Long Beach police have arrested a man who was allegedly driving under the influence of alcohol when his car struck and killed a pedestrian in a wheelchair late Saturday. Joseph Maez, 26, of Long Beach was arrested and booked on suspicion of driving under the influence of alcohol and gross vehicular manslaughter while intoxicated, police said. He was being held on $100,000 bail. Responding to a crash about 11:19 p.m., officers attempted lifesaving measurers on the pedestrian until Long Beach Fire Department personnel arrived and continued efforts to revive him. The pedestrian died at the scene.
Most microscopic pedestrian navigation models use the concept of "forces" applied to the pedestrian agents to replicate the navigation environment. While the approach could provide believable results in regular situations, it does not always resemble natural pedestrian navigation behaviour in many typical settings. In our research, we proposed a novel approach using reinforcement learning for simulation of pedestrian agent path planning and collision avoidance problem. The primary focus of this approach is using human perception of the environment and danger awareness of interferences. The implementation of our model has shown that the path planned by the agent shares many similarities with a human pedestrian in several aspects such as following common walking conventions and human behaviours.
The government and manufacturers are promoting electric wheelchairs as a means of transportation to replace automobiles at a time when elderly people are increasingly returning their driver's licenses. Suzuki Motor Corp. is one of the country's largest makers of electric wheelchairs. Its mainstay ET4D model, priced at ¥368,000, has advanced safety functions and it can even give verbal instructions when it approaches a steep slope, according to the automaker. The ET4D also features a large basket and a range of as much as 31 km after a full charge, according to the company. Startup Whill Inc. sells electric wheelchairs controlled by hand levers.
We have deployed a fleet of robots that pickup and deliver items requested by users in an office building. Users specify time windows in which the items should be picked up and delivered, and send in requests online. Our goal is to form a schedule which picks up and delivers the items as quickly as possible at the lowest cost. We introduce an auction-based scheduling algorithm which plans to transfer items between robots to make deliveries more efficiently. The algorithm can obey either hard or soft time constraints. We discuss how to replan in response to newly requested items, cancelled requests, delayed robots, and robot failures. We demonstrate the effectiveness of our approach through execution on robots, and examine the effect of transfers on large simulated problems.
Battery life is yet one of the main limiting factors to a robot's total mission time, and efficient energy management is paramount in a robotic application. In this paper, we integrate energy awareness in the path planning of a mobile robot performing autonomous navigation. Our contributions are: 1) The formalization of a planning domain for mobile robot path planning which accounts for energy consumption and integrates energy actions in the generated plans; 2) A proof of concept of automatic path planning that avoids high energy areas in a known environment. We test our approach in simulation, extending an embedded computer's total battery discharge time by approximately 42.8%, and in a real ground mobile robot, achieving a mean energy draw reduction of 52.02%, both compared to conventional path planning.