Sensor Fusion and Obstacle Avoidance for an Unmanned Ground Vehicle

In recent years, the capabilities and potential value of unmanned autonomous systems (UAS) to perform an extensive variety of missions have significantly increased. It is well comprehended that there are various challenges associated with the realization of autonomous operations in complex urban environments. These difficulties include the requirement for precision guidance and control in conceivably GPS-denied conditions as well as the need to sense and avoid stationary and moving obstructions within the scene. The small size of some of these vehicles restricts the size, weight and power consumption of the sensor payload and onboard computational processing that can accommodated by UAS. This thesis analyzes the development and implementation of terrain mapping, path planning and control algorithms on an unmanned ground vehicle. Data from GPS, IMU and LIDAR sensors are fused in order to compute and update a dense 3D point cloud that is used by an implicit terrain algorithm to provide detailed mathematical representations of complex 3D structures generally found in urban environments. A receding horizon path planning algorithm is employed to adaptively produce a kinematically-feasible path for the unmanned ground vehicle. This path planning algorithm incorporates obstacle avoidance constraints and provides a set of waypoints to be followed by the unmanned ground vehicle. A waypoint controller is designed and implemented to enable the vehicle to follow the waypoints from the path planner. Open-loop experiments are provided with an unmanned ground vehicle in order to demonstrate terrain generation with real sensor data. Closed-loop results are then presented for a simulated ground vehicle in order to demonstrate the performance of the receding horizon path planning and control algorithms using the terrain map generated from the open-loop experiments

Automatic Path Planning for Unmanned Ground Vehicle Using UAV Imagery

Field machines play an important role in the management of agricultural environments. Increasing use of automated machines in precision agriculture has gained significant attention of farmers and industries to minimize human work load to perform tasks such as land preparation, seeding, fertilizing, plant health monitoring and harvesting. Path planning is considered as a fundamental step for agricultural machines equipped with autonomous navigation system. For mountain vineyards, path planning is a big challenge due to terrain morphology and unstructured vineyards. This paper proposes a workflow to generate an automatic coverage path plan for unmanned ground vehicles (UGVs) using georeferenced imagery taken by an unmanned aerial vehicle (UAV). First, image acquisition is performed over a vineyard to generate an orthomosaic and a digital surface model, which are then used to identify the vine rows and inter-row terrain. This information is then used by the algorithm to generate a path plan for UGV

Modified virtual semi-circle path planning

The challenging part of path planning for an Unmanned Ground Vehicle (UGV) is to conduct a reactive navigation. Reactive navigation is implemented to the sensor based UGV. The UGV defined the environment by collecting the information to construct it path planning. The UGV in this research is known as Mobile Guard UGV-Truck for Surveillance (MG-TruckS). Modified Virtual Semi Circle (MVSC) helps the MG-TruckS to reach it predetermined goal point successfully without any collision. MVSC is divided into two phases which are obstacles detection phase and obstacles avoidance phase to compute an optimal path planning. MVSC produces shorter path length, smoothness of velocity and reach it predetermined goal point successfully

Motion Planning for Mobile Robots

This chapter introduces two kinds of motion path planning algorithms for mobile robots or unmanned ground vehicles (UGV). First, we present an approach of trajectory planning for UGV or mobile robot under the existence of moving obstacles by using improved artificial potential field method. Then, we propose an I-RRT* algorithm for motion planning, which combines the environment with obstacle constraints, vehicle constraints, and kinematic constraints. All the simulation results and the experiments show that two kinds of algorithm are effective for practical use

Path and trajectory planning of a tethered UAV-UGV marsupial robotics system

This paper addresses the problem of trajectory planning in a marsupial robotic system consisting of an unmanned aerial vehicle (UAV) linked to an unmanned ground vehicle (UGV) through a non-taut tether that has a controllable length. The objective is to determine a synchronized collision-free trajectory for the three marsupial system agents: UAV, UGV, and tether. First, we present a path planning solution based on optimal Rapidly exploring Random Trees (RRT*) that takes into account constraints related to the positions of UAV, UGV, tether and the 3D environment. The specialization of the main RRT* methods allows us to obtain feasible solutions in short times. Then, the paper presents a trajectory planner based on non-linear least squares. The optimizer takes into account aspects not considered in the path planning, like temporal constraints of the motion that impose limits on the velocities and accelerations of the robots. Results from simulated scenarios demonstrate that the approach is able to generate obstacle-free and smooth trajectories for the UAV, UGV, and tether.Comment: 8 pages, 4 figures, 2 table

Path planning for mobile robot based on reactive collision avoidance method

Background: The number of research regarding local path planning of the Unmanned Ground Vehicle (UGV) is increasing widely. The Modified Virtual Semi Circle (MVSC) approach is proposed for real-time path planning. This research proposes the implementation of five ultrasonic range finder sensors with a very small blind zone existence in the sensor arrangement. The navigation of the mobile robot depends on the position of the mobile robot in the influence zone area. The formation of three layers of influence zone shows the optimized path planning without making any unnecessary obstacle avoidance presence. Objective: The purpose of this paper is to navigate a cost effective UGV known as MG-TruckS with optimal path planning. Results: The implementation of MVSC produced shortest path, smoothness of the velocity and successfully avoids collision with the obstacles to reach it predetermined target. Conclusion: MVSC propose a simple path planning that requires low computational cost and do not demand for a very large memory

Feasibility of Onboard Processing of Heuristic Path Planning and Navigation Algorithms within SUAS Autopilot Computational Constraints

This research addresses the flight path optimality of Small Unmanned Aerial Systems (SUAS) conducting overwatch missions for convoys or other moving ground targets. Optimal path planning algorithms have been proposed, but are computationally excessive for real-time execution. Using the Arduino-based ArduPilot Mega Unmanned Aerial Vehicle (UAV) autopilot system, Hardware-in-the-Loop (HIL) analysis is conducted on default mobile target tracking methods. Designed experimentation is used to determine autopilot settings that improve performance with respect to path optimality. Optimality is characterized using a weighted combination of stand-off range and aircraft roll-rate. Finally, a state-based heuristic navigation strategy is designed, developed, and tested that approximates optimal path solutions and can be used for real-time execution. A 66% improvement in mean performance is achieved over default target tracking methods. Finite state machine improvements are found to be statistically significant and it is concluded that heuristic strategies can be a viable approach to realizing near-optimal SUAS flight paths utilizing onboard processing capabilities

A Feasible Architecture Of Real-Time Collision Avoidance And Path Planning For Semi-Autonomous Unmanned Ground Vehicle (Ugv)

An Unmanned Ground Vehicle (UGV) is a vehicle that is on contact with ground and operates without human on-board. UGVs are widely used for mission-based applications that are often hazardous or inconvenient for humankind. Practically, UGVs are equipped with multiple devices like sensors and on-board camera to enable self-navigation and fulfil the mission requisite. In this project, the UGV combines two main elements, which are collision avoidance system (CAS) and path planning system (PPS). Costing is highly concerned for this project, therefore a low-cost ultrasonic sensor (HC�SR04) is used as a distance sensor for obstacle detection and avoidance purpose. Meanwhile, an Arduino Mega 2560 and ArduPilot Mega 2.6 (APM) are used as microcontrollers for obstacle avoidance and path optimization purpose respectively. Vehicle navigation is based on point-to-point basis and the waypoints are set in Mission Planner software that works alongside APM. In this project, the vehicle is programmed to maneuver at lower speed between 2 to 4 km/h. The maneuver is assisted with ultrasonic sensors (front and rear) sense and avoid the obstacles within 3 meters range. The ultrasonic sensors are programmed in such a way that it sweeps the sensors that the field of view of 180° angle both front and rear of the vehicle. Path planning is the key element for unmanned vehicle. Therefore, APM is used for navigation before and after approaching obstacles. Path planning algorithm in this project provides the best path to avoid the obstacle and create temporary new waypoints within split seconds. This practice makes the UGV more authentic and proximately imitable with human sensors and its responses