid obstacles safely and smoothly. A comparison is also made with a reactive path planning algorithm which has been modified to follow the COLREGs.

Obstacle avoidance method of three-dimensional obstacle spherical cap

Focusing on obstacle avoidance in three-dimensional space for unmanned aerial vehicle (UAV), the direct obstacle avoidance method in dynamic space based on three-dimensional velocity obstacle spherical cap is proposed, which quantifies the influence of threatening obstacles through velocity obstacle spherical cap parameters. In addition, the obstacle avoidance schemes of any point on the critical curve during the multi-obstacles avoidance are given. Through prediction, the insertion point for the obstacle avoidance can be obtained and the flight path can be re-planned. Taking the Pythagorean Hodograph (PH) curve trajectory re-planning as an example, the three-dimensional direct obstacle avoidance method in dynamic space is tested. Simulation results show that the proposed method can realize the online obstacle avoidance trajectory re-planning, which increases the flexibility of obstacle avoidance greatly.

Piecewise Bézier Curves for Avoiding Collisions During Multivehicle Coordinated Missions

This paper presents a Bézier-curve-based collision prediction and avoidance algorithm for multivehicle cooperative missions. The proposed algorithm runs on each vehicle independently from other vehicles. Given a predicted trajectory for the obstacle, it checks for a possible collision and modifies the vehicle’s own trajectory, if needed. Under a few assumptions, the replanned trajectory is guaranteed to avoid the collision and satisfy mission-specific constraints. The bounds on deviations in position, velocity, and acceleration caused by trajectory replanning are computable a priori. These bounds can be used during the mission-planning phase to guarantee satisfaction of vehicle dynamic constraints and intervehicle safety distance by the replanned trajectory.

A Framework of Vehicle Trajectory Replanning in Lane Exchanging with Considerations of Driver Characteristics

This paper presents a control framework to address a typical vehicle-to-vehicle (V2V) encountering scenario of lane exchanging with two vehicles traveling on contiguous lanes in the same direction and are maneuvered to exchange lanes. A trajectory generating model for lane-changing maneuver is integrated into the driver–vehicle system. Based on this system, both the risk of collision and the lane-exchanging intentions of the drivers can be considered in the trajectory replanning. A linear time-varying model predictive control with parameters of a discretized nonlinear system is proposed to realize the optimization objectives of satisfying the drivers’ maneuvering intentions and reducing the risk of collision. The optimization problem is subject to the constraints consisting of the human driver preferences, driving habits, and driving capabilities. Simulations have verified the controller's performance and robustness in different scenarios, including different initial conditions for the risk of collision and different combinations of drivers’ preferences and driving habits. The results show that the proposed controller can satisfy drivers’ intentions while avoiding collisions in all these scenarios.

Automated Threat Avoidance with 4-D Trajectory Replanning in Resource-Constrained Cockpits

Military aircraft equipped with tactical data links and onboard sensors can receive information about pop-up threats, such as surface-to-air missiles and storm cells, which can put the safety of the aircraft in jeopardy. Pilots currently need to mentally combine hazard information from various sources to determine whether these hazards will endanger the safety of the aircraft. If an avoidance maneuver is required, preferably at standoff ranges so that a less-drastic evasive maneuver can be used, pilots also need to decide what necessary maneuver should be performed to avoid these potential hazards as well as terrain and surrounding traffic. This paper presents a 4-D trajectory replanning algorithm that can operate very efficiently in cockpits with limited computational resources to automate the threat avoidance task. A layered approach is used by the proposed algorithm to transform the long-range pathplanning problem that is NP-hard into a problem that can be solved rapidly in embedded computational environments. In addition, the generated flight path is flyable and compatible with onboard flight-management systems to enable the automatic loading of a modified flight plan. The performance of the proposed algorithm was evaluated with a high-fidelity flight-deck simulator for operationally relevant scenarios.

Trajectory Planning and Replanning Strategies Applied to a Quadrotor Unmanned Aerial Vehicle

This paper considers the problem of trajectory planning/re-planning for a quadrotor unmanned aerial vehicle (UAV) system. In the fault-free case, the objective is to find the profile of the trajectory to follow so that the system constraints are not violated. When actuator faults occur, trajectory re-planning changes the nominal trajectory so as to take into consideration the new control limitations induced by the occurred fault. Trajectory re-planning requires information about fault location and amplitude and thus unscented Kalman filter (UKF) is employed to detect the occurrence of the fault, isolate the location of the fault and identify the fault amplitude. The main objective is to design a trajectory planning/re-planning approach that can be implemented on real UAV systems with low computation capabilities. For this purpose, the reference trajectories are designed as second-order systems to be used with a flatness-based trajectory planning/re-planning method. The approach is successfully applied to a quadrotor UAV testbed at the Department of Mechanical and Industrial Engineering of Concordia University.

Remote Calibration and Trajectory Replanning for Robot Manipulators Operating in Unstructured Environments

In this work the problem of remote calibration and trajectory replanning for a subsea robotic manipulator is considered. Because the trajectory planning is normally done in a structured environment, several uncertainties arise when the robot is placed on the seabed and these need to be compensated for to guarantee that the task specifications are fulfilled. We address the particular problem of configuration errors in the robot base with respect to the configuration used during the off-line trajectory planning. A calibration method based on both internal and external sensors is presented in order to estimate the uncertainty in the location of the robot. Moreover, a trajectory replanning strategy in the Cartesian velocity space is proposed to guarantee that the originally planned trajectory is followed. Simulation and experiments performed with a 6-DoF robot manipulator and a real calibration grid show the viability of the proposed planning and control schemes.

Max-Min Adaptive Ant Colony Optimization Approach to Multi-UAVs Coordinated Trajectory Replanning in Dynamic and Uncertain Environments

Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic model of multi-UAVs coordinated trajectory replanning, which includes problem description, threat modeling, constraint conditions, coordinated function and coordination mechanism, a novel Max-Min adaptive Ant Colony Optimization (ACO) approach is presented in detail. In view of the characteristics of multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments, the minimum and maximum pheromone trails in ACO are set to enhance the searching capability, and the point pheromone is adopted to achieve the collision avoidance between UAVs at the trajectory planner layer. Considering the simultaneous arrival and the air-space collision avoidance, an Estimated Time of Arrival (ETA) is decided first. Then the trajectory and flight velocity of each UAV are determined. Simulation experiments are performed under the complicated combating environment containing some static threats and popup threats. The results demonstrate the feasibility and the effectiveness of the proposed approach.

On-line trajectory resolution for two-armed systems with conflicting performance criteria

Abstract Recent automation research has proposed robotic machining as an alternative to traditional CNC machining. However, unlike traditional machine tools, robots are subject to relatively larger dynamic disturbances and operate closer to their torque limits. As a result of these conditions, combined with inaccurate models of the manipulators and the machining process, joint actuators can often saturate during machining operation, resulting in damage to the tool, or the part, or both. As off-line planned trajectories are executed by the controller, unmodeled disturbances arising due to inaccurate manipulator and blank models may necessitate on-line trajectory re-planning to provide alternate joint trajectories. Such joint trajectories must be constrained to maintain a desired tool path relative to a blank, to respect the manipulators’ torque and joint limits, and to avoid collisions. This paper presents a new method for selecting the null-space motion based on torque limits and conflicting performance criteria. The effectiveness of the null-space motion algorithm has been demonstrated via extensive simulations.

EXPERIMENTAL VALIDATION OF HIGH SPEED HAZARD AVOIDANCE CONTROL FOR UNMANNED GROUND VEHICLES

At high speeds in natural terrain, unmanned ground vehicles can experience unexpected situations that require rapid hazard avoidance maneuvers. In these scenarios there is often limited time to replan a path based on high-order dynamic models. This paper presents a novel method for high speed navigation and hazard avoidance based on the two dimensional “trajectory space,” a compact and computationally efficient model-based representation of a robot's dynamic performance limits on natural terrain. This paper also presents a novel method for trajectory replanning, based on a “curvature matching” technique. Experimental results on a small high-speed UGV demonstrate the method's effectiveness.

Robot planning and re-planning using decomposition, abstraction, deduction, and prediction

One of the current topics of research in robotics is activity planning. Most works in this field are related to artificial intelligence (AI) techniques. The main feature of robot planning systems is that they are focused on the particular robot problem or are intended to solve a particular aspect of the planning problem. For this last case, motion planning or task planning are solved in an independent way. This paper covers robot activity planning and re-planning, from the high-level activity description to the final trajectory of the robot. Problem decomposition and abstraction are applied to obtain a robot plan; deduction and prediction are used for on-line trajectory re-planning. Temporal reasoning enables the obtention of velocity tuning for a trajectory.