A series of experimental and simulation studies were performed in order to demonstrate and evaluate the performance of the GNC algorithms applied to the unmannedgroundvehicle. An initial open-loop experiment was conducted in which the UGV was manually controlled to drive a straight path on level terrain with two cones on either side of the path serving as obstacles. The purpose of this experiment was to confirm the functionality of the terrain mapping algorithms for a relatively simple test case. Then, a second open-loop experiment was conducted in which the robot slowly traveled on a curved path with multiple obstacles in the area in order to test the terrain mapping algorithms on more complex terrain. Finally, a set of closed-loop simulations was performed in which the UGV planned a path and autonomously drove through the planned waypoints to arrive at a target location. In these simulations, the terrain data derived from the second set open-loop experiment was used to provide the obstacleavoidance constraints for the path planning algorithm.
This chapter covers on the applications of mobile robot and the summary and discussion of section. On the applications of mobile robot, it covers briefly on obstacleavoidance in indoor environment. Next, the type of distance sensors, the type motors, and the type of controllers are covered in obstacleavoidance in indoor environment. The different types of distance sensor and motor used for obstacleavoidance and their construction are also include in the type of distance sensor and type of motor sections. Other than that, different types of controller are reviewed in order to control the mobile robot to avoid the obstacle.
Even in the absence of obstacleavoidance manoeuvres, the basic heading-to-goal controller as described above is incapable of accurately tracking a path specified by a series of way- points. In the event that the vehicle is directed away from the path to avoid an obstacle in its way, having passed the obstacle it will turn back towards the path heading straight towards the interim goal directly from its current location. No attempt is made to track the path itself. A simple expedient which improves path tracking is to steer towards a point on the path a fixed lookahead distance from the vehicle rather than steering towards the next waypoint or the goal position itself. The lookahead point, shown in Figure 2, slides along the path a fixed distance ahead of the vehicle. A PID loop can then be used to control the error between the current heading and the heading to the tracking ”carrot”, adhering more closely to the intended path.
The sensorfusion problem is not a new one, of course, and has been explored in the past. One potential solution was explored by a group of undergraduate and graduate students from Caltech on their entry into the DARPA Grand Challenge: a 10-hour, 175-mile off-road race for unmanned autonomous vehicles that was held for the second time in October 2005 (see figure 1.1). In preparation for the Grand Challenge, a particularly flexible framework was developed for performing sensorfusion, using both DEMs and goodness maps. In particular, every sensor created its own DEM and corresponding goodness map using methods described in Cremean et al. (2006). The resulting goodness maps were then fused using a heuristic algorithm that was biased toward sensors whose coverage areas were closer to the vehicle. A diagram of this framework is depicted in figure 1.2. Unfortunately, this solution had several limitations, which we briefly review here.
The UnmannedGroundVehicle Electronic Hardware Architecture memorandum describes an electronic hardware architecture that has the flexibility and extensibility to support a wide range of UGV platforms. It achieves this flexibility by ascribing to a distributed paradigm which enables the use of multiple scales of processors. The Electronic Hardware Architecture is applicable to small indoor platforms with limited payloads and it easily scales to support large platforms that do not have payload limitations. The hardware architecture does not exist in isolation since it provides services and functionality to software algorithms. It is the software that creates autonomous capabilities to the UGV. These autonomous capabilities are implemented by software algorithms such as: the representation of the world; path planning; obstacleavoidance; and others. Additional software will be involved in commanding the various motors that allow the UGV to traverse the selected path.
Laser is a new navigation tool developed with the development of laser technology, it is applicable for vehicle navigation, field exploration and other work under poor light. In recent years, many research achievements in the fields of obstacleavoidance, autonomous driving and map construction have been produced. Lindström and Eklundh  developed an algorithm that identifies range readings in areas that was detected earlier as free is described without incorporating any grid maps that are inherently memory and computationally consuming. Liu et al.  analyzed three typical obstacles belonging to three categories (active obstacles, inactive obstacles and inactive obstacles) based on frequency analysis respectively. Kondaxakis et al.  presented an innovative approach that addresses all issues (robot’s relative motion compensation, feature extraction, measurement clustering, data association and targets’ state vector estimation) exploiting various probabilistic and deterministic techniques. Arnay et al.  developed a method to combine a low-cost 16 beam solid state laser sensor and a conventional video camera for obstacle detection. Lundell et al.  presented a method to estimate the distance directly from raw 2D laser data. .
Abstract—Small/micro Unmanned Aerial Systems (UAVs) re- quire the ability to operate with constraints of a diverse, auto- mated airspace where obstacle telemetry is denied. This paper proposes a novel Sense, Detect and Avoid (SDA) algorithm with inherit resilience to sensor uncertainty. This is achieved through the interval geometric formulation of the avoidance problem, which by the use of interval analysis, can be extended to consider multiple obstacles. The approach is shown to demonstrate the ability to both tolerate sensor uncertainty and enact generated 3D avoidance trajectories. Monte-Carlo simulations demonstrate successful avoidance rates of 88%, 96% and 91% in two ex- ample collision scenarios and one multi-agent conflict scenario respectively.
 This paper presents an obstacle detection and avoidance system for an unmanned Lawnmower. The system consists of two (Infrared and Ultrasonic) sensors, an Arduino microcontroller and a gear DC motor. The ultrasonic and infrared sensors are implemented to detect obstacles on the robot's path by sending signals to an interfaced microcontroller. The micro- controller redirects the robot to move in an alternate direction by actuating the motors in order to avoid the detected obstacle. In conclusion, an obstacle detection circuit was successfully implemented using infrared and ultrasonic sensors modules which were placed at the front of the robot to throw both light and sound waves at any obstacle and when a reflection is received, a low output is sent to the Arduino microcontroller which interprets the output and makes the robot to stop.
 Choon-Young Lee, Ho-Gun Choi, Jun-Sik Park, Keun-Young Park, Sang-Ryong Lee “Collision Avoidance by the Fusion of Different Beam-width Ultrasonic Sensors” Several ultrasonic sensors with a fixed bandwidth were implemented in this paper and were used for robot navigation. For mobile robot communication each ultrasonic sensor consists of different bandwidths. To differentiate environmental conditions, small beam width is applied and wide beam width is applied to use only a few sensors. It is possible to obtain more effective collision avoidance system when two types of beam widths are used. A sensor with enormous beam width provides knowledge of the possible obstacles in robot movement and can maneuver in a complex environment. This paper created an original module and an advanced algorithm to avoid obstacles in the mobile robot. Better precise map,
In this project for measuring the distance between obstacle and vehicle, ultrasonic sensors are used. The ultrasonic sensor consists of two parts. One part is an emitter which is used to emit high frequency sound wave of the order of 40kHz. The second part consists of detector which detects high frequency sound wave of the order of 40kHz. When trigger signal is applied to the ultrasonic sensor by processing unit, it starts sending sound waves. This sound wave after hitting any obstacle is returned back to the ultrasonic sensor as echo signal. This is explained in fig 2. This returned sound wave has apparent shift in its frequency produced by either moving obstacle or stationary obstacle. This is known as Doppler effect. Processing unit receives this echo signal and determines the apparent frequency shift to calculate the time length required for the sound wave which was generated by the emitter to travel the distance to the obstacle.
The application and complexity of mobile robots are slowly growing every day. They are gradually making their way into real world settings in different fields such as military, medical fields, space exploration, and everyday housekeeping . Motion being a vital characteristic of mobile robots in obstacleavoidance and path recognition has a major impact on how people react and perceive an autonomous system. This enables an autonomous robot to be able to navigate from one place to another without human intervention. Computer vision and range sensors are primary object detection methods used in mobile robots’ detection. Computer vision as an obstacle detection method is more rigorous and expensive technique than the range sensors’ method. However, most commercial autonomous robots use range sensor to detect obstacles. The use of radar, infrared (IR) sensor and ultrasonic sensor for developing an obstacle detection system had started as early as the 1980’s . Although, after testing these technologies it was concluded that the radar technology was the most suitable for use as the other two technology options were prone to environmental constraints such as rain, ice, snow, dust and dirt. The radar approach was also a very cost effective technology both for the present and the future.  presented a method using a single charge-coupled device (CCD) camera in conjunction with a spherically shaped curved reflector
In the development of collision avoidance algorithms, only a simple kinematic model of the vehicle is used normally. This greatly simplifies the analysis and design of collision avoidance algorithms. However, the model in the verification stage must be as close to the real world as possible, which demands a much more complicated model. A simplified model of a vehicle and its operational environment is used in the algorithm development process while the real vehicle and its operational environment are much more complicated, with possibly a much high order of dynamics, nonlinearity, and much more complicated operation scenarios. This causes structural uncertainties in the verification of collision avoidance algorithms. The parameter uncertainties represent the variations of parameters that capture the changes of vehicle dynamics and its operational environment. The variations of the autonomous vehicle dynamics in operation may arise due to the changes in the vehicle itself (e.g. the change of mass or the centre of gravity) or the change of the operation environment (e.g. tyre friction for different road surfaces). In the online motion planning, unmanned vehicles must be able to sense obstacles, determine the obstacles positions and velocities, and reach the target position. However, there is inevitably uncertainty in the sensor data due to the limited accuracy of the robot’s sensors and environmental noises. Therefore, it is necessary to verify whether or not an OAS under question is able to avoid obstacles with uncertain sensor data.
groundvehicle. The traditional potential field controller is also augmented to take the stream function into account. Simulation results are presented to show the effectiveness of the potential field generation technique and the augmented vehicle controller . A new concept using a virtual obstacle is proposed to escape local minimums occurred in local path planning. A virtual obstacle is located around local minimums to repel a mobile robot from local minimums. A sensor based discrete modeling method is also proposed for modeling of the mobile robot with range sensors. This modeling method is adaptable for a real-time path planning because it provides lower complexity . A path- planning algorithm for the classical mover's problem in three dimensions using a potential field representation of obstacles is presented. A potential function similar to the electrostatic potential is assigned to each obstacle, and the topological structure of the free space is derived in the form of minimum potential valleys. Path planning is done at two levels. First, a global planner selects a robot's path from the minimum potential valleys and its orientations along the path that minimize a heuristic estimate of the path length and the chance of collision. Then, a local planner modifies the path and orientations to derive the final collision-free path and orientations. If the local planner fails, a new path and orientations are selected by the global planner and subsequently examined by the local planner. This process is continued until a solution is found or there are no paths left to be examined. The algorithm solves a much wider class of problems than other heuristic algorithms and at the same time runs much faster than exact algorithms . The potential field method is widely used for autonomous mobile robot path planning due to its elegant mathematical analysis and simplicity. However, most researches have been focused on solving the motion planning problem in a stationary environment where both targets and obstacles are stationary. Ref.  proposes a new potential field method for motion planning of mobile robots in a dynamic environment where the target and the obstacles are moving. Firstly, the new potential function and the corresponding virtual force are defined. Then, the problem of local minima is discussed. Finally, extensive computer simulations and hardware experiments are carried out to demonstrate the effectiveness of the dynamic motion planning schemes based on the new potential field method.
A mathematical model of the GUV movement based on the Dubins machine is developed which allows determining the motion vector of a wheeled vehicle. It is established that there are 64 combinations of motion to construct the trajectory of the GUV. A scheme is developed that determines the time of movement along the trajectory and allows to determine the characteristics of the movement of the ground GUV. The proposed optimization criterion time and the determination of equilibrium points, given the 64 combinations of the trajectory.
In shallow water, the use of large AUVs is not practical since larger object has more inertia when moving around in water and it is difficult to avoid obstacle. Hence, small AUVs which are miniature in size will come useful in shallow water to perform missions because of ease for mobility as well as compact without limiting its functionality and long lasting power . There are number of researchers who do researches on small AUVs. Some of the small AUVs developed are Seabird AUV (2007), Monsun II AUV (2012) and Dagon AUV (2010) . One researcher named Simon A. Watson developed a spherical AUV (2011) with a dimension of 20cm in length and width, and 15cm in height. AUVs with such miniature size are known as micro-AUV . The prototype developed in this Final Year Project is 35cm in length, which is an intermediate AUV in between SeaBird and Watson’s. Figure 1.1 illustrates the comparison of a few small AUV examples includes the prototype of this Final Year Project.
According to C. R. Rocha, R. M. Brancoy, L. A. D. Cruzz, M. V. Schollx, M. M. Cezarx and Felipe D , the Arduino Uno as shown in Figure 2.3 is chosen as a microcontroller for the Mission Monitoring/Control System (MMCS) to supervise te underwater vehicle. Arduino Nano board is a small and complete board based on the ATmega328. It has 14 digital input/output pins (6 provide PWM output) and 8 analog inputs. It features 32KB flash memory, 2KB SRAM and 16 MHz of clock frequency. It consists of a mini USB interface but no DC power jack. Its operating voltage is 5V. The mini USB header of the board is used to upload programming language from an IDE and supply power.
ABSTRACT: The various steering systems used in all-terrain vehicles by various authors are reviewed in this paper. This paper presents the comparison of the different steering systems used, their application and limitation by various authors. The objective of present work is to study the different steering systems and to choose the best suitable steering system for an unmannedgroundvehicle which would be used for military purpose.
that the yaw of the quadcopter is inherently known and thus there is no need to estimate. From this research, an understanding is developed to implement a consistent quadcopter controller for attitude stabilization and position navigation. Coordinated landing of a quadcopter on a moving groundvehicle is discussed in ,  and . In these works the camera is mounted on the groundvehicle or at the ground station but the camera is stationary. Conventionally, quadcopters carry a camera on them for vision based navigation, but for the work in this a thesis camera is mounted on the moving groundvehicle. The camera faces up and forwards and provides images of the moving quadcopter. This simplifies the problem statement as it aids in pose estimations from groundvehicle and simplifies the quadcopter control problem. This also helps in ground air interactions for multi-agent systems. Also, to deal with situations of temporary target loss the vision-based tracking algorithm was coupled with Kalman filter to estimate position.
AGVs at high speed through unstructured environments without collision while en- suring vehicle dynamical safety. However, the formulation assumes that the vehicle longitudinal speed is maintained constant, which can limit the mobility performance and the obstacle fields that can be cleared with this algorithm. Thus, the formulation is later extended to simultaneously optimize both the longitudinal speed and steering control commands. The novelty of the formulation includes: (1) A varying prediction horizon MPC is used to achieve a fixed distance prediction. This is prompted by two features of the proposed system. First, the terminal point of the planned trajectory is constrained at the LIDAR ’s maximum detection range in an effort to fully utilize as much information from the LIDAR as possible. Second, the variable vehicle speed necessarily leads to a variable prediction horizon with the previous constraint. (2) The effects of powertrain and brake systems are taken into account through the relation- ship between acceleration and speed and the bounds on longitudinal jerk, acceleration, and speed. The vehicle’s acceleration capability varies with the speed resulting from the powertrain and brake systems. To generate a speed profile that can be tracked by the vehicle, the algorithm uses an offline generated look-up table to account for the acceleration and deceleration limitations. (3) The no-wheel-lift-off requirement is considered through both hard and soft constraints using equations with empirical parameters that can predict tire vertical loads. A hard constraint bounds the vertical loads to be greater than a specified minimum threshold. A soft constraint is also used to provide a smooth approach to this threshold to prevent overshoot. Three sets of numerical simulations are conducted to demonstrate the effectiveness of the algorithm.
As it has just been said, we decided to use the “APM Mission Planner” and the “QGroundControl” as our ground stations for testing the APM:Rover-based UGV. Apart from allowing us to compare the functionalities between them, the reason behind using two different stations is that they will give us two main separate results: while the first will allow us to deeply and easily configure all the parameters of the APM:Rover firmware (apart from managing waypoints and flight plans), the second will be used to test and analyse the standard MAVLink messages sent and received. Regarding the “APM Mission Planner”, it is because of the importance of having a proper control of the aircraft (or any unmannedvehicle) while using the autopilot that the ArduPilot community has been working in designing their own ground station. This program allows you to operate in real-time with your aircraft, your multi-copter or your rover, and to configure everything in the APM board. The following are some of the major functionalities this software offers: