Fix DWA #95

wowthecoder · 2026-01-23T23:33:10Z

Revamp the DWA algorithm

Collision Detection

Changed the collision detection area from circular to rectangular. The collision box has dynamic length at the front depending on how fast the robot is moving. Added optional config show_debug_rectangles to draw the boxes.
The intersection area of the collision boxes is used to calculate the obstacle penalty. The penalty is exponential the closer larger the intersection area of the boxes. This is then used to lower the score of that motion segment.

Central loop logic in `plan_local()`:

I read the Tigers Mannheim TDP. They are using something similar to DWA, but they sample their trajectories randomly. In contrast our current approach samples from 8 fixed angles and 3 fixed speed scales (8 * 3 = 24 trajectories). I changed it to try with the target-facing angle first, if there's no collision just use this angle. If there is collision, try out 5 other random angles at 3 random speed scales (linearly decreases from 1.0 at 4m distance to 0.05). This optimised the FPS slightly and it's a bit faster (cuz there's less trajectories to evaluate score for).
I found that the changed speed scales are what stopped the overshooting, because before this we did not clamp the max scale (1.0) according to distance.

Score function of motion segment

Use 3 factors to score a segment: distance to target(+), speed of robot(+), and obstacle presence(-). The final score is the weighted sum of these 3 factors.
Bumping the obstacle factor weight from 1.0 to 1.5 did magic for obstacle avoidance. Also fine tuned the other 2 weights so that their scales match, and target proximity contributes the most to the score.

Cleanup

Removed segment_overshoots_target(), segment_too_close() and other ineffective functions.

Minor changes to the Motion planning tests:

Make the random movement tests have 6 robots to stress test obstacle avoidance
Standardise endpoint tolerance (how close the robots have to be to the target position to pass the tests) to 0.1m

it runs in sim but the robots are undergoing brownian motion

…oller for opponents for testing

…and normalized the different factors for scores

…test to use 6 robots

…olerance in tests

wowthecoder · 2026-01-23T23:36:25Z

The failing CI test is due to the new tests merged from main, that doesnt concern this PR

Copilot

Pull request overview

This PR revamps the Dynamic Window Approach (DWA) algorithm for robot motion planning by introducing several significant improvements to collision detection, trajectory sampling, and scoring.

Changes:

Replaced circular collision detection with rectangular bounding boxes that dynamically adjust based on robot velocity
Changed from fixed-angle trajectory sampling (24 trajectories) to adaptive random sampling with target-facing angle prioritization
Redesigned scoring function with three weighted factors (target progress, speed, obstacle penalty) and tuned weights for better obstacle avoidance
Standardized test endpoint tolerance to 0.1m across all motion planning tests and increased stress testing to 6 robots
Changed default control scheme from PID to DWA in StrategyRunner

Reviewed changes

Copilot reviewed 9 out of 9 changed files in this pull request and generated 9 comments.

Show a summary per file

File	Description
utama_core/motion_planning/src/dwa/planner.py	Core implementation of new oriented rectangle collision detection, random trajectory sampling, and updated scoring function
utama_core/motion_planning/src/dwa/config.py	Updated configuration with new weight parameters and removed obsolete safety radius settings
utama_core/motion_planning/src/dwa/translation_controller.py	Added debug visualization for target positions
utama_core/run/strategy_runner.py	Changed default control scheme from "pid" to "dwa"
utama_core/tests/motion_planning/random_movement_test.py	Increased robot count to 6 and added collision-free initial placement logic
utama_core/tests/motion_planning/strategies/random_movement_strategy.py	Fixed circular import with TYPE_CHECKING pattern
utama_core/tests/motion_planning/single_robot_static_obstacle_test.py	Standardized endpoint tolerance to 0.1m
utama_core/tests/motion_planning/single_robot_moving_obstacle_test.py	Standardized endpoint tolerance to 0.1m
utama_core/tests/motion_planning/multiple_robots_test.py	Standardized endpoint tolerance to 0.1m

Copilot · 2026-01-23T23:38:16Z

utama_core/motion_planning/src/dwa/planner.py

        obstacles = temporary_obstacles or []
        return self._plan_local(game, robot, target, obstacles)


The temporary_obstacles parameter is documented and passed to _plan_local, but is never actually used in the implementation. The parameter is assigned to a local variable obstacles on line 234 but this variable is not referenced anywhere in the _plan_local method. Either implement support for temporary obstacles or remove this parameter from the API to avoid misleading users of this method.

Copilot · 2026-01-23T23:38:16Z

utama_core/motion_planning/src/dwa/planner.py

+        self._w_goal = getattr(self._config, "weight_goal")
+        self._w_obstacle = getattr(self._config, "weight_obstacle")
+        self._w_speed = getattr(self._config, "weight_speed")


Using getattr here is fragile and unnecessary since the config attributes are defined in the dataclass. If an attribute is missing, this will raise an AttributeError at runtime. Access these attributes directly (e.g., self._config.weight_goal) which provides better type safety and clearer error messages if the attribute is missing.

Suggested change

self._w_goal = getattr(self._config, "weight_goal")

self._w_obstacle = getattr(self._config, "weight_obstacle")

self._w_speed = getattr(self._config, "weight_speed")

self._w_goal = self._config.weight_goal

self._w_obstacle = self._config.weight_obstacle

self._w_speed = self._config.weight_speed

Copilot · 2026-01-23T23:38:17Z

utama_core/tests/motion_planning/random_movement_test.py

+                    break
+            else:
+                raise RuntimeError(
+                    "Unable to find non-colliding start positions; " "consider widening bounds or reducing robot count."


The string concatenation with a space between the two parts creates an unnecessary mid-sentence space. Combine these into a single string or ensure the concatenation is intentional.

Suggested change

"Unable to find non-colliding start positions; " "consider widening bounds or reducing robot count."

"Unable to find non-colliding start positions; consider widening bounds or reducing robot count."

Copilot · 2026-01-23T23:38:17Z

utama_core/run/strategy_runner.py

        field_bounds: FieldBounds = Field.FULL_FIELD_BOUNDS,
        opp_strategy: Optional[AbstractStrategy] = None,
-        control_scheme: str = "pid",  # This is also the default control scheme used in the motion planning tests
+        control_scheme: str = "dwa",  # This is also the default control scheme used in the motion planning tests


Changing the default control scheme from "pid" to "dwa" is a significant behavioral change that affects all code using StrategyRunner without explicitly specifying a control_scheme. This could have wide-reaching implications beyond the motion planning tests. Consider whether this should be done in a separate PR with broader testing, or ensure that all affected callers are identified and tested.

Copilot · 2026-01-23T23:38:17Z

utama_core/motion_planning/src/dwa/planner.py

+                # This should be commented out in real execution to speed up further
+                self._draw_collision_box(obstacle_safety_rect, "red" if intersection_ratio > 0.5 else "orange", 2)
+
+            else:
+                # Calculate distance between rectangles
+                dist = our_safety_rect.distance_to(obstacle_safety_rect)
+
+                # Apply penalty based on proximity
+                # Close but not intersecting still gets some penalty
+                if dist < 0.5:  # Within 0.5m
+                    # Exponential decay: closer = higher penalty
+                    # dist=0.0m: penalty ≈ 1.0
+                    # dist=0.2m: penalty ≈ 0.45
+                    # dist=0.5m: penalty ≈ 0.08
+                    penalty = 1.0 * math.exp(-4.0 * dist)
+                    obstacle_factor = max(obstacle_factor, penalty)
+
+                # This should be commented out in real execution to speed up further
+                self._draw_collision_box(obstacle_safety_rect, "green", 1)


For better performance, check self._config.show_debug_rectangles before calling _draw_collision_box rather than checking inside the function. This avoids function call overhead when debugging is disabled. Consider wrapping these calls with if self._config.show_debug_rectangles: to short-circuit when the flag is False.

Copilot · 2026-01-23T23:38:17Z

utama_core/motion_planning/src/dwa/planner.py

-    ObstacleRegion,
-    to_rectangles,
-)
+from utama_core.motion_planning.src.planning.geometry import point_segment_distance


The point_segment_distance function is imported but never used in this file. This import can be removed to keep the code clean.

Copilot · 2026-01-23T23:38:18Z

utama_core/motion_planning/src/dwa/config.py

+    weight_goal = 50.0
+    weight_obstacle = 1.5  # Increased to match new (0-10) obstacle cost scale
+    weight_speed = 1.0
+    show_debug_rectangles = False  # Enable visualization of bounding boxes for testing


These dataclass fields are missing type annotations. Add explicit type annotations for consistency with the other fields in this class. For example: weight_goal: float = 50.0, weight_obstacle: float = 1.5, weight_speed: float = 1.0, and show_debug_rectangles: bool = False.

Suggested change

weight_goal = 50.0

weight_obstacle = 1.5 # Increased to match new (0-10) obstacle cost scale

weight_speed = 1.0

show_debug_rectangles = False # Enable visualization of bounding boxes for testing

weight_goal: float = 50.0

weight_obstacle: float = 1.5 # Increased to match new (0-10) obstacle cost scale

weight_speed: float = 1.0

show_debug_rectangles: bool = False # Enable visualization of bounding boxes for testing

Copilot · 2026-01-23T23:38:18Z

utama_core/motion_planning/src/dwa/planner.py

+                    score = self._evaluate_segment(game, robot, segment_start, segment_end, target)
+
+                    if score > best_score:
+                        best_score = score
+                        best_move = segment_end


In the secondary trajectory sampling loop (when best_collides is True), collision checking is not performed before updating the best move. This is inconsistent with the first loop (lines 269-286) which checks collisions. While the _evaluate_segment function does apply obstacle penalties that should discourage colliding trajectories, the logic should be consistent. Consider either: (1) also calling _segment_collides in this loop and tracking collision state, or (2) removing the explicit collision check from the first loop if the obstacle penalties in scoring are sufficient.

Suggested change

score = self._evaluate_segment(game, robot, segment_start, segment_end, target)

if score > best_score:

best_score = score

best_move = segment_end

score = self._evaluate_segment(game, robot, segment_start, segment_end, target)

collides = self._segment_collides(game, robot, segment_start, segment_end)

if score > best_score:

best_score = score

best_move = segment_end

best_collides = collides

Copilot · 2026-01-23T23:38:18Z

utama_core/motion_planning/src/dwa/planner.py

@@ -1,25 +1,157 @@
 import copy


The copy module is imported but never used in this file. This import can be removed to keep the code clean.

Jawad12256 and others added 16 commits November 19, 2025 14:25

the dwa is cooked

0ee5652

it runs in sim but the robots are undergoing brownian motion

Merge branch 'main' into dwa-new

bc68db2

Merge branch 'main' into dwa-new

0f4327b

test: write simple straight line test

ad2bd95

test: uncomment straight line test with obstacles

855fb39

test: moving robots obstacle course, also added separate motion contr…

7be09fa

…oller for opponents for testing

test: implement unit test of 12 robots dashing straight at each other

c2a697a

test: implement test with 6 robots moving randomly in a half court

c75f02a

feat: replace circular safety radius with rectangular bounding boxes …

ebafe76

…and normalized the different factors for scores

Merge branch 'main' into dwa-new

7db0427

Remove debug logging

08cd297

WIP: less oscillation and no hesitation but still overshooting a little

7797e58

test: delete duplicate test file for single robot and updated random …

7580aee

…test to use 6 robots

feat: completely fixed overshooting my changing the speed scale

fdeb8c8

Chore: clean up code, fine tune parameters and standardise endpoint t…

f56ad9f

…olerance in tests

Merge branch 'main' into dwa-new

58c6130

Copilot AI review requested due to automatic review settings January 23, 2026 23:33

Copilot started reviewing on behalf of wowthecoder January 23, 2026 23:33 View session

Copilot AI reviewed Jan 23, 2026

View reviewed changes

chore: reduce target tolerance to pass tests

3e402ba

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Fix DWA #95

Fix DWA #95

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wowthecoder commented Jan 23, 2026

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		obstacles = temporary_obstacles or []
		return self._plan_local(game, robot, target, obstacles)

	"Unable to find non-colliding start positions; " "consider widening bounds or reducing robot count."
	"Unable to find non-colliding start positions; consider widening bounds or reducing robot count."

Fix DWA #95

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Fix DWA #95

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wowthecoder commented Jan 23, 2026

Revamp the DWA algorithm

Collision Detection

Central loop logic in plan_local():

Score function of motion segment

Cleanup

Minor changes to the Motion planning tests:

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Pull request overview

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Central loop logic in `plan_local()`: