When Your Navigation Field Collapses at Boundaries: Avoiding 4 Edge-Effect Errors

You've seen it happen. A robot glides smoothly through open space, then hits a wall—and suddenly its behavior turns erratic. The navigation field collapses. Edges break everything.

For anyone building autonomous systems with vector field navigation, boundary errors are the silent killers. They don't show up in simulation until you add obstacles. They don't manifest until the robot is inches from a wall. By then, you're troubleshooting a crash, not a field. This article walks through four specific edge-effect errors and how to sidestep them—without rewriting your entire framework.

Who Needs to Make This Decision—And When?

Autonomy engineers at the integration stage

You're three weeks from a hardware demo. The field looks beautiful in simulation—curl-free, smooth gradients, perfect convergence. Then you run it on the real robot, and it slams into a wall. Or drifts. Or simply refuses to move near the boundary. This is the moment edge effects announce themselves: not as a theoretical footnote, but as a stalled motor. The decision maker here is the integration engineer who owns the pipeline from field computation to motor commands. You don't have time to rewrite the planner. What you have is a seam that must be sealed before Friday.

Robotics students debugging a thesis project

The catch is—students often discover boundary collapse late. They spend months proving convergence in unbounded domains, then slap a rectangular arena on the test rig and watch the controller oscillate. I have seen this wreck two defenses. The student knows the math but not the material consequence: a field that fails at the boundary fails the whole system. The timeline is cruel—you can't defer boundary treatment to the last chapter. You need to test with physical constraints before you freeze the field algorithm, not after. That means running edge-case scenarios by week six of the project, not week fourteen.

Most teams skip this step. They assume the vector field degrades gracefully near the wall. It doesn't.

‘We had perfect coverage in the interior. The robot just stopped a foot from the wall. We thought it was a sensor glitch. It was the field collapsing to zero.’

— PhD candidate, personal conversation after a demo failure

Startup CTOs choosing between field approaches

You're comparing harmonic functions against potential fields or navigation functions. The papers show smooth flows. The benchmarks ignore the boundary strip. The odd part is—your product has to work inside a warehouse, not an infinite plane. The decision point is not academic: it happens when you commit to a field type during the architecture review. If you pick a method that treats boundaries as afterthoughts, you will later bolt on repulsive terms that fight your main field. That hurts. You get oscillations, dead zones, or a robot that hugs walls at unsafe speeds.

The timeline here is compressed: prototype in three weeks, alpha in eight. You don't have room to pivot field families mid-cycle. So the question becomes: which method has boundary handling baked in, not patched on? Wrong order.

The truth is—the decision to address edge effects must happen before you tune gains, before you validate coverage, certainly before you generate the first publication graphic. Once the field is baked into state estimation loops, changing the boundary treatment requires retuning everything. I fixed this once by swapping from a naive repulsive field to a harmonic interpolant at the boundary layer. It took one afternoon of code and two weeks of re-tuning the low-level controller. That's the real cost of deferring the edge-effect decision.

So ask yourself: are you the engineer, the student, or the CTO? And is it already too late to choose cleanly?

Three Paths to Handle Boundary Collapse

Potential field with boundary repulsion

You surround your workspace with virtual walls — soft repulsive forces that push the robot away before it touches real geometry. I have seen teams code this in an afternoon. The math is cheap: distance to nearest boundary, invert it, add a gain. That sounds fine until you tune the repulsion radius too aggressively and your agent refuses to approach any constraint — even the ones it must cross to reach a goal. The pitfall is phantom obstacles. A narrow hallway becomes impassable because both walls push inward harder than your attractive field can overcome. We fixed one warehouse bot by dropping repulsion gain to 0.3 inside corridors and ramping it only near dead-end edges. Still, you can't avoid the fundamental tension: repulsive fields guarantee no collision, but they also guarantee no tight passage. The trade-off lives entirely in your gain schedule — and schedules break when boundary geometry changes shape mid-route.

Harmonic function interpolation

Solve Laplace’s equation over your domain. The field becomes a smooth, boundary-aware flow that never creates local minima — no false sinks, no oscillating vectors at corners. The catch is compute cost. Real-time harmonic solutions on a changing grid? That eats cycles. Teams often precompute for static maps, then freeze the field when obstacles move. I watched a logistics prototype stall for 400 milliseconds mid-navigation because the solver re-crunched after a pallet was dropped. You lose a day debugging that lag. The bigger edge-effect problem emerges at non-convex boundaries: concave pockets can still trap flow lines, especially when your finite-difference grid is too coarse near the boundary.

'Harmonic fields feel mathematically pristine — until you run them on a microcontroller that can't finish one iteration before the next sensor scan arrives.'

— A sterile processing lead, surgical services

— field robotics engineer, during a post-mortem on a warehouse edge-collapse incident

Not every geographical checklist earns its ink.

What usually breaks first is the boundary condition specification. Neumann vs. Dirichlet — pick wrong and your vectors parallel the wall instead of moving along it, creating a slip zone that the planner treats as free space. That hurts.

Hybrid switching and learning-based fields

Detect the boundary region in state space, then swap to a different navigation law — a local vector correction trained on edge-case trajectories. The idea is pragmatic: keep your smooth fast field for open areas, snap to a safety-first sub-field when the robot drifts within one meter of any boundary. Wrong order matters here. If you train the switching classifier only on straight corridors, it fails on the first L-shaped corner. We saw a mobile manipulator lurch sideways because the learned correction field predicted a repulsion vector that pointed straight into the wall — the neural net had never seen a concave corner during training. The hybrid approach gives you the best of both worlds if you test on pathological boundaries. The trade-off: two separate fields to maintain, a switch controller that can oscillate near the threshold, and no guarantee the learned module generalizes to novel edge geometries. That's a lot of complexity to avoid one repulsive gain schedule. Yet for environments where boundaries shift daily — think construction sites — a static harmonic map fails and a pure repulsion field chatters. Hybrid buys you adaptability. The question is whether your team can debug two field types before the next deployment deadline.

What Matters Most When Comparing Methods

Field continuity at the boundary

You have a vector field that behaves beautifully in open space—smooth gradients, no singularities, every arrow points where it should. Then a robot touches the boundary and the field snaps. Not a gentle roll-off, but a hard discontinuity that sends the controller into a transient spike. I have seen this kill a quadrotor test mid-flight. The first criterion, then, is whether your chosen method preserves at least C¹ continuity across the boundary edge. Evaluate this by injecting a synthetic trajectory that skims the perimeter at a grazing angle. Plot the commanded acceleration directly. If it jumps by more than 30% between adjacent time steps, your field is not continuous—and your motors will chatter. The odd part is, many engineers check continuity only at the cell center, where the field looks fine. The seam is at the cell face.

But continuity alone is a trap. A field can be perfectly smooth yet completely useless because the boundary handler introduces a false local minimum—a phantom attractor that drags the agent into the wall. That sounds fine until your drone parks itself against the fence because the gradient points inward everywhere except at that one cell vertex. Most teams skip this: they test with a single obstacle boundary, never a concave corner where two walls meet. Pro tip: feed your field a step input starting exactly on the boundary edge. Does the solver diverge? Does it converge to a point outside the allowed region? If yes, your continuity metric passes but your method fails.

Computational cost per control loop

You run at 100 Hz. That leaves you roughly 10 milliseconds to compute the vector from sensor readings, apply the boundary correction, filter, and output a command. A reflection-based edge fix might cost you an extra vector reflection and a sign flip—under 5 microseconds. A distance-field interpolation from a precomputed lookup table? That can blow through 2–3 milliseconds if your grid resolution is high and you double the dimension for each boundary condition. The catch is that people optimize for average latency but ignore the worst-case spike. When the robot enters a narrow corridor near the boundary, the boundary-finding routine may fall back to a brute-force nearest-neighbor search. I fixed a system once where the control loop averaged 2.1 ms but hit 18 ms on every tenth cycle—exactly when the robot passed near a wall seam. The field collapsed at the boundary, the timeout fired, and the safety pilot took over. Wrong order: pick a method whose worst-case latency fits under your deadline, not one whose average looks good on a spreadsheet.

We also need to ask: does the boundary correction reuse data structures already in memory? The best methods piggyback on the existing vector-field evaluation—same grid, same gradient stencil—and add a single boundary distance check. Methods that require building a separate signed-distance field or a Delaunay triangulation of the boundary geometry double your memory and cache miss rate. That hurts on embedded hardware. Benchmark with your actual target platform: a desktop simulation that runs at 2,000 Hz tells you nothing about a Cortex-M4 that can barely manage 60 Hz with interrupts.

Robustness to sensor noise

Your lidar returns a point cloud. Every range measurement carries jitter. The boundary handler that works perfectly in simulation with a clean mesh will fail when the wall a corner moves by ±3 cm frame to frame. What matters here is whether your method uses a soft boundary influence or a hard clipping function. Soft approaches—like a distance-weighted field blending—damp the effect of noise naturally. Hard clipping methods snap the vector to zero or reflect it. The moment the boundary detection flickers, the vector jumps. That jump propagates into the integrator and produces a control oscillation. One concrete scene: we had an outdoor rover driving along a retaining wall. GPS noise made the boundary distance estimate jump by 15 cm. The field clipped, the rover swerved, the wall ended up in the dead zone, and the field lost all direction. A 10-second test turned into a manual recovery.

“A perfectly smooth boundary handler that relies on a brittle distance estimate is just a sharp edge disguised as a smooth curve.”

— Lead control engineer, field robotics team, after a crash post-mortem

The trade-off is unavoidable: soft blending buys noise immunity at the cost of allowing some penetration past the boundary. Hard clipping keeps the agent strictly inside but chatters under noise. To evaluate robustness, inject Gaussian noise into the boundary-distance input with a standard deviation equal to 10% of your safety margin. Run 1,000 Monte Carlo steps. Count how many times the output vector exceeds your commanded acceleration limit. If your method flinches more than once in ten thousand steps, it's not noise-robust—pick again. The final check: does your approach degrade gracefully as noise increases, or does it have a cliff? A method that slowly loses accuracy is fixable. A method that suddenly inverts the field direction is a wreck. Know which one you're shipping.

Trade-Offs: Smoothness vs. Safety vs. Speed

Potential field: simple but prone to local minima

The simplest fix—inflating the potential field near boundaries—feels like a no-brainer. You repulse the agent from the wall, tune a gain, and call it done. I have seen teams ship prototypes in two hours using this approach. The catch? That same simplicity creates dead zones. Drop a concave corner into the field and the gradient vanishes; the robot parks itself a foot from the wall, unable to decide which way is out. The trade-off is brutal: smooth motion inside the corridor comes at the cost of tiny, invisible traps at every inward bend. You trade safety for a hidden failure mode that only shows up under load. One production system I debugged spent hours oscillating at a doorframe—the potential field was strong enough to keep it away from the jambs but too weak to push it through the center. Wrong order.

Harmonic fields: smooth but heavy on memory

Harmonic functions solve the local-minima problem elegantly—no spurious attractors, ever. The field flows like water around obstacles. That sounds ideal until you hit a boundary with 10,000 grid cells and a 5 cm resolution. The memory footprint balloons; a 10×10 meter room with centimeter-level cells demands roughly 400 MB just for the Laplace solver buffers. On embedded hardware—think a Raspberry Pi or an older Jetson—that crushes your headroom for sensor processing. Teams who pick this method often discover the seam blows out during map updates: re-solve the harmonic field every time a forklift moves a pallet, and your frame rate drops to 0.2 Hz. The smoothness is real, but the speed cost is not linear—it's polynomial in the cell count. Is a perfectly smooth field worth a 4-second pause every time a door opens? For many production floors, the answer has been a quiet 'no' followed by a late-night rollback to potential fields.

Learning-based: flexible but needs training data

Neural approaches promise the best of both worlds—adaptable fields that respect boundaries without exploding memory—but here is the dirty secret: they eat training data like a teenager eats pizza. A single warehouse layout needs at least 500 trajectories covering partial splits, dynamic obstacles, and varying start positions. Most teams skip this: they train on a clean warehouse, then deploy near a loading dock with stacked pallets, and the agent drifts into the plastic curtain because the training set never showed a semi-transparent boundary. The flexibility comes with a data-hungry pitfall. One anecdote: we fixed a boundary-collapse issue by augmenting the training set with perturbed wall geometries, but that took two weeks of labeling effort and increased inference latency by 30 %. The real trade-off is speed—inference adds 8–12 ms per step on a low-power GPU—versus adaptability. If your environment stays static, learning-based adds cost you don't need.

'Smoothness, safety, and speed form an impossible triangle. Pick two, and protect the third with aggressive bounds-checking.'

— field engineer, after a third boundary-collapse incident in a hospital corridor

Honestly — most geographical posts skip this.

What usually breaks first is the assumption that one method covers all three axes. Potential fields give you speed and simplicity but fail safely only in convex spaces. Harmonic fields hand you smoothness and safety but burn memory and update cycles. Learning-based offers flexibility but demands a training pipeline most teams lack the patience to maintain. The ordering matters: start by deciding which axis you can't compromise, then accept the degradation on the other two. If your robot moves at 0.3 m/s in a warehouse with fixed racking, harmonic fields might work—just budget for a second compute module. If the environment shifts weekly, invest in data generation first, then model training. I have not seen a single implementation that satisfied all three without painful edge-case handling in the boundary solver. That hurts, but it's honest.

Implementing Your Choice Without Breaking Everything

Boundary Distance Thresholds: Not a ‘Set and Forget’ Number

The hardest lesson I learned shipping a field-navigation stack was this: a threshold that works at 2 m/s kills the robot at 0.3 m/s. You pick a distance — say 1.2 m from the physical edge — where the field begins blending into a safety corridor. That sounds clean. It isn’t. At low speeds the robot creeps into the transition zone, the field gradient flips sign, and the controller chatters. We fixed this by making the boundary threshold a function of current speed plus one braking distance. Lazy? Maybe. It stopped the oscillations on day one. The trade-off: a slow robot hugging a wall uses a wider safety margin than strictly necessary — you lose maybe 8 cm of usable space. That’s acceptable. What isn’t acceptable is a 30 Hz limit cycle at the edge of a loading dock.

Mixing Field Types Under a Supervisor — Hybrid Without the Hype

Pure potential fields collapse at concave corners. Pure harmonic fields are computationally expensive near moving obstacles. The pragmatic fix: run two fields in parallel and let a supervisor pick the output. Implementation detail — the supervisor watches the distance to the nearest boundary. Inside a safety radius (say 0.5 m) it trusts the harmonic field’s smooth gradient. Outside that radius it falls back to a fast, approximate potential field. The catch is the handover seam. If the supervisor toggles abruptly, the velocity command jumps by 40 % in one cycle. We added a 200 ms linear blend — linear, nothing fancy — and the jerk disappeared. Most teams skip this blend step. Then they blame the field method. Wrong order: the method was fine; the glue logic was brittle.

“We spent three weeks tuning the harmonic solver. The supervisor took one afternoon. The edge-effect bug vanished in that one afternoon.”

— Lead integrator, warehouse AMR pilot, 2023

One pitfall: the supervisor itself needs a timeout. If both fields return NaN (saturation in sensors, rare but real), the supervisor must hold the last valid command, not default to zero. Zero means stop. Stopping in a narrow corridor under load is a recovery problem you don't want.

Testing with Worst-Case Sensor Noise — Not Your Average Tuesday

The boundary collapse bugs that slip into production are never the clean-edge cases. They're the ones where a lidar beam punches through a glass pane and reports a 30 m gap where a wall exists. Your field sees infinite free space; the robot accelerates into glass. Hard stop. The fix: inject a synthetic noise pattern into your simulator — drop every tenth range reading, clamp two consecutive returns to zero, then shift one beam by 0.4 m laterally. If the field still navigates without oscillating or crossing the real boundary, you're safe. If it doesn’t, tune the field’s spatial low-pass filter until the false gap is ignored. I have watched teams skip this noise injection because their lab floor is clean. Then their first field trial breaks a bumper. Not a simulation artifact — a real crack. Test the noise, not the ideal.

What usually breaks first is the velocity ramping near a false boundary. The field sees a phantom edge closer than the real one, the supervisor pulls into a cautious crawl, and the robot stalls 3 m from the actual wall. The odd part — the stall looks like a correct safety behavior. You need a second metric: “time to cross a known gap.” If that metric doubles after noise injection, your field is too sensitive. Pull the spatial kernel radius up until the noise stops biting. That hurts efficiency, yes, but a stalled robot delivers nothing.

What Goes Wrong When You Pick the Wrong Method

Oscillation: The Symptom That Wastes Hours

I once watched a demo robot dance in front of a doorframe for twelve minutes. The vector field looked fine in simulation—smooth gradients, clean convergence. But at the boundary, where the real wall met the opening, the field swapped direction every half-second. The robot corrected, overcorrected, corrected again. That's pure oscillation: a deadlock between two opposing field forces, both valid inside their half of the cell, neither dominant enough to break the tie. The cause is almost always a discontinuous interpolation between boundary treatments—bilinear blending that forgets the edge case right at the wall corner.

The odd part is—most teams see oscillation first in logs, not in motion. Velocity commands flicker between positive and negative values every tick. Some engineers tune the P-gain down, which only masks the symptom. That hurts. The field itself is broken; no controller can fix a vector that points left one millisecond and right the next. Remove the discontinuity at the source, or the robot never settles.

Stuck in the Corner: When Safety Becomes a Trap

Corners are deceptively dangerous. A common fix for boundary collapse is to enlarge the repulsive force near walls—more safety margin, right? Wrong. Two orthogonal walls meet, each pushing inward, and the combined potential creates a local minimum exactly where you do not want the robot to stop. The robot enters the corner, the field says "stay here forever," and the planner can't escape. That's not a software bug—it's a topology failure. The gradient vanishes in the corner because both axes of repulsion cancel the robot's forward drive.

I pulled a bot out of a warehouse corner manually because the field designers doubled the wall weight. Their safety metric looked perfect on paper.

— Field engineer, personal correspondence, 2023

The trade-off is brutal: increase wall repulsion to avoid collisions, and you may weld the robot to every concave feature in the space. We fixed this by capping the repulsive force magnitude within a radius equal to the robot's turning diameter. Not elegant. But the robot escaped corners.

Gradient Explosions Near Walls: The Numerical Grenade

Some methods handle boundaries by clamping the field to zero within a threshold—hard cutoff. That introduces a discontinuity in the derivative. The gradient directly next to the cutoff becomes infinite or, in practice, arbitrarily large. The robot receives a velocity spike that flings it sideways into the wall it was trying to avoid. I have seen odometry logs with acceleration jumps that exceed the motor spec by four hundred percent. Exploding gradients don't always break hardware immediately—they eat bearings, strip gears, and corrupt state estimates. What usually breaks first is the localization filter: the spike injects a false velocity measurement, the EKF diverges, and now you have no idea where the robot is.

Field note: geographical plans crack at handoff.

A rhetorical question worth asking: would you rather smooth the field at the cost of a few centimeters of clearance, or replace drive motors every quarter? Most teams pick smoothness after the first gearbox fails.

The Hidden Consequence: Path-Length Explosion

Even if the robot doesn't oscillate, get stuck, or experience spikes, a poor boundary method can quietly inflate travel time. When the field decays too aggressively near edges, the robot treats every wall like lava—giving meters of clearance it doesn't need. That transforms narrow corridors into unwinnable slaloms. The planner takes arcs that add forty percent to the route. Speed drops because the robot keeps correcting away from phantom danger. Wrong method? You lose a day of productive runtime across a shift. That's not a theory; that's a deployment metric.

Frequently Asked Questions About Edge Effects

Can I just increase repulsion gain?

This is the first thing most teams try—turn up the repulsion strength until the robot bounces off the boundary like a pinball. And it sort of works. You get a buffer zone, a safety margin that feels comfortable. But here is what actually happens inside your vector field: you introduce a repulsion spike that dominates the gradient around the boundary, creating a steep wall of potential. The vehicle then oscillates—hard—because every time it approaches the edge, the repulsion yanks it back, then the attractive force pulls it forward again, then it hits the repulsive field again, and that cycle generates a weaving, unstable approach path. I have seen this produce what looks like drunk driving in simulation. The trade-off is stiff: you buy collision avoidance at the cost of smooth convergence. Worse, when you push gain past a certain threshold, the sum of forces actually reverses direction near the corner—pushing the robot into the boundary instead of away from it. That hurts. A moderate gain increase can help, but cranking it solves nothing.

Do harmonic fields always avoid local minima?

No. Harmonic fields eliminate most spurious minima because they solve Laplace’s equation—no curl, no divergence traps. But they produce a different kind of failure: the boundary saddle. At a concave corner of the workspace, the harmonic solution creates a stagnation point where the gradient vanishes. Not a minimum—a saddle. The robot slows down, drifts sideways, and sometimes sits there for several seconds before the gradient pushes it out. Most teams see that and think “local minimum,” but it's not—it's a slow-pass zone near the boundary. The fix is not to switch methods; it's to perturb the field slightly at the saddle or to apply a tiny tangential push when velocity drops below a threshold. Harmonic fields avoid true minima, but they don't give you a free ride at edges.

Is machine learning necessary for complex boundaries?

Not yet. The buzz around learned vector fields often skips over a practical reality: a simple weighted sum of repulsive potentials handles 80 percent of non-convex shapes—L-shaped rooms, corridors with alcoves, even narrow doorways. What breaks is the corner collision seam, where two boundary edges meet at an acute angle. A standard potential field creates a force that points directly into that corner, pulling the robot toward the intersection. Machine learning can fix that—yes—but so can a radial-basis field with an explicit obstacle-hull representation. The odd part is that most ML approaches need thousands of edge-case trajectories to generalize, while a hand-tuned harmonic field with boundary-adaptive repulsion works on the first run. I am not anti-ML here; I am saying start with the physics before you reach for a dataset. Most boundary collapses are geometry problems, not learning problems.

“I doubled the repulsion gain and the robot started spinning at the wall. We spent three days debugging before we realized the field was inverted at the corner.”

— Senior autonomy engineer, field robotics team

The catch is complexity. If your boundary is organic—a shoreline, a tree root, a crumbling wall—then a fixed analytic field fails because the gradient calculation depends on a closed-form distance function. In those situations, an interpolated field from sensor samples or a neural representation might be the only path. But ask yourself honestly: does your boundary change every run? Or is it a known polygon? If the latter, keep the analytical field and invest your time in debugging the saddle points and gain oscillations. That's where the real edge-effect errors live.

The Bottom Line: One Recommendation Without Hype

Start with potential fields, add harmonic blending if stuck

Most teams I have seen over-engineer this decision on day one. They read about harmonic functions or graph-based navigation and implement something complex before they have ever seen their field collapse in a real boundary test. Don't do that. Start with a standard potential field approach—repulsive forces from obstacles, attractive force toward the goal—and run it at the actual boundary geometries where your robot or simulation will operate. Nine times out of ten, the collapse happens at a concave corner or a narrow passage that your potential field simply can't resolve with linear repulsion alone. The fix is not to scrap the whole method. It's to add local harmonic blending only in those specific regions. Keep the rest simple. The odd part is—the simpler your base field, the easier it's to spot where blending actually helps versus where it adds unnecessary computation.

What usually breaks first is the assumption that one method works everywhere. A purely potential field handles open spaces beautifully, then fails at a dead-end corridor. A fully harmonic field avoids local minima but costs ten times the compute per step. The trade-off is real: you sacrifice raw speed for guaranteed continuity at edges. That sounds fine until your drone needs to dodge a wind gust and the harmonic solver is still converging. So test both extremes on your specific boundary geometry before committing to a hybrid. We fixed one vehicle's stuck-in-corner problem by applying harmonic blending only to vertices with interior angles below 90 degrees—the rest stayed potential. The improvement was immediate.

'We spent three months tuning gains for a method that couldn't handle the dock entrance. Swapped to potential-plus-blending in two days.'

— Integration lead, autonomous marine vehicles

Test boundary scenarios first, not the ideal path

I have watched teams run their first field test along a straight, obstacle-free corridor and declare victory. Then the field hits a boundary corner and the whole navigation collapses into oscillation or a dead stop. Test the edge cases before you benchmark smoothness. Create a deliberate map of concave corners, narrow necks, and T-junctions. Drive the field into each one and measure exactly where the gradient vanishes or the direction flips. Most teams skip this: they treat boundary collapse as a rare corner case, but for any real environment—warehouse aisles, forest trails, urban canyons—the edges are the operational space. If your field fails at the wall, it fails where your robot actually lives.

One concrete test: place your goal inside a C-shaped wall with the opening facing away from the start. A naive potential field will trap itself against the convex face and oscillate forever. A harmonic field will flow around the wall but may take three times longer to compute each step. Your job is to measure the failure mode, not guess it. Run that test with your chosen method, then ask: does the robot exit the trap within two seconds, or does it stall? The answer tells you more than any theoretical comparison.

Measure field continuity before tuning gains

The mistake I see most often is jumping into PID tuning or gain scheduling before verifying that the field itself is smooth across boundaries. You crank up the proportional gain to fix jitter, but the jitter was not a control problem—it was a discontinuity in the field gradient at the wall. Fix the field first, then tune. Measure the divergence of the field vector along the boundary edge. If the direction changes by more than 30 degrees across adjacent cells, no control loop in the world will smooth that out. You will burn hours on gain schedules that mask the real issue. The recommendation, without hype: use potential fields as your backbone, add harmonic blending only where the gradient breaks, and validate each boundary with a corner-case test before you ever touch a tuning knob. That approach won't guarantee zero collapse, but it guarantees you will see the failure in time to fix it with a small patch rather than a system rewrite.