A local planner for Ackermann-driven vehicles in ROS SBPL

Limpert, Nicolas; Schiffer, Stefan; Ferrein, Alexander

doi:10.1109/robomech.2015.7359518

Cited by 7 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A global route is planned via a free-space planner like smac or map-based planner using a Lanelet2 HD map. The vehicle follows this path utilising a path-following module, including a feedback loop [34,35]. As path-following algorithm, we use a model-predictive controller (MPC) which uses a GPU-based grid-search on a set of predicted trajectories achievable by the vehicle's kinematic model.…”

Section: Software Architecturementioning

confidence: 99%

Controlling a Fleet of Autonomous LHD Vehicles in Mining Operation

Ferrein,

Nikolovski,

Limpert

et al. 2023

Multi-Robot Systems - New Advances

Self Cite

View full text Add to dashboard Cite

In this chapter, we report on our activities to create and maintain a fleet of autonomous load haul dump (LHD) vehicles for mining operations. The ever increasing demand for sustainable solutions and economic pressure causes innovation in the mining industry just like in any other branch. In this chapter, we present our approach to create a fleet of autonomous special purpose vehicles and to control these vehicles in mining operations. After an initial exploration of the site we deploy the fleet. Every vehicle is running an instance of our ROS 2-based architecture. The fleet is then controlled with a dedicated planning module. We also use continuous environment monitoring to implement a life-long mapping approach. In our experiments, we show that a combination of synthetic, augmented and real training data improves our classifier based on the deep learning network Yolo v5 to detect our vehicles, persons and navigation beacons. The classifier was successfully installed on the NVidia AGX-Drive platform, so that the abovementioned objects can be recognised during the dumper drive. The 3D poses of the detected beacons are assigned to lanelets and transferred to an existing map.

show abstract

Section: Software Architecturementioning

confidence: 99%

Controlling a Fleet of Autonomous LHD Vehicles in Mining Operation

Ferrein,

Nikolovski,

Limpert

et al. 2023

Multi-Robot Systems - New Advances

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, state-of-the-art global path criteria are traditionally limited to minimum-distance to the goal. The most popular are Dijkstra and A-star algorithms [5], and kinematics constrains through motion primitives such as Search-based Planning Library (SBPL) [11,12]. Although Dijkstra provides the optimal path distance, it's main drawback is the path search ability that becomes computationally heavy for large environment as it requires to explore the whole environment A-star was proposed as an improvement to cope with the computation time of Dijkstra by adding a heuristic term that indicates the closeness of each cell to the goal.…”

Section: Introductionmentioning

confidence: 99%

Toward Safer and Energy Efficient Global Trajectory Planning of Self-Guided Vehicles for Material Handling System in Dynamic Environment

et al. 2023

View full text Add to dashboard Cite

For a sustainable operation of multiple Self-Guided Vehicles (SGVs) in a dynamic manufacturing environment, it is essential to guarantee collision-free and efficient navigation to the autonomous mobile platforms and safety to the surrounding subjects. To prevent from navigation failures, an SGV must avoid conflicts that constrain itself to abruptly brake or stop to avoid collisions. These inefficient conflicts result from unexpected changes in the configuration space or due to nearby unforeseen obstacle. In this paper, a navigation approach is proposed to adapt the global trajectory in order to reduce conflict occurrence while limiting energy consumption of the mobile platform. To generate such trajectory, first the collision risks are characterized using an objective risk perception parameter, the Time-To-Collision TTC, that rely on the kinematics of the egoSGV and the neighboring obstacles. Next, weighted Kernel Density Estimation (wKDE) defines the spatial distribution of conflict severity in configuration space. The defined zones are incorporated as a conflict layer in the global map. Then, a global trajectory planner algorithm is used to weigh between the length cost and conflict cost. Finally, to test the proposed solution, a simulation is performed in a factory-like environment, then an experiment is conducted with a real SGV. In comparison with the state-of-the-art geometrical path planning method, the results show that the proposed approach reduces navigation failures by up to 52%, while reducing the trajectory execution time by around up to 10 %. Also, the smoothness of the executed motion allowed to reduce energy consumption by over 12%.

show abstract

“…The mainstream research of UMV systems focuses on the combined use of multiple sensors to exploit their complementary advantages and disadvantages. Potdar et al [18] proposed a method to accomplish localization, obstacle detection, and path planning in an Ackerman steering robot [19,20] using a single camera and LiDAR sensor; LiDAR data are transmitted over a wireless network to a computer using the TCP/IP protocol, and obstacle data from the robot are augmented with potential fields and combined with positional data from overhead cameras to construct cost maps for use in navigational algorithms. Four different search algorithms [21][22][23] were implemented for testing.…”

Section: Introductionmentioning

confidence: 99%

Ackerman Unmanned Mobile Vehicle Based on Heterogeneous Sensor in Navigation Control Application

Shih

Lin

Jhang

2023

Sensors

View full text Add to dashboard Cite

With the advancement of science and technology, the development and application of unmanned mobile vehicles (UMVs) have emerged as topics of crucial concern in the global industry. The development goals and directions of UMVs vary according to their industrial uses, which include navigation, autonomous driving, and environmental recognition; these uses have become the priority development goals of researchers in various fields. UMVs employ sensors to collect environmental data for environmental analysis and path planning. However, the analysis function of a single sensor is generally affected by natural environmental factors, resulting in poor identification results. Therefore, this study introduces fusion technology that employs heterogeneous sensors in the Ackerman UMV, leveraging the advantages of each sensor to enhance accuracy and stability in environmental detection and identification. This study proposes a fusion technique involving heterogeneous imaging and LiDAR (laser imaging, detection, and ranging) sensors in an Ackerman UMV. A camera is used to obtain real-time images, and YOLOv4-tiny and simple online real-time tracking are then employed to detect the location of objects and conduct object classification and object tracking. LiDAR is simultaneously used to obtain real-time distance information of detected objects. An inertial measurement unit is used to gather odometry information to determine the position of the Ackerman UMV. Static maps are created using simultaneous localization and mapping. When the user commands the Ackerman UMV to move to the target point, the vehicle control center composed of the robot operating system activates the navigation function through the navigation control module. The Ackerman UMV can reach the destination and instantly identify obstacles and pedestrians when in motion.

show abstract

A local planner for Ackermann-driven vehicles in ROS SBPL

Cited by 7 publications

References 15 publications

Controlling a Fleet of Autonomous LHD Vehicles in Mining Operation

Controlling a Fleet of Autonomous LHD Vehicles in Mining Operation

Toward Safer and Energy Efficient Global Trajectory Planning of Self-Guided Vehicles for Material Handling System in Dynamic Environment

Ackerman Unmanned Mobile Vehicle Based on Heterogeneous Sensor in Navigation Control Application

Contact Info

Product

Resources

About