The Drive-Safe System (DSS) is a collision-avoidance system for power wheelchairs designed to support people with mobility impairments who also have visual, upper-limb, or cognitive impairments. The DSS uses a distributed approach to provide an add-on, shared-control, navigation-assistance solution. In this project, the DSS was tested for engineering goals such as sensor coverage, maximum safe speed, maximum detection distance, and power consumption while the wheelchair was stationary or driven by an investigator. Results indicate that the DSS provided uniform, reliable sensor coverage around the wheelchair; detected obstacles as small as 3.2 mm at distances of at least 1.6 m; and attained a maximum safe speed of 4.2 km/h. The DSS can drive reliably as close as 15.2 cm from a wall, traverse doorways as narrow as 81.3 cm without interrupting forward movement, and reduce wheelchair battery life by only 3%. These results have implications for a practical system to support safe, independent mobility for veterans who acquire multiple disabilities during Active Duty or later in life. These tests indicate that a system utilizing relatively low cost ultrasound, infrared, and force sensors can effectively detect obstacles in the vicinity of a wheelchair.
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http://dx.doi.org/10.1682/jrrd.2010.01.0008 | DOI Listing |
Sensors (Basel)
January 2025
Department of Product & Systems Design Engineering, University of the Aegean, 84100 Syros, Greece.
This paper addresses the complex problem of multi-goal robot navigation, framed as an NP-hard traveling salesman problem (TSP), in environments with both static and dynamic obstacles. The proposed approach integrates a novel path planning algorithm based on the Bump-Surface concept to optimize the shortest collision-free path among static obstacles, while a Genetic Algorithm (GA) is employed to determine the optimal sequence of goal points. To manage static or dynamic obstacles, two fuzzy controllers are developed: one for real-time path tracking and another for dynamic obstacle avoidance.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Automation and Electrical Engineering, Beihang University, Beijing 100191, China.
Since the field of autonomous vehicles is developing quickly, it is becoming increasingly crucial for them to safely and effectively navigate their surroundings to avoid collisions. The primary collision avoidance algorithms currently employed by self-driving cars are examined in this thorough survey. It looks into several methods, such as sensor-based methods for precise obstacle identification, sophisticated path-planning algorithms that guarantee cars follow dependable and safe paths, and decision-making systems that allow for adaptable reactions to a range of driving situations.
View Article and Find Full Text PDFFront Robot AI
January 2025
Institute of Automatic Control, Leibniz University Hannover, Hannover, Germany.
In this paper, we present a global reactive motion planning framework designed for robotic manipulators navigating in complex dynamic environments. Utilizing local minima-free circular fields, our methodology generates reactive control commands while also leveraging global environmental information from arbitrary configuration space motion planners to identify promising trajectories around obstacles. Furthermore, we extend the virtual agents framework introduced in Becker et al.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Diriyah, Riyadh, Saudi Arabia.
Reinforcement learning is a remarkable aspect of the artificial intelligence field with many applications. Reinforcement learning facilitates learning new tasks based on action and reward principles. Motion planning addresses the navigation problem for robots.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Seamless Trans-X Lab (STL), School of Integrated Technology, Yonsei University, Incheon 21983, Republic of Korea.
In the domain of autonomous driving, trajectory prediction plays a pivotal role in ensuring the safety and reliability of autonomous systems, especially when navigating complex environments. Unfortunately, trajectory prediction suffers from uncertainty problems due to the randomness inherent in the driving environment, but uncertainty quantification in trajectory prediction is not widely addressed, and most studies rely on deep ensembles methods. This study presents a novel uncertainty-aware multimodal trajectory prediction (UAMTP) model that quantifies aleatoric and epistemic uncertainties through a single forward inference.
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