Reinforcement and Curriculum Learning for Off-Road Navigation of an UGV with a 3D LiDAR

Sánchez, Manuel; Morales, José; Martínez, Jorge L.

doi:10.3390/s23063239

Cited by 8 publications

(6 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The computer of Andabata employs an inertial measurement unit (IMU), with inclinometers, gyroscopes, and a compass, and a global navigation satellite system (GNSS) receiver with a horizontal resolution of 1 m included in its onboard smartphone for outdoor localization [36]. The main exteroceptive sensor for navigation is a custom 3D LiDAR sensor with 360°field of view built by rotating a 2D LiDAR [37].…”

Section: Outdoor Navigationmentioning

confidence: 99%

“…Although waypoints for the UGV are calculated in the detected paths, reactivity is still necessary to avoid steep slopes and unexpected obstacles that are not visible on satellite images. Local navigation between distant waypoints has been implemented on Andabata with a previously developed actor-critic scheme, which was trained using reinforcement and curriculum learning [36].…”

Section: Outdoor Navigationmentioning

confidence: 99%

“…Basically, acquired 3D point clouds are employed to emulate a 2D traversability scanner, which produces 32 virtual levelled ranges up to 10 m around the vehicle (see Figure 19). These data, together with the heading error of the vehicle with respect to the current waypoint (p t ), are employed by the actor neural network to directly produce steering speed commands while moving at a constant longitudinal speed [36]. When the distance to the current waypoint (d t ) is less than 1 m, the next objective from the list is chosen.…”

Section: Outdoor Navigationmentioning

confidence: 99%

“…Figure19. Representation of a virtual 2D traversability scan for Andabata[36]. A nearby obstacle is shown in grey.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Waypoint Generation in Satellite Images Based on a CNN for Outdoor UGV Navigation

2023

Self Cite

View full text Add to dashboard Cite

Moving on paths or trails present in natural environments makes autonomous navigation of unmanned ground vehicles (UGV) simpler and safer. In this sense, aerial photographs provide a lot of information of wide areas that can be employed to detect paths for UGV usage. This paper proposes the extraction of paths from a geo-referenced satellite image centered at the current UGV position. Its pixels are individually classified as being part of a path or not using a convolutional neural network (CNN) which has been trained using synthetic data. Then, successive distant waypoints inside the detected paths are generated to achieve a given goal. This processing has been successfully tested on the Andabata mobile robot, which follows the list of waypoints in a reactive way based on a three-dimensional (3D) light detection and ranging (LiDAR) sensor.

show abstract

Section: Outdoor Navigationmentioning

confidence: 99%

Section: Outdoor Navigationmentioning

confidence: 99%

Section: Outdoor Navigationmentioning

confidence: 99%

“…Figure19. Representation of a virtual 2D traversability scan for Andabata[36]. A nearby obstacle is shown in grey.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Waypoint Generation in Satellite Images Based on a CNN for Outdoor UGV Navigation

2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…Less common, but more relevant from that perspective, especially as computation capabilities increase, is the possibility of having deformable environmental objects and terrain [ 14 ]. Conducted analyses have shown that there are several simulators which offer extensive use case possibilities [ 15 , 16 , 17 , 18 ]. A solution commonly used in research is the MuJoCo [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

A Concept of a Plug-In Simulator for Increasing the Effectiveness of Rescue Operators When Using Hydrostatically Driven Manipulators

Typiak

2024

Sensors

View full text Add to dashboard Cite

The introduction of Unmanned Ground Vehicles (UGVs) into the field of rescue operations is an ongoing process. New tools, such as UGV platforms and dedicated manipulators, provide new opportunities but also come with a steep learning curve. The best way to familiarize operators with new solutions are hands-on courses but their deployment is limited, mostly due to high costs and limited equipment numbers. An alternative way is to use simulators, which from the software side, resemble video games. With the recent expansion of the video game engine industry, currently developed software becomes easier to produce and maintain. This paper tries to answer the question of whether it is possible to develop a highly accurate simulator of a rescue and IED manipulator using a commercially available game engine solution. Firstly, the paper describes different types of simulators for robots currently available. Next, it provides an in-depth description of a plug-in simulator concept. Afterward, an example of a hydrostatic manipulator arm and its virtual representation is described alongside validation and evaluation methodologies. Additionally, the paper provides a set of metrics for an example rescue scenario. Finally, the paper describes research conducted in order to validate the representation accuracy of the developed simulator.

show abstract

Low Computational Cost for Multiple Waypoints Trajectory Planning: A Time‐Optimal‐Based Approach

Lin‐Yang,

Pastor,

García‐Cerezo

2024

Advanced Intelligent Systems

View full text Add to dashboard Cite

In the field of mobile robots, achieving minimum time in executing trajectories is crucial for applications like delivery, inspection, and search and rescue. In this article, a novel time‐optimal planner based on optimization methods is introduced. Despite the high computational cost associated with such methods, the solution calculates time‐optimal multi‐waypoint trajectories, achieving results in the order of milliseconds. The proposed method formulates a time‐optimal trajectory using the Pontryagin's maximum principle as a policy. By utilizing a point mass model, the planner generates trajectories that are adaptable to different robot models. The approach incorporates a definition of a search space to guarantee convergence while considering the system limits. Simulation and real‐world experiments are performed to validate the feasibility of our method with different configurations. Simulation results compared to a benchmark method demonstrate our approach's superior performance in terms of computational time, achieving near‐optimal solutions. In addition, in the real‐world experiments, the integration of the method into practical applications is validated.

show abstract

Reinforcement and Curriculum Learning for Off-Road Navigation of an UGV with a 3D LiDAR

Cited by 8 publications

References 37 publications

Waypoint Generation in Satellite Images Based on a CNN for Outdoor UGV Navigation

Waypoint Generation in Satellite Images Based on a CNN for Outdoor UGV Navigation

A Concept of a Plug-In Simulator for Increasing the Effectiveness of Rescue Operators When Using Hydrostatically Driven Manipulators

Low Computational Cost for Multiple Waypoints Trajectory Planning: A Time‐Optimal‐Based Approach

Contact Info

Product

Resources

About