The Quadruped Robot Uses the Trajectory Planned by DIACO to Complete the Obstacle Avoidance Task

Algorithm 1

Diffusion-improved ant colony optimization algorithm.

	Input:
	: Number of ants
	: The maximum number of iterations
	: Pheromone weight
	: Heuristic information weight
	: Grid map matrix containing obstacles
	Output:
	best_path: Coordinate information matrix on the optimal path
	% Main loop for the iterations
	for k = 1: K
	% Loop for each ant
	for m = 1: M
	% Initialize Tabu list
	TABUkm = ones(N)
	% Initialize path list
	DIACO = []
	while ∼ target_position(current_position)
	leave_pheromone(current_position, equation_7_parameters)
	% Add the current position to the path list and Tabu list
	DIACO = add_to_path(DIACO, current_position)
	TABUkm = update_tabu(TABUkm, current_position)
	% Calculate transition probabilities according to equations (1)–(6) transition_probabilities = calculate_transition_probabilities(current_position, equations_1_to_6_parameters)
	next_position = select_next_position(transition_probabilities)
	% Update the current position
	current_position = next_position
	end
	% Check if there are positions in the path that satisfies the diffusion threshold according to (18)
	if any_position_satisfies_diffusion_threshold(DIACO, equation_18_parameters)
	% Perform pheromone diffusion according to equations (9)–(17)
	Pheromone_update = perform_pheromone_diffusion (DIACO, equa-tions_9_to_17_parameters)
	% Local pheromone update
	Pheromone = leave_pheromone + Pheromone_update
	end
	% Check if all ants have completed the path search
	if all_ants_have_completed_path_search()
	% Check if this is the last iteration
	if k < K
	% Perform global pheromone update according to (8)
	perform_global_pheromone_update(equation_8_parameters)
	else
	% Return the best path and the best path curve
	[best_path] = get_best_path_and_curve(DIACO)
	return
	end
	end
	end
	end
	end