Abstract: This paper mainly studies the path planning method based on ant colony optimization (ACO), and proposes heuristic integration ant colony optimization (HIACO). This paper not only analyzes and optimizes the principle, but also simulates and analyzes the parameters related to the application of HIACO in path planning. Compared with the original algorithm, the improved algorithm optimizes probability formula, tabu table mechanism and updating mechanism, and introduces more reasonable heuristic factors. The optimized HIACO not only draws on the excellent ideas of the original algorithm, but also solves the problems of premature convergence, convergence to the sub optimal solution and improper exploration to some extent. HIACO can be used to achieve better simulation results and achieve the desired optimization. Combined with the probability formula and update formula, several parameters of HIACO are tested. This paper proves the principle of the HIACO and gives the best parameter range in the research of path planning.
Abstract: Harmonic functions are solutions to Laplace’s equation
that are known to have an advantage as a global approach in providing
the potential values for autonomous vehicle navigation. However,
the computation for obtaining harmonic functions is often too slow
particularly when it involves very large environment. This paper
presents a two-stage iterative method namely Modified Arithmetic
Mean (MAM) method for solving 2D Laplace’s equation. Once
the harmonic functions are obtained, the standard Gradient Descent
Search (GDS) is performed for path finding of an autonomous vehicle
from arbitrary initial position to the specified goal position. Details
of the MAM method are discussed. Several simulations of vehicle
navigation with path planning in a static known indoor environment
were conducted to verify the efficiency of the MAM method. The
generated paths obtained from the simulations are presented. The
performance of the MAM method in computing harmonic functions
in 2D environment to solve path planning problem for an autonomous
vehicle navigation is also provided.
Abstract: In this paper a real-time trajectory generation algorithm for computing 2-D optimal paths for autonomous aerial vehicles has been discussed. A dynamic programming approach is adopted to compute k-best paths by minimizing a cost function. Collision detection is implemented to detect intersection of the paths with obstacles. Our contribution is a novel approach to the problem of trajectory generation that is computationally efficient and offers considerable gain over existing techniques.
Abstract: Based on a non-linear single track model which
describes the dynamics of vehicle, an optimal path planning strategy
is developed. Real time optimization is used to generate reference
control values to allow leading the vehicle alongside a calculated lane
which is optimal for different objectives such as energy consumption,
run time, safety or comfort characteristics. Strict mathematic
formulation of the autonomous driving allows taking decision on
undefined situation such as lane change or obstacle avoidance. Based
on position of the vehicle, lane situation and obstacle position, the
optimization problem is reformulated in real-time to avoid the
obstacle and any car crash.
Abstract: This paper presents a constrained valley detection
algorithm. The intent is to find valleys in the map for the path planning
that enables a robot or a vehicle to move safely. The constraint to the
valley is a desired width and a desired depth to ensure the space for
movement when a vehicle passes through the valley. We propose an
algorithm to find valleys satisfying these 2 dimensional constraints.
The merit of our algorithm is that the pre-processing and the
post-processing are not necessary to eliminate undesired small valleys.
The algorithm is validated through simulation using digitized
elevation data.
Abstract: A novel path planning approach is presented to solve
optimal path in stochastic, time-varying networks under priori traffic
information. Most existing studies make use of dynamic programming
to find optimal path. However, those methods are proved to
be unable to obtain global optimal value, moreover, how to design
efficient algorithms is also another challenge.
This paper employs a decision theoretic framework for defining
optimal path: for a given source S and destination D in urban transit
network, we seek an S - D path of lowest expected travel time
where its link travel times are discrete random variables. To solve
deficiency caused by the methods of dynamic programming, such as
curse of dimensionality and violation of optimal principle, an integer
programming model is built to realize assignment of discrete travel
time variables to arcs. Simultaneously, pruning techniques are also
applied to reduce computation complexity in the algorithm. The final
experiments show the feasibility of the novel approach.