Motion Planning of Autonomous Vehicles on a Dual Carriageway without Speed Lanes

The problem of motion planning of an autonomous vehicle amidst other vehicles on a straight road is considered. Traffic in a number of countries is unorganized, where the vehicles do not move within predefined speed lanes. In this paper, we formulate a mechanism wherein an autonomous vehicle may travel on the “wrong” side in order to overtake a vehicle. Challenges include assessing a possible overtaking opportunity, cooperating with other vehicles, partial driving on the “wrong” side of the road and safely going to and returning from the “wrong” side. The experimental results presented show vehicles cooperating to accomplish overtaking manoeuvres

Coordinated Navigation of Multiple Independent Disk-Shaped Robots

This paper addresses the coordinated navigation of multiple independently actuated disk-shaped robots-all placed within the same disk-shaped workspace. Assuming perfect sensing, shared-centralized communications and computation, as well as perfect actuation, we encode complete information about the goal, obstacles, and workspace boundary using an artificial potential function over the configuration space of the robots’ simultaneous nonoverlapping positions. The closed-loop dynamics governing the motion of each (velocity-controlled) robot take the form of the appropriate projection of the gradient of this function. We impose (conservative) restrictions on the allowable goal positions that yield sufficient conditions for convergence: We prove that this construction is an essential navigation function that guarantees collision-free motion of each robot to its destination from almost all initial free placements. The results of an extensive simulation study investigate practical issues such as average resulting trajectory length and robustness against simulated sensor noise. For more information: Kod*La

Motion of Mobile Robots in Environments with Dynamic Obstacles and Arbitrary Directions

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordThis paper presents an improved study on the motion of mobile robots with dynamic obstacle environments and arbitrary directions. This study focuses on incorporating the concept of inertia into the movement of obstacles to enhance the capabilities of mobile robots in complex environments. Unlike random movements, the obstacles in this study possess inertia, which constrains their motion in predictable patterns. This inertia can be learned or predicted by the robot, enabling it to better anticipate the obstacle positions. This research employs a grid-based simulation environment with systematically moving obstacles. By considering inertia, the robot gains the ability to understand and leverage the predictable aspects of obstacle motion, resulting in improved navigation performance. The robot can predict obstacle trajectories more effectively, reducing the likelihood of collisions and increasing overall efficiency by using the velocity obstacle algorithm. By incorporating inertia into the movement of obstacles, the robot gains valuable insights that enable it to plan its movements more intelligently. Incorporating inertia as a factor in obstacle motion contributes to a more systematic and predictable environment, allowing the robot to make informed decisions based on the anticipated positions of both fixed and moving obstacles. This research opens up possibilities for further advancements in mobile robot navigation in complex environments and dynamic scenarios

Decentralized prioritized planning in large multirobot teams

In this paper, we address the problem of distributed path planning for large teams of hundreds of robots in constrained environments. We introduce two distributed prioritized planning algorithms: an efficient, complete method which is shown to converge to the centralized prioritized planner solution, and a sparse method in which robots discover collisions probabilistically. Planning is divided into a number of iterations, during which every robot simultaneously and independently computes a planning solution based on other robots' path information from the previous iteration. Paths are exchanged in ways that exploit the cooperative nature of the team and a statistical phenomenon known as the "birthday paradox". Performance is measured in simulated 2D environments with teams of up to 240 robots. We find that in moderately constrained environments, these methods generate solutions of similar quality to a centralized prioritized planner, but display interesting communication and planning time characteristics